Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

A morphological survey of particulate matter emissions from spark-ignited engines Lagally, Christie D. 2011

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2011_spring_lagally_christie.pdf [ 10.6MB ]
Metadata
JSON: 24-1.0071718.json
JSON-LD: 24-1.0071718-ld.json
RDF/XML (Pretty): 24-1.0071718-rdf.xml
RDF/JSON: 24-1.0071718-rdf.json
Turtle: 24-1.0071718-turtle.txt
N-Triples: 24-1.0071718-rdf-ntriples.txt
Original Record: 24-1.0071718-source.json
Full Text
24-1.0071718-fulltext.txt
Citation
24-1.0071718.ris

Full Text

A MORPHOLOGICAL SURVEY OF PARTICULATE MATTER EMISSIONS FROM SPARK-IGNITED ENGINES  by  Christie D. Lagally  BSME, University of California Santa Barbara, 2006 BA, Organizational Psychology, Sonoma State University, 1998  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE  in  THE FACULTY OF GRADUATE STUDIES (Mechanical Engineering)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2011 ©Christie D. Lagally, 2011  Abstract Spark-ignited engines are known to produce PM composed of solid, volatile or semi-volatile particles including spheres of carbon soot formed into agglomerates, other forms of carbonaceous particles, metal particles and charred droplets of engine oil. In this thesis, detailed observation has revealed that SI PM is partly composed of fully-formed carbon nanotubes and fullerenes in addition to known particle types previously presented in the literature.  The purpose of this work is to ascertain the shape and size of particulate matter being emitted by SI engines. In this thesis, PM thermophoretic sampling and transmission electron microscopy were used to collect and analyze engine soot samples, respectively. Furthermore, the operation of the thermophoretic sampling device used in engine PM sample collection was characterized to identify the sampling efficiency based on particle deposition and sampling biases based on differences in particle thermoconductivity for various forms of carbon such as turbostratic soot, crystalline carbon nanotubes and calcium. In general, the efficiency of the TPS method was roughly estimated to be 30-80% efficient based on experimental results.  In this thesis, carbon nanotubes and fullerenes have been identified as being emitted from in-use, spark-ignited natural gas and gasoline burning auto-rickshaw engines tested in New Delhi, India. Emission of fullerenes and CNTs was on the order of 10% +/- 7% of the non-volatile particulate matter. Agglomerates, dense spherical particles believed to be charred engine oil, and unidentified or compound particles were also cataloged.  ii  Confirmation that nanotubes are being produced by SI engines was achieved using PM samples collected from the Ricardo Hydra laboratory test engine at the University of British Columbia, Clean Energy Research Centre. Under more controlled conditions than can be achieved sampling in-use vehicles, SI engine PM is found to be a complex collection of dense, dark (possibly charred oil) spheres, small primary particle agglomerates, small particle deposits, volatile droplets, carbon nanotubes and fullerenes and large ‘other’ particles. High resolution TEM confirmed tube-shaped particles to be fully formed multi-walled carbon nanotubes.  iii  Preface Chapter 3 and Appendices G – J contain data and writing jointly produced by Prof. Steve Rogak, Prof. Milind Kandlikar, Dr. Conor Reynolds, Dr. Andy Grieshop and the author, Christie Lagally. Prof. Kandlikar and Dr. Reynolds conceived of the study of Indian Auto-rickshaws. Dr. Reynolds and Dr. Grieshop developed the particle sampling plan with guidance from Prof. Steve Rogak, with a particular focus on TEM sample collection. Dr. Reynolds and Dr. Grieshop collected all of the samples for electron microscopy. All transmission electron microscopy and energy dispersive x-ray data was completed by Christie Lagally. Furthermore, all image analysis, CNT statistical analysis, drive cycle analysis, TEM grid contamination research and analysis and literature review was completed out by Christie Lagally. All authors contributed heavily to the revisions of the Chapter 3 manuscript. Christie Lagally provided the majority of data and writing on the CNT results and literature review on the scarcity of CNTs in engine emissions streams.  iv  Table of contents Abstract ........................................................................................................................................... ii Preface............................................................................................................................................ iv Table of contents ............................................................................................................................. v Lists of tables ................................................................................................................................. ix List of figures .................................................................................................................................. x List of symbols and abbreviations ................................................................................................ xv Acknowledgements ..................................................................................................................... xvii Dedication .................................................................................................................................. xviii 1  Introduction ............................................................................................................................. 1 1.1  Particulate matter from spark-ignited engines ................................................................. 2  1.1.1  Solid particles............................................................................................................ 2  1.1.2  Potential for carbon nanotube growth in engines ..................................................... 3  1.1.3  Fullerenic soot vs. fullerenes .................................................................................... 3  1.1.4  Semi-volatile particles .............................................................................................. 7  1.2  Previous SI engine PM morphology research .................................................................. 7  1.2.1 1.3  Particle morphology categories................................................................................. 8  Measurement methods of engine exhaust aerosols .......................................................... 9  1.3.1  Online methods ......................................................................................................... 9  1.3.2  Off-line methods: filters, spectroscopy and microscopy ....................................... 10  1.4  Thermophoretic sampling of PM emissions................................................................... 13  1.4.1  Review of thermophoretic sampling applications................................................... 13  1.4.2  Thermophoresis on emission particles .................................................................... 13 v  1.5  2  1.5.1  Thesis objective ...................................................................................................... 16  1.5.2  Thesis project overview .......................................................................................... 17  Experimental methods, sampling and microscopy ............................................................... 19 2.1  Sampling particles using thermophoresis....................................................................... 19  2.1.1  Thermophoretic sampler description ...................................................................... 20  2.1.2  Heat transfer analysis in the TPS ............................................................................ 22  2.1.3  Thermophoretic velocity of various materials ........................................................ 25  2.1.4  TPS particle deposition efficiency .......................................................................... 28  2.2  3  Thesis objectives and structure ...................................................................................... 16  Microscopy methods for engine emissions analysis ...................................................... 33  2.2.1  Microscopy methods for particle imaging .............................................................. 33  2.2.2  Energy dispersive X-ray ......................................................................................... 41  2.2.3  Image and data processing ...................................................................................... 43  Carbon nanotubes and fullerenes in Indian auto-rickshaw emissions .................................. 44 3.1  Introduction .................................................................................................................... 44  3.2  Vehicles and testing methods ......................................................................................... 46  3.3  Results ............................................................................................................................ 52  3.3.1  Fuel-specific emissions ........................................................................................... 52  3.3.2  Identifying carbon nanotube/fullerenic particles .................................................... 54  3.4  Discussion ...................................................................................................................... 61  3.4.1  Possibility of sampling artifacts .............................................................................. 61  3.4.2  Estimates of fuel-specific CNT/FUL emissions ..................................................... 62  3.4.3  Are auto-rickshaws the only vehicles producing CNT/FUL?................................. 63  vi  3.5 4  Ricardo Hydra engine emissions .......................................................................................... 66 4.1  Motivation for laboratory engine sampling .................................................................... 66  4.2  Methods for sampling the Ricardo Hydra test engine .................................................... 68  4.2.1  Engine conditions and particle sample collection ................................................... 68  4.2.2  Sampling and data collection issues ....................................................................... 71  4.2.3  Microscopy of RH samples..................................................................................... 72  4.3  Morphology results ........................................................................................................ 73  4.3.1  Results from RH 105 (λ=1.3; high load) ................................................................ 73  4.3.2  Results from RH 108 (λ=1.6; high load) ................................................................ 77  4.3.3  Results from RH 208 (λ=1.6; high load) ................................................................ 80  4.3.4  Results from RH 213 (λ=1.3; low load).................................................................. 84  4.3.5  Apparent abundance of carbon nanotubes .............................................................. 87  4.4 5  Conclusion...................................................................................................................... 65  Conclusions from laboratory testing .............................................................................. 89  Conclusions ........................................................................................................................... 95  References ..................................................................................................................................... 97 Appendix A: Scaled drawing of the TPS ................................................................................... 105 Appendix C: Observations of grid degradation .......................................................................... 112 Appendix D: Fullerenes in compression ignition engine PM ..................................................... 113 Appendix E: Nu number calculations ......................................................................................... 114 Appendix F: Carbon grid contamination .................................................................................... 115 Appendix G: Emissions for full IARP vehicle set ...................................................................... 117 Appendix H: Summary of image database for IARP samples .................................................... 119  vii  Appendix I: Inter-vehicle variability .......................................................................................... 123 Appendix J: Volume of elemental carbon on grids .................................................................... 125 Fibers, fullerenes and nanotubes (CNT/FUL) ........................................................................ 125 Open and closed agglomerates................................................................................................ 125 Spherical and other particles ................................................................................................... 126 Appendix K: SMPS profiles for the Ricardo Hydra samples .................................................... 130 Appendix L: Supplementary electronic files .............................................................................. 132  viii  Lists of tables Table 1: Typical operational settings and device geometry......................................................... 21 Table 2: Explanation of thermophoretic velocity terms .............................................................. 26 Table 3: Iteration of air and tube temperature for variable air properties .................................... 29 Table 4: Theoretical results of TPS modeling ............................................................................. 29 Table 5: Fraction area efficiency for the TPS using Indian ink samples ...................................... 32 Table 6: Emissions for vehicles with TPS sampling included..................................................... 53 Table 7: Estimates of total grid square CNT/FUL counts for IARP Test 07 ............................... 62 Table 8: Engine operation settings for the Ricardo Hydra testing engine. SMPS profiles for these tests are shown in Appendix K. Electronic files containing gas data are listed in Appendix L. . 70 Table 9: Ricardo Hydra (RH) engine samples imaged under TEM............................................. 72 Table 10: Estimates of total grid square CNT/FUL counts for IARP Test 07 ............................ 116 Table 11: Estimate of total grid square CNT/FUL counts for IARP Test 22 ............................. 116 Table 12: Average fuel-based emission factors for all vehicles in each fuel-technology group. Four-stroke vehicles are disaggregated by age. Ranges shown are 95% confidence intervals. . 118 Table 13: Particle counts and shape category percentages for all morphologies for each vehicle sampled. ...................................................................................................................................... 121 Table 14: Binomial distribution results of the hypothesis that there is a single underlying CNT/FUL fraction (10%), and that the number observed on each grid is governed by the binomial distribution. .................................................................................................................. 124 Table 15: Volume totals for all particles imaged. ....................................................................... 127 Table 16: Summary of CNT emissions based on only the volume of CNT/FUL, agglomerates and spherical particles. ................................................................................................................ 129 ix  List of figures Figure 1: Turbostratic soot from a diesel test engine (courtesy of Arka Soewono). ..................... 5 Figure 2: Typical soot agglomerate of carbon spheres. This particle is from a compressed natural gas engine........................................................................................................................................ 5 Figure 3: Short, multi-walled carbon nanotube with characteristic hollow core. This particle came from a compressed natural gas engine................................................................................... 6 Figure 4: Comparison of SEM and TEM images of fibrous protrusions. a) an SEM image of a large particle with a small fiber emanating from the side; b) TEM of a particle with fibrous protrusions some of which show the characteristic hollow core of nanotubes. ............................ 12 Figure 5: Knudsen number graph for particles typically found from SI engines ........................ 15 Figure 6: Thermophoretic sampling device (TPS) schematic...................................................... 21 Figure 7: Schematic of the control volumes used to determine heat transfer in the TPS ............ 23 Figure 8: Thermophoretic velocity for various particle materials at 3 PSI through the TPS ...... 27 Figure 9: Flow chart for TPS efficiency analysis ........................................................................ 31 Figure 10: Predetermined TEM imaging locations ...................................................................... 34 Figure 11: Particle deposition at two locations on the Indian Ink E6 grid. On the left, the origin point at (0,0), as determined by the TEM navigation software, has more particle deposition in the lower left corner. On the right, the highest density of particle deposition, believed to be the jet stagnation point, is located at (292.2, 33.4). ................................................................................. 36  x  Figure 12: Locations on a sample TEM grid where the deposition of particles appears to originate from different directions. This common area of origin is identified as the stagnation point of the impinging jet. ............................................................................................................. 39 Figure 13: Location of the Indian Ink E6 TEM imaging location points. All the points are roughly along a path through the geometric origin, but location scatter is due to the TEM operator avoiding grids with no film or from scanning along the path from the opposite side of the grid square. .............................................................................................................................. 40 Figure 14: EDX energy spectrum from irradiation of the soot particles shown in the insert. Oxygen is most abundant in the sample due to the silicon monoxide grid film. As a result, it is difficult to decipher the contribution of carbon to the composition of these particles. ................ 42 Figure 15: The Indian drive cycle (IDC). IDC is used for regulatory emission testing of lightduty vehicles in India. TPS sampling occurred during the acceleration between 0 and 12 m/s in either the second or third sub-cycle, as indicated by the bold line. .............................................. 47 Figure 16: Thermophoretic Sampler. Resistance-heated tube is 70 mm long with 1.02 mm ID.. 49 Figure 17: Representative particle for each of the particle shape categories: a. CNT/FUL, b. open agglomerate, c. closed agglomerate, d. spherical, e. ‘other’. Scale bars are 100 nm in images a, b, c and e, and 500 nm for image d. .................................................................................................. 51 Figure 18: Carbon nanotubes and nanotube agglomerates from three different fuel-engine combinations: CNG/four-stroke (a and b); gasoline/four-stroke (c and d); CNG/two-stroke (e and f). All scale bars are 100 nm. ........................................................................................................ 56 Figure 19: Fullerenes and CNT types: fullerenic cones with tip angles of approximately 19 degrees from separate 2-stroke engines (a and b); string-like, long carbon nanotubes (c and d);  xi  jointed carbon nanotubes from CNG/two-stroke engines and gasoline/four-stroke engines respectively (e and f). Scale bars represent 100 nm...................................................................... 57 Figure 20: Examples of particles were categorized as CNT/FUL but without the expected features (such as a hollow core) of fullerenes or carbon nanotube. Scale bars are 500 nm. ........ 58 Figure 21: High resolution TEM images of fullerenic wall structures from a CNG/four-stroke engine PM. a, MWCNT and wall structure of approximately 22 layers; b, wide-diameter MWCNT; c, fullerene structure. ................................................................................................... 59 Figure 22: Size distribution of the imaged particles (N=2121) by shape category. Size corresponds to RMS length parameter from TEM image analysis. .............................................. 60 Figure 23: Schematic of the Ricardo Hydra sampling test set-up ............................................... 69 Figure 24: Particle categories for the RH 105 sample. a) open agglomerate; b) closed agglomerate; c) spherical or oil droplet particles; d) possible nanotube or fiber particles; e) VOC or semi VOC particles; and f) ‘other’ or otherwise unidentified particles. .................................. 74 Figure 25: Morphology and size distribution of RH105 engine sample (λ=1.3) ......................... 75 Figure 26: SMPS profile of the Test 105 particles and the Test 205 particles showing extreme variation in samples. ..................................................................................................................... 76 Figure 27: Typical particles found in the RH 108 sample. a,b) agglomerate particles; c) spherical or oil droplet particles; d) VOC or semi VOC particles plus some ‘elongated’ droplets; e, f) carbon nanotubes and fullerenes ........................................................................................... 78 Figure 28: SMPS profiles of Tests 108 and 208 .......................................................................... 79 Figure 29: Agglomerate and oil droplet samples from RH 208 sample ....................................... 81 Figure 30: Particles found in the RH 208 sample. a) possible nanotube ; b) semi VOC particles; c,d) compound particles. ............................................................................................................... 82  xii  Figure 31: Unidentified crystalline particles embedded in compound particles of the RH208 sample. .......................................................................................................................................... 83 Figure 32: Particles from RH 213 sample. a) agglomerate particles (lower and right sides); b) spherical or oil droplet particle; c) ‘other’ particle. Note that the RH 213 samples were temporarily labels RHC5 indicating the grid number, rather than the test number. ..................... 85 Figure 33: SMPS profile of Tests 113 and 213 which shows considerable variability between samples at the same conditions. .................................................................................................... 86 Figure 34: Expanse of nanotube particle deposition. (top images) HR TEM of nanotubes at 600K and 500K times respectively. (bottom image) TEM image of particles at 100K times magnification. ............................................................................................................................... 88 Figure 35: Bent carbon nanotubes which appears to have buckled under stress due to a hollow center. ............................................................................................................................................ 90 Figure 36: High resolution TEM images of the tube structures from in the RH 108 samples. Insert ‘a’ (below) shows a parallel wall structure with atomic spacing consistent with carbon graphene sheets. Insert ‘b’ (below) shows similar wall structures of a tube that is likely behind the larger tube in the center of the image above because wall structures would not show up in the center of a tube. Note that the graininess of these images that obscures a clear view of the parallel wall structures is due to the silicon monoxide film on the TEM grid.............................. 91 Figure 37: Larger carbon nanotube in the RH 108. Insert shows wall structure of tube. ........... 93 Figure 38: SolidWorks drawing of the TPS ............................................................................... 105 Figure 39: E6 particle count map ................................................................................................ 106 Figure 40: E6 particle deposition efficiency map ....................................................................... 107 Figure 41: E7 particle count map ............................................................................................... 108  xiii  Figure 42: E7 particle deposition efficiency ............................................................................... 109 Figure 43: E8 particle count map ................................................................................................ 110 Figure 44: E8 particle deposition efficiency ............................................................................... 111 Figure 45: Possible fullerene particles from a diesel engine (courtesy of Arka Soewono) ....... 113 Figure 46: Carbon nanotubes attached to other particles may be difficult to locate in heavily sooting engines such as diesel engines. Shown is a. long nanotube attached to an agglomerate of other particulate matter; scale bar is 500 nm, and b. a nanotube embedded inside another type of particulate matter (indicated by arrow); scale bar is 100 nm. ..................................................... 121 Figure 47: Two examples of wide-diameter, multi-walled CNT. a. Scale bar is 200 nm; b. Scale bar is 100 nm............................................................................................................................... 122 Figure 48: SMPS profiles for December 2010 Ricardo Hydra samples listed in Table 8. ........ 130 Figure 49: SMPS profiles for December 2010 Ricardo Hydra samples listed in Table 8. ......... 131  xiv  List of symbols and abbreviations CERC  Centre for Clean Energy Research  CNG  Compressed natural gas  CNT  Carbon nanotubes  CPC  Condensation particle counter  CVS  Constant volume sampler  EC  Elemental carbon  EDX  Energy dispersive x-ray  FUL  Fullerenes  HRTEM  High resolution transmission electron microscopy  IARP  Indian Auto-Rickshaw Project  IC  Integrated circuit  ICAT  International Centre for Automotive Technology  IDC  Indian Drive Cycle  MWCNT  Multi-walled carbon nanotubes  OM  Organic matter  PAH  Poly-aromatic hydrocarbons  PM  Particulate matter  RH  Ricardo hydra test engine  RMS  Root mean squared  SEM  Scanning electron microscope or microscopy  SI  Spark-ignited  xv  SMPS  Scanning mobility particles sizer  STEM  Scanning transmission electron microscopy  SVOC  Semi volatile organic compounds  TEM  Transmission electron microscope or microscopy  TEOM  Tapered element oscillation microbalance  TOT  Thermo-optical transmittance  TPS  Thermophoretic sampler  UFP  Ultrafine particles  VOC  Volatile organic compounds  xvi  Acknowledgements I would like to gratefully acknowledge my supervisor, Professor Steven Rogak and my MaSc. committee members Professor Milind Kandlikar and Professor Robert Evans for their support and guidance in this research, and the Michael Smith Laboratories for supporting my graduate research here at UBC. Furthermore, I’ll like to acknowledge Prof. Alireza Nojah and graduate student Parham Yaghoobi for their assistance in helping me to identify carbon nanotubes in the engine samples. Additionally I’d like to express my appreciation for my fellow researchers Conor Reynolds, Arka Soewono, Hugo Arri, Andy Greishop, Eric Kastanis, Bronson Patychuk and Arminta Chicka for their guidance and assistance during my short stay at UBC. Finally, this research could not have been attempted if it were not for the help of Bob Perry at CERC, Perry Yabuno in Mechanical Engineering and the staff of the UBC Bioimaging Lab – Garnet Martin, Derrick Horne and Brad Ross.  xvii  Dedication This thesis is dedicated whole-heartedly to my beloved husband, Eric Lagally, who has supported me, cared for me and been my dearest friend through all my career ambitions and beyond.  xviii  1 Introduction Particulate matter (PM) is one of many byproducts from internal combustion engines. Significant resources are committed to reducing particulate matter production due to the respiratory and cardiac health impacts associated with breathing these emissions (Englert 2004). The size distribution of particles as well as the total amount of exposure is shown to cause chronic and fatal health consequences (Oberdörster et al. 2005). Ultrafine particle (UFP), defined as particles with a characteristic diameter of less than 100 nm, are easily absorbed by the body causing inflammation and irritation (Oberdörster et al. 2005). Furthermore, studies of highaspect ratio particles such as asbestos or carbon nanotubes are similarly dangerous when deposited in the lungs (Donaldson et al. 2010), indicating that both the shape (morphology) as well as PM size is important to understanding the health implications of PM pollution.  Despite the enormous number of spark-ignited (SI) engines in operations in countries all over the world, the study of SI engine PM morphology has been limited. In part, this may be due to lack of concern for the significantly smaller total mass of PM emitted from SI engines in comparison to heavily-emitting diesel engines (Dallmann and Harley 2010). In addition, PM morphology is only determined using transmission or scanning electron microscopy, and this is both laborious and expensive. While PM emissions from SI engines are generally lower, the morphology of these particles is shown here to be more complex than just aggregates of soot spheres and agglomerates typically identified as PM from automotive engines.  1  1.1  Particulate matter from spark-ignited engines  Several types of particles are emitted from SI engines, but generally the PM is divided into solid particles and semi-volatile particles.  1.1.1 Solid particles Automotive PM includes solid particles composed of metal oxides (derived from lubricating oil and wear particles), sulphates and carbonaceous particles. The carbonaceous particles are generally accepted to be composed of molecules of short-range order crystalline carbon and/or polyaromatic hydrocarbons (PAH) collected into a turbostratic sphere (Richter and Howard 2000) to form a primary soot particle (Figure 1). (A turbostratic sphere is one in which short order molecules of carbon form stratified layers to form a sphere.) Studies of the nanostructure show that these primary particles are composed of either amorphous carbon molecules with minimal short-range crystalline structure (Palotás et al. 1996) or composed of partially ordered crystalline carbon often referred to as fullerenic soot (Grieco et al. 2000). Soot is defined as the solid particle byproduct of combustion and is essentially composed of carbon in spheres of graphene layers; it is also referred to as soot carbon and forms into the agglomerates typically identified in engine soot (Andreae and Gelencsér 2006). See Figure 2.  Ash is the general term used to describe solid waste particles of combustion (usually from coal (i.e. fly ash) and is typically composed of metals and metal oxides ranging in size from 10 µm to nanometers and have a variety of shapes (Singh and Kolay 2002; Ahmaruzzaman 2010).  2  Carbon nanotubes and fullerenes are forms of crystalline carbon consisting of graphene sheets forming a closed shell 3-D structure (Figure 3). Nanotubes can range in length from a few nanometers to several centimeters when grown under the right conditions (Ajayan 1999).  1.1.2 Potential for carbon nanotube growth in engines The growth of carbon nanotube and other fullerene structures occurs when a combustion flame is between 600-2000 C, a carbon source is present and a metal catalyst is readily available to nucleate the organized building of carbon atoms (Height et al. 2004).  Potentially, an  environment combining these conditions and materials could, theoretically, produce fullerenes. Open flame combustion sources burning fuels typically used in vehicles has been shown to produce nanotubes given the correct equivalence ratio and temperatures (Wen et al. 2008).  1.1.3 Fullerenic soot vs. fullerenes In 1997 researchers at Toyota found that diesel engine soot exhibited fullerene-like characteristics (Ishiguro et al. 1997). Greico, Howard et. al. later confirmed that longer-ranged curved shells were possible from flame generated soot, despite very short residence times for some particles (Grieco et al. 2000), and these particles were dubbed ‘fullerenic soot’. Richter et. al established that PM appeared in the form of fullerenic soot when crystalline wall structures only compose a part of the primary particle and the remaining carbon is amorphous (Grieco et al. 2000). Greico’s samples contained longer-range order carbon atoms in portions of primary particles, but the order did not extend throughout the particle and were not shown to contain fully closed fullerenes. In 2004, Su et. al showed that samples of diesel soot processed through  3  emission control systems had smaller primary particles, but found fullerenic-soot characteristics in the core of the primary particles (Su et al. 2004).  The difference between fullerenic soot and fully formed fullerenes was shown by Hebgen et. al in 2002. The degree of long-range order of crystalline carbon is determined by the curvature of the continuous walls in the primary particle of soot from a benzene flame (Goel et al. 2002). Particles with closed shells are considered to have a curvature factor of C=1. Partial, yet continuous wall structures have a curvature factor of between 0 and less than 1.  4  Figure 1: Turbostratic soot from a diesel test engine (courtesy of Arka Soewono).  Figure 2: Typical soot agglomerate of carbon spheres. This particle is from a compressed natural gas engine.  5  Figure 3: Short, multi-walled carbon nanotube with characteristic hollow core. This particle came from a compressed natural gas engine.  6  1.1.4 Semi-volatile particles Semi-volatile particles or semi-volatile organic compounds (SVOC) are those that may appear in morphology studies as solid, but will often evaporate over time off the TEM grid or under the radiation of an electron beam during microscopy. SVOCs include materials such as engine lubrication oil (Miller et al. 2007) and polyaromatic hydrocarbons (PAH) (Richter and Howard 2000).  While a range of fuels are used in SI engines including gasoline, natural gas and hydrogen, all vehicles require engine lubrication oil. As a result, engine oil is partially burned and contributes to the overall composition of engine PM (Miller et al. 2007), (Blom et al. 2000). Typically, engine oil particles are generally around 50-150 nm and appear as a round, dark circle on the TEM grid (Blom et al. 2000; Miller et al. 2007; Etissa et al. 2008).  PAH is the byproduct of fuel decomposition (amongst other sources including engine oil and unburned fuel) and is emitted from SI engines in gaseous form as well as attached to PM (Elghawi et al. 2010). PAH is shown to be a precursor to soot as well as to fullerenes and fullerenic particles (Richter and Howard 2000).  1.2  Previous SI engine PM morphology research  Several studies have confirmed that spark-ignited engines are a source of ultrafine particles (UFP) (Kittelson 1998). A large number of PM morphology studies have been performed to  7  determine the size and shape of PM from diesel engine sources (for example Lee et al. 2002; Neer and Koylu 2006; Soewono 2008; Soewono and Rogak 2009), but fewer than a dozen studies focus on the morphology of PM from SI engines (for example Mathis et al. 2004; Chakrabarty et al. 2006). Based on a rough estimate, the ratio of morphology research papers on diesel PM verses spark-ignited engine PM is approximately 10:1.  1.2.1 Particle morphology categories Chakrabarty et. al, investigated the fractal characteristics of SI PM in detail, and focused specifically on the agglomerates of soot primary particles and their fractal characteristics (Chakrabarty et al. 2006). Roughly 200 images were selected for analysis based on the clarity of the image required for the primary particle and morphology study. In contrast, Blom et. al. proposed distinct categories of SI engine particle emissions including incompletely burned (or charred) engine oil deposits, turbostratic carbon soot, crystalline carbon soot and extended agglomerates of primary particles (Blom et al. 2000). Similarly, Etissa et. al showed that 2-stroke scooters also emitted at least three categories of particles – volatiles, soot agglomerates and calcium-rich deposits also thought to be charred engine oil (Etissa et al. 2008). The significant contribution of engine oil to PM from diesel and SI engines was confirmed by Kleeman et. al (2008) and Miller et. al (2007). Ortner et. al found and reported fibrous carbon structures emitted from an Otto engine (Ortner et al. 1998). Similarly, crystalline carbon soot was reported by Evelyn et. al (2003) in diesel soot in the form of fully formed carbon nanotubes, but neither emission rates nor frequency were mentioned since samples were scarce.  8  While there are clearly careful studies done to determine to the morphology of PM from SI engines, this area of research is still being established to provide a benchmark of morphological categories for SI particle emissions.  1.3  Measurement methods of engine exhaust aerosols  The difficulty in determining the morphology of PM lies in the limited number and types of tools (either online or offline) that can determine the shape of a particle. Typical PM aerosol measurements are made via online tools to measure mobility diameter, particle number and mass using a stream of emissions through the equipment. Offline measurements include particle mass and chemical composition as well as morphology of particles viewed using scanning or transmission electron microscopy. A summary of particle analysis tools is included here.  1.3.1 Online methods Particle mobility diameter can be measured using Scanning Mobility Particle Sizer (SMPS) and paired with a Condensation Particle Counter (CPC), indicates the concentration of particles for a range of mobility diameters.  An aethalometer offers another method for particle concentration measurements of carbonaceous particles. A light sensitive sensor measures the decrease in light passed through a particle filter that is continuously impacted by particles from the emissions stream (Hansen et al. 1984). The change in light intensity indicates the rate of filter loading and hence the particle concentration. 9  A Tapered Element Oscillating Microbalance (TEOM) is used to measure particle mass concentration of a known size (for example PM2.5) via the collection of particles on a fixed oscillating beam which changes oscillation frequency as more particles load onto the filter (Ruppecht et al. 1992; Patashnick et al. 2002). While all these instruments provide vital data for aerosol research, none provide information on particle morphology.  1.3.2 Off-line methods: filters, spectroscopy and microscopy Particles loaded on the TEOM filter or collected by another means onto tissue quartz or spun quartz filters can be analyzed off-line by gravimetric measurements and chemical analysis. Raman spectroscopy can also be used to determined the short-range order of aerosol particles (Rosen and Novakov 1977; Sadezky et al. 2005)  In order to perform microscopy and determine the morphology of individual particles, the PM must be deposited onto either a scanning electron microscope (SEM) compatible substrate or TEM grid. Both SEM and TEM will provide information on the structure of the PM, however SEM images show the exterior of particles where as TEM can provide information on the internal structure of the material (Figure 4).  SEM images can be taken from a conductive substrate containing the particles, provided that scanning electrons have a continuous path to move away from the imaging location. Conversely, the TEM imaging requires that particles be placed on a TEM grids usually composed of a copper support grid and a film composed of either formvar, amorphous carbon, silicon monoxide or some layered combination of these substrates (Ted Pella,Inc. 2011). Identification of particles 10  such as multi-walled carbon nanotubes (MWCNT) is easier using TEM rather than SEM because the interior hollow core and multiple wall structure of these particles is only visible using TEM.  11  Figure 4: Comparison of SEM and TEM images of fibrous protrusions. a) an SEM image of a large particle with a small fiber emanating from the side; b) TEM of a particle with fibrous protrusions some of which show the characteristic hollow core of nanotubes.  12  1.4  Thermophoretic sampling of PM emissions  1.4.1 Review of thermophoretic sampling applications Careful characterization of a thermophoretic PM sampler is required to determine the overall emissions rates of specific particles and determine any sampling biases for or against particles of various sizes, shapes or materials found in the emissions stream. Thermophoretic samplers have been used in a variety of particle sampling experiments. Sampling PM in flame studies is frequently done using thermophoresis by simply inserting a cooled piece of metal into the flame for microseconds at various heights in the flame (Megaridis and Dobbins 1987),(Lee et al. 2008). Thermophoretic sampling of diesel soot was found to be effective for capturing particles for morphology investigations (Soewono and Rogak 2009).  1.4.2 Thermophoresis on emission particles Thermophoresis is the force on a suspended aerosol particles caused by a temperature gradient in the bulk fluid (Seinfeld and Pandis 1998). Hence, the thermophoretic velocity is the speed at which the particle moves parallel to the temperature gradient within the bulk fluid. It is based on a number of parameters including particle diameter and especially Knudsen number (Seinfeld and Pandis 1998).  The Knudsen number, Kn, is a ratio of the mean free path (λ) of the molecules of the surrounding bulk fluid to the characteristic length (d = diameter) of the aerosol particles within the fluid (equation 1).  13  Kn =  2λ d  [1]  In the free molecular regime, where Kn >> 1, aerosol particles have a much smaller diameter than that of the mean free path of the surrounding fluid and the particles will collide with the molecules in the fluid less frequently. Conversely, when the mean free path of the fluid is much smaller than the diameter of the aerosol particles, Kn << 1, the particles are considered to be in the continuum regime where the bulk fluid acts as a continuous median in which the aerosol particle moves and collisions with molecules in the fluid are constantly occurring. In the transition regime, where Kn ≈ 1 , particles in the fluid have roughly the same diameter as the mean free path of the fluid (Seinfeld and Pandis 1998).  For the SI engine aerosol particles sampled in this work using the UBC thermophoretic sampler (described in Chapter 2), the Kn ranges from roughly Kn = 11 in the free molecular regime to Kn = 0.0113 in the continuum regime (Figure 5) based on mobility diameter.  14  Figure 5: Knudsen number graph for particles typically found from SI engines  It is generally accepted that thermophoretic velocity is independent of particle size in the freemolecular regime but dependant on the primary particle size of aggregates for particles in below the transition regime (Zurita-Gotor 2006), (Suzuki et al. 2009).  Rogak et. al. (1993) showed  that the agglomerate mobility diameter (a typical measure of PM size) is determined by the projected area of the agglomerate in the free-molecular. As a result, the implication of this relationship is that particles of complex aggregate morphology would reach a thermophoretic velocity expected of a sphere of the same projected area (Rogak et al. 1993). 15  However, Suzuki et. al presents experimental results showing that morphology, as well as the size, has a direct impact on the thermophoretic velocity of a particle when the primary particle is in the continuum regime (Suzuki et al. 2009),(Zheng 2002).  Nevertheless, this phenomenon  does not apply to the majority of particles considered in this thesis which are in the freemolecular regime.  1.5  Thesis objectives and structure  1.5.1 Thesis objective The purpose of these studies is to characterize a PM thermophoretic sampling (TPS) device and to refine TEM methodologies for particle imaging and categorization; to apply and interpret the TPS/TEM methods to an in-use SI engine study (i.e. the Indian Auto-rickshaw Project (IARP)); and to confirm the validity of field study findings by using the TPS/TEM method to collect and analyze particles from a laboratory engine. Finally, the outcome of these studies was to ascertain the shape and size of particulate matter being emitted by SI engines and estimate the thermophoretic sampling efficiency and related issues that influence the ability to determine emission rates for the particle categories established here.  The title of this thesis, A Morphological Survey of Particulate Matter Emissions from SparkIgnited Engines, demonstrates the breadth of research covered here. As a survey of the PM emissions, all types of particles are considered possible contributors to the categories of particulate matter found in our environment. Furthermore, there are untapped implications for considering morphology as well as size in the study of PM. While a range of PM sizes has  16  already been shown to have various health effects, the influence of morphology is less understood. This is primarily due to the limited amount of research on the morphology of PM from SI engines. But early research into particles such as carbon nanotubes in an industrial environment shows how morphology of a particle can influence the way a particle interacts in the body and in the environment. Hence, a survey of the PM morphology of SI engines is a step forward in determining the health effects of SI PM emissions.  1.5.2 Thesis project overview Here two observational studies were performed to determine the morphology of SI engine PM from 1) in-use Indian auto-rickshaws in New Delhi (in conjunction with the broader Indian Autorickshaw Project (IARP)), and 2) the Ricardo Hydra test engine located in the Clean Energy Research Centre (CERC) at the University of British Columbia (UBC). Samples of PM were collected in Delhi by researchers Conor Reynolds and Andy Greishop at the Institute of Resources, Environment and Sustainability (IRES) and brought to UBC for further analysis. PM samples from the Ricardo Hydra were taken by the author at CERC.  Transmission electron  microscopy (TEM) was performed by the author on nearly all TEM grid collections shown here.  It is necessary to understand the chronology of these projects and the progression of research leading to the structure of this thesis. In 2009, the UBC thermophoretic sampler (TPS) was sent to Delhi for use in the Indian Auto-Rickshaw Project (IARP). Prior to this research, the TPS had been used for aerosol collection, but the particle deposition efficiency had not been characterized. When carbon nanotubes and fullerene particles were discovered in the PM emissions of the autorickshaw in 2010, the question of emissions rate or particle frequency was  17  important for establishing that the identification of these particles was not an artifact of sampling or some variation of contamination on the TEM grid.  Confirmation that these particles were actually being produced by SI engine in significant number above background noise was accomplished using two approaches. First, laboratory tests with simple, model aerosols were performed to confirm sampling efficiency and understand biases inherent in the TPS. Secondly, the TPS/TEM method was applied to a natural gas SI engine at UBC to determine whether the results from the IARP study might be reproduced. Therefore, the thesis is structured as follows. Chapter 2 is an overview of the TPS device and operation, the TPS the sampling efficiency and issues and the microscopy methods used for imaging particles in the TEM. Subsequently, the results of the IARP study were documented in a multi-author paper (currently under review) which is reprinted here in Chapter 3. Chapter 4 is an overview of the morphology of particles found from laboratory SI engines and confirms that carbon nanotubes and fullerenes are unintentionally being produced by SI engines. Finally, a conclusion of this body of work is presented in Chapter 5.  18  2 2.1  Experimental methods, sampling and microscopy Sampling particles using thermophoresis  Thermophoresis is the force on an aerosol particle due to a temperature gradient in the surrounding gas. Higher energy air molecules in the hotter gas area collide with the aerosol particles with greater force than the molecules in the cooler gas. As a result, there is a net force on the particles propelling it from an area of warmer air to cooler air. This condition can be used to deposit particles onto a cool TEM grid from a warmed air flow.  Here the UBC thermophoretic sampler (TPS) was used for sampling particulate matter from the Indian auto-rickshaws and the UBC CERC Ricardo Hydra test engine. Sampling efficiency – defined as the number of particles actually deposited on the grid verses the total number of particles that move through the TPS during sampling (i.e. that could have deposited on the grid) – is crucial to understanding the influence of the thermophoretic technique on the collected particle data including total number of particles in the sample stream (i.e. concentration), types of particles (i.e. frequency of specific particles in the population) and any bias that the thermophoretic method my cause due to variations in particle material composition or particle size. In the following sections, the UBC TPS is described and its operation is characterized for three particulate matter materials (amorphous carbon PM, engine oil (indicated by calcium), carbon nanotubes and fullerenes) and a range of sizes and the deposition efficiency is evaluated.  19  2.1.1 Thermophoretic sampler description The TPS consists of a simple heated flow tube impinging an air jet onto a room-temperature stage containing a TEM grid (Figure 6). A diluted emissions stream is drawn through the top of the TPS and then through a 1.02 mm ID steel tube. The tube is in contact with the copper cap on top of the TPS which serves at the positive electrode and a steel plate at the bottom of the tube serving as the negative electrode. The steel tube is resistance heated using 1-3 W of electrical power. Air exiting the tube impinges onto the room-temperature TEM grid stage. The temperature gradient between the heated air and the cool TEM stage allows a thermophoretic force to more particles in the air stream towards the stage for deposition onto the TEM grid. Pressure taps (not shown) are connected to the entrance and exit air flow to measure the pressure drop through the TPS device.  20  Figure 6: Thermophoretic sampling device (TPS) schematic  To characterize the operation of the TPS, the following operational settings are used as constants. Geometric variables and material constants are also listed below. Table 1: Typical operational settings and device geometry Current [amps] 1.0 Voltage [Volts] 1.0 Entrance Temp. [C] 20 Electrical Resistivity of steel [mohm-mm] 0.718 Tube ID [mm] 0.508 Tube OD [ mm] 0.690 Tube Length [mm] ~70.0 Distance from jet exit to impinged plate [mm] 1.60  21  2.1.2 Heat transfer analysis in the TPS Heat is generated in the TPS tube via electrical resistance. The electrical resistance in the length ( Ltube ) of the tube is determined by the equation Rtube =  ρ ele * Ltube Atube  [ohms] where the area of the  tube ( Atube ) is simply the tube cross-section of the steel material. Hence the electrical heat produced in this volume of steel is E& gen = I 2 Rtube [W] .  Using two independent energy-balances across two control volumes (Figure 7), the following derivation is used to determine tube surface temperature and tube exit air temperature. Note that the TPS Teflon™ base has two different diameters along its length. The larger, top portion is referred to as ‘section 1’ and the bottom, smaller part is ‘section 2’. In practice (i.e. in the MATLAB code) the heat transfer equations are used for sections 1 and 2 separately, but for simplicity the derivation is shown just once. The air temperature for section 2 is simply the output air temperature for section 1.  22  CONTROL VOLUME 1 z r  CV1 is from the interior surface of the tube to the exterior surface of the Teflon cylinder.  CONTROL VOLUME 2 (tube interior)  Steel tube Air gap Teflon case rteflon  rteflon −outer  Thermal resistance circuit for CV1:  Ttube  Rair  Rteflon  Tteflon _ outer  Figure 7: Schematic of the control volumes used to determine heat transfer in the TPS  Rair =  ln(rtube / rteflon )  Rteflon =  2πkair Ltube ln(rteflon / rteflon −outer ) 2πkteflon Ltube  [2]  [3]  In control volume 1, the heat generated by the electrical resistance in the tube exits the system via conduction through the Teflon base and via convection from the tube air flow. Heat generation in the tube is modeled as E& gen = I 2 Rtube and the energy leaving the control volume by conduction through the tube and Teflon is modeled as qcond =  Ttube − T∞ and by convection as Rair + Rteflon  23  qconv = hAs (Ttube − Tb ) where Tb is defined as Tb =  (Tin + Tout ) . The conductive heat transfer 2  resistance only includes the conduction through the air gap and the Telfon™ wall in the radial direction, and the conductive resistances are modeled by equations 2 and 3. Any heat transfer from the Teflon™ outer wall to the room air is considered to be negligible. Hence the radial resistance to heat conduction is U =  1 , and the overall energy equation for CV 1 is: Rair + Rteflon  I 2 Rtube = hAs (Ttube − Tb ) + U (Ttube − T∞ ) .  For control volume 2, the energy balance from the inlet and outlet air temperature difference and the convective heat transfer from the interior surface of the tube is m& c p (Tout − Tin ) = hAs (Ttube − Tb ) .  For both control volumes, Ttube and Tout are unknown, and the following matrix is solved for each section of the TPS.  hAs  m& c p + 2  − hA s  2      Ah   − hAs  T    m& c p − Tin  tube 2     * T  =  hA s out    & U + hAs  E gen + UT∞ + Tin   2    [ 4]  The internal convection coefficient, h , for the flow of air on inside the steel tube can be determined under one of two conditions – uniform heat flux or uniform temperature (Incropera and DeWitt 2002). For a steady state analysis, the tube was assumed to have uniform heat flux due to constant electrical resistivity and constant power input in the tube causing heat generation.  24  Hence the Nu=4.36 value used was for these calculations (Incropera and DeWitt 2002). The convection coefficient is determined by h =  N D * ka . Dh  2.1.3 Thermophoretic velocity of various materials The TPS deposits PM on to a TEM grid via thermophoresis, and based on a variety of parameters, a predictable thermophoretic velocity is achieved. The thermophoretic velocity is the speed achieved by particles pushed toward the cool TEM grid. A brief mathematical description of the thermophoretic velocity of a particle in the non-continuum, transition and continuum regimes is shown in equation 5 (Seinfeld and Pandis 1998).  vth =  3Cc µ air (k air + Ct k p K n ) * ∇T  [5]  2 ρ airT (1 + 3cm K n )(k p + 2k a + 2c1k p K n  The Cunningham correction factor is Cc = 1 + 2 K n (1.257 + 0.4e  ( −1.1  Kn  )  ) and is used to correct drag  calculations of a fluid on a particle in non-continuum or free molecular regime conditions (Seinfeld and Pandis 1998). All the remaining variables and constants are described in Table 2.  25  Table 2: Explanation of thermophoretic velocity terms Variable or constant Value Units µm Particle diameter range D = 0.01 to 1 µm  Reference  p  Boltzman constant Knudsen number  k B = 1.3806503E-23 2λ fmp Kn = Dp  [m2 kg /( s2 K)]  Cc =1.2908  Note that his is for 1 um. The value changes with particle diameter (Seinfeld and Pandis 1998)  Ct =2.2 C m =1.146  (Tsai et al 1997), (Brock 1962) (Tsai et al 1997)  C m =1.0  (Brock 1962)  mean free path  λ fmp =0.0651 x 10-6  [m]  Evaluated for STP of air (Seinfeld and Pandis 1998)  distance of thermal gradient  δ = 5.7316e-005  [m]  Based on the thermal boundary layer of the  Tinlet = T∞ = Tgrid gradient of temperature Material properties used  ∇T =  (T grid − Toutlet )  [K/m]  δ  µ air = 2.1025e-005 ρ air =0.9978 υ air =21.02484E-6; k air = 0.02996;  [kg/m*s]  Based on roughly 80.5 C  [kg/m^3]  Based on roughly 80.5C  [m2/s]  Kinematic viscosity of air  [W/m K]  Per iteration. Table below.  The thermophoretic velocity is dependent on the size of the particle, the temperature difference and the boundary layer thickness, δ . The value of δ is determined by δ =  h jet =  ka where h jet  Nu ave k a where Dtube is the inner diameter of the tube and k a is the thermoconductivity of Dtube  air at the heated tube temperature. See Appendix E for more information on the average Nu  26  calculation. The thermophoretic velocity is also dependent on the thermoconductivity of the PM material consisting of turbostratic soot, crystalline carbon nanotubes or calcium. However, Figure 8 shows that even with these differences in thermoconductivity, the thermophoretic velocity of the particles differs very little for the range of particles sizes (10-800 nm) relevant to SI engines.  Figure 8: Thermophoretic velocity for various particle materials at 3 PSI through the TPS  27  2.1.4 TPS particle deposition efficiency  2.1.4.1 Previous work to determined TPS efficiency by number count Early attempts by graduate student Scott Brown to determine the TPS efficiency resulted in the number of particles being under counted on the grid. Brown’s estimate of TPS deposition efficiency was based on the number of particles deposited on the TEM grid compared to the number of particles that could have be deposited if all particles through the TPS landed on the grid (Brown n.d.).  Here, two alternative approaches to determine particle deposition efficiency  are discussed – 1) efficiency, eTPS, estimated thermophoretic velocity by the particles through the TPS and 2) efficiency based on the fractional area of particles covering the TPS found during microscopy, ηTPS . Each of these methods is discussed in the following sections.  2.1.4.2 TPS efficiency predicted by thermophoretic velocity The TPS efficiency can be defined in Eqn. 6.  eTPS =  Agrid * vth  [6]  Q  The efficiency is based on the thermophoretic velocity (defined in Section 2.1.3) using the heat transfer calculations for the TPS device. Since the thermophoretic velocity is based on the exit air temperature of flow through the TPS, this value is crucial for determining the deposition flow rate. As a result, using the standard TPS values described in Table 1, the TPS exit air temperature was determined by iteration of the air material properties. That iteration is shown in Table 3 and convergence is determined by Tube Temp. and Exit Air Temp columns. However, in addition to being temperature dependant, the thermophoretic velocity depends on particle 28  diameter as shown in Figure 8 for three different aerosol materials. Note that the materials generally have the same thermophoretic velocity, and only diverge when particle diameters are greater than one micron. To determine the efficiency, the mean value of the thermophoretic velocity and the results of this TPS modeling are shown in Table 4. Based on these variables and a 3 [mm] TEM grid, the TPS deposition efficiency is just 1.3% for the conditions listed in Table 4 at ~3 PSI. By comparison, Brown’s estimate for thermophoretic efficiency is 1.37%  (Brown n.d.). Table 3: Iteration of air and tube temperature for variable air properties Iteration Cp ρ [kg/m^3] k [W/m K] υ [m2/s] air  air  1  --  2 3  --  Tube Temp. [K] 355.7  Exit Air Temp. [K] 354.4  air  0.0271  1.57E-05  --  0.0299  2.09E-05  --  353.1  350.7  1  0.02969  2.06E-05  1.009  358.7  354.3  4  0.9973  0.029975  2.10E-04  1.009  358.5  354.0  5  0.99784  0.02996  2.10E-04  1.009  358.5  354.0  Table 4: Theoretical results of TPS modeling Pressure Exit Est. Tube Re Drop Temp. Temperature Flow [psi] [C] Rate [C] [m3/s] ~3.0 0.5e-5 80.8 85.4 596 ~1.0 0.3e-5 90.7 101.1 397  Ave. Nu for an impinging jet  Boundary layer thickness [mm]  Temperature gradient [K/µm]  Ave. vth [µm/s]  8.8 9,3  0.015 0.055  -1.06 -1.29  9,700 8,900  29  2.1.4.3  TPS efficiency by fractional area  The experimental particle deposition efficiency (ηTPS ) is defined as the percentage of the projected area of particles captured by the TPS and deposited onto the TEM grid verses the percentage of area that would have been covered if all particles in the TPS were deposited onto the TEM grid. Mathematically this is described in equation 7.  η TPS =  % AreaTPS % Areatheoretical  [7]  Where % AreaTPS is the projected area of particles found on the TEM grid verses the number of particles known to move through the TPS during sampling that could have potentially landed on the grid ( % Areatheoretical ). % Areatheoretical is determined via the flow rate and sample time through the TPS and the size distribution of particles determined by the Scanning Mobility Particle Sizer (SMPS). The theoretical total area of the particles was assumed to be simply the projected circular area based on the range of mobility diameters found using the SMPS. A flow chart of this process is shown below.  30  Figure 9: Flow chart for TPS efficiency analysis  2.1.4.4  Methods for determining TPS efficiency by fractional area  For the purpose of determining the TPS efficiency, dried and aerosolized Indian ink was used as a model soot particle and was aerosolized into a nitrogen stream. Simultaneous SMPS/CPC and TPS/TEM samples were taken from this particle stream. Those samples are referred to here as Indian Ink E6, E7 and E8.  2.1.4.5  Fractional area efficiency results  For the three Indian ink tests, the fraction of area covered by particles changes with radial distance from the stagnation point. All three tests show that the fraction of area covered by particles near the user-defined stagnation point is higher and tends to decrease radially outward.  31  As defined above, the efficiency of the particle deposition is based on the number of particles that could potentially be deposited on the grids based on the volume of air through the TPS. For samples E6 and E7, the theoretical fraction area is between 50 and 60%. However for E8, the entire grid would be covered, and particles would be overlapping. This is due to sampling time of 105 second verses 45 and 60 seconds for E6 and E7 respectively. The efficiency of the TPS based on fractional area of coverage is shown in Table 5.  Table 5: Fraction area efficiency for the TPS using Indian ink samples  Grid ID E6 E7 E8  % AreaTPS 0.40 0.35 0.42  % Areatheoretical 0.50 0.65 1.12  ηTPS 0.80 0.54 0.38  2.1.4.6 Comparison efficiency results There is a large discrepancy between the TPS deposition efficiencies determined by thermophoretic velocity verses fractional area of deposited particles. However, the efficiencies and the various parameters that contribute to them should be taken as simply rough estimates with large uncertainty due to the influence of the practically unknown flow rate that had to be used in these calculations. The flow rate through the TPS is difficult to measure experimentally due to the large number of valves and connections that must be independently set that influence the flow rate and pressure measurements. Without careful, parameter controlled testing, the TPS flow rate measurements are reported to be a wide range of values. Similarly, in order to estimate the flow rate mathematically, large numbers of parameters must be assumed including laminar vs. turbulent flow, temperature, and head losses due to exceptionally non-standard connections.  32  2.2  Microscopy methods for engine emissions analysis  Transmission electron microscopy (TEM) and energy dispersive x-ray (EDX) techniques were used to image and analyze samples collected onto TEM grids for the engine samples discussed in this thesis. TEM was used to determine the size, shape and frequency of particles deposited onto the grid. EDX was used to determine the material composition of certain types of particles. Each of these methods is described in the following subsections. A brief summary post image processing and data analysis is also included in this section.  2.2.1 Microscopy methods for particle imaging TEM is used throughout this work to determine the morphology of PM, find the emission rates of certain particle types and evaluate the efficiency of the particle sampling methods. Both standard and high resolution TEM was performed at the UBC Bioimaging Lab on a Hitachi H7600 TEM and a FEI Tecnai G2 HR TEM respectively. Since imaging the full area of a TEM grid is nearly impossible at high magnifications of 80,000x – 300,000x, which is required to resolve nanoparticles, images were taken at a subset of locations on the grid that were selected by either predetermined location imaging or stagnation point centered imaging. The IARP PM samples (described in Chapter 3) were imaged using predetermined location imaging. The Ricardo Hydra PM samples (described in Chapter 4) and the model particles used to determine the efficiency of the TPS sampling methods (described in Section 2.1) were imaged using the stagnation point centered method. Each method is described below.  33  2.2.1.1  Predetermined location imaging  The predetermined location imaging is a survey method which focuses on nine, predetermined evenly spaced points on the TEM grid. Images are taken along the diagonal path of one grid square at each of those nine points. The point locations are shown in Figure 10. Predetermined Microscopy Sampling Locations 1000  800  600 Pt. 1  Pt. 2  Pt. 3  Pt. 8  Pt. 9, (0,0)  Pt. 4  Pt. 7  Pt. 6  Pt. 5  400  200  0  -200  -400  -600  -800  -1000 -1000  -800  -600  -400  -200  0  200  400  600  800  1000  Figure 10: Predetermined TEM imaging locations  Based on the theoretical operation of the TPS of a jet impinging onto a flat surface, the microscopy method using predetermined points assumes that the jet impingement is centered precisely at (0,0) or Pt. 9. In practice, this is rarely the case. TEM grids can shift in any lateral direction when they are placed in the TPS or in the TEM grid stage making the actual location of the grid center unknown. As a result, particle imaging on the grid was limited to small points  34  that were unrelated to the actual stagnation point of the air stream on the grid during sample collection.  Imaging at predetermined points 1-9 has the potential to cause image sampling aliasing if the distribution of the particles across the entire grid is not uniform. As a result, the majority of particles could be missed or the frequency of particle distribution misrepresented in the event that the stagnation point of the air stream in the TPS did not correspond to the origin point. Figure 11 shows two images of TEM grids where the particle deposition at the stagnation point is off center from the origin determined by the TEM navigation software.  35  Figure 11: Particle deposition at two locations on the Indian Ink E6 grid. On the left, the origin point at (0,0), as determined by the TEM navigation software, has more particle deposition in the lower left corner. On the right, the highest density of particle deposition, believed to be the jet stagnation point, is located at (292.2, 33.4).  36  2.2.1.2  Stagnation point centered imaging  An alternative method for imaging the TEM grids is based on the air jet stagnation point as the origin of image sampling. Since the impinging jet may not necessarily deposit particles evenly on the grid, there will be locations on the grid with a higher concentration of particles presumably near the stagnation point of the impinging jet. This may be a result of the higher thermophoretic force on particles in the stream of air at the stagnation point. Hence, the stagnation point centered image entails finding that stagnation point (i.e. the point of highest general particle concentration on the grid) and using this point to begin imaging across the grid. Details of the method are given below.  The stagnation point center imaging is performed as follows: 1) At high magnification (~10 micron view) the grid is imaged at (0,0). 2) 10 to 15 additional wide view images are recorded at (+/-300,0), (0,+/-300), (+/-500,0), (0,+/-500) and intermediate points inside quadrants I-IV. 3) The location of highest particle deposition or where the particle deposition seems to change direction (see Figure 12), is identified at the stagnation point. 4) In the wide view, stagnation point is centered. 5) Zooming in on the stagnation point, the nearest grid square is imaged along its diagonal. Approximately 10 images are taken. The point is established as “Pt. 1.” 6) To determine the location of more sampling points, a path along the grid is uniquely determined by the location of the stagnation point and the TEM grid stage origin. While  37  this origin point is arbitrary, it provides for a relatively straight path across the grid. See Figure 13.  38  Figure 12: Locations on a sample TEM grid where the deposition of particles appears to originate from different directions. This common area of origin is identified as the stagnation point of the impinging jet.  39  Stagnation Point-based Sampling Locations 1000  800  600  400  200  Stagnation Pt  0  -200  -400  -600  -800  -1000 -1000  -800  -600  -400  -200  0  200  400  600  800  1000  Figure 13: Location of the Indian Ink E6 TEM imaging location points. All the points are roughly along a path through the geometric origin, but location scatter is due to the TEM operator avoiding grids with no film or from scanning along the path from the opposite side of the grid square.  40  2.2.2 Energy dispersive X-ray Energy dispersive X-ray spectrometry (EDX) is a technique by which a material is irradiated by an electron beam in an electron microscope and the x-rays emitted from the material following irradiation are used to determine the elemental material composition (Kanda 1991).  Here, EDX was used to determine the composition of particles in the IARP samples (Chapter 3). A Hitachi H-800 TEM (STEM) equipped with an EDX located at the UBC Material Science Microscopy lab was used to look at several types of particles including agglomerates, spherical particles and nanotubes.  EDX was also attempted on particles collected from the Ricardo Hydra (Chapter 4). However, these particles were collected onto silicon monoxide/formvar TEM grid substrate. As a result, most EDX readings of particle on this substrate displayed high peaks of oxygen between 0-1 keV while the materials of interest, such as carbon and various other metals were also in this general energy level band. Subsequently, it was impossible to identify a particle as carbon when the signal for oxygen blocked out this energy band (Figure 14).  41  Figure 14: EDX energy spectrum from irradiation of the soot particles shown in the insert. Oxygen is most abundant in the sample due to the silicon monoxide grid film. As a result, it is difficult to decipher the contribution of carbon to the composition of these particles.  42  2.2.3 Image and data processing Image processing and analysis for all sets of particle images included in this thesis consisted of measuring the number, size, shape and location of particles found in every image obtained. Recording this information for every particle in every image was crucial for determining frequency of unusual particles. Image and particle data was collected and organized for later analysis either in spreadsheets or databases.  43  3 Carbon nanotubes and fullerenes in Indian auto-rickshaw emissions 3.1  Introduction  Particulate matter (PM) emitted by motor vehicles represents an established health risk, but the chemical or physical features of PM responsible for toxicity are largely unknown (Englert 2004). It is widely believed that motor vehicle PM is composed almost exclusively of solid soot, traces of ash and metals, sulphates and semi-volatile organics (Kittelson 1998). There is relatively little research on PM from spark-ignited (SI) engines because SI engines emit far less PM than diesel engines (Chakrabarty et al. 2006; Dallmann and Harley 2010). However, SI engines, especially poorly maintained ones, can produce substantial PM emissions (Mazzoleni et al. 2004) and may contribute disproportionately to urban PM exposures (Wu et al. 2009). Here, we show that SI engines produce particle types only sparsely reported in the engine emissions literature, including capped carbon nanotubes and closed-shell fullerenes.  Carbon nanotubes (CNT) and fullerenes (FUL) were discovered approximately 20 years ago (Kroto et al. 1985; Iijima 1991) and are of great current interest because of their useful mechanical, thermal, electrical and chemical characteristics (Baughman et al. 2002). However, concern has been raised about the impacts of potential occupational and environmental exposure to CNTs (Lam et al. 2006). A number of in vitro and in vivo toxicological studies show that CNTs longer than 10-15 microns may cause lung damage analogous to that caused by asbestos fibers (Donaldson et al. 2010). Toxicological studies have shown that, like soot nanoparticles, inhaled submicron CNTs are transported throughout the lungs and can have toxic effects in 44  animals (Muller et al. 2005; Oberdörster et al. 2005). CNT/FUL (long or short) have been detected in ambient air (Murr and Garza 2009), but their abundance has not been quantified.  Intentional synthesis of CNTs using laboratory flames has been demonstrated, especially with the aid of metal catalyst particles (Hafner et al. 1998; Jander and Wagner 2006; Wen et al. 2008). Emission factors for CNT/FULs from common combustion sources have not been published to our knowledge, though these morphologies have been identified in exhaust from natural gas stoves (Murr et al. 2004) and tentatively from a diesel engine (Evelyn et al. 2003) and gasoline engines (Ortner et al. 1998; Blom et al. 2000).  PM samples for this study are from a subset of vehicles tested as part of the Indian Autorickshaw Project (IARP) conducted by the University of British Columbia. IARP measured emissions from compressed natural gas-(CNG) and gasoline-fueled auto-rickshaws. Autorickshaws are three-wheeled taxis with single-cylinder spark-ignited engines and are a common type of motor vehicle in India and other parts of Asia, as well as in Africa and Latin America. In Delhi, these vehicles are exclusively powered by CNG after the Indian Supreme Court ruled that all public transportation vehicles (buses, taxis and auto-rickshaws) must switch to CNG. Most auto-rickshaws use spark-ignited engines and many are also capable of running on gasoline, which offered an opportunity to test them in both fueling modes. IARP was designed to assess fuel switching and technology policy options and is described elsewhere (Reynolds, Grieshop, et al. 2011; Reynolds, Kandlikar, et al. 2011). Here, we focus on the results from sampling particulate matter and characterizing non-volatile particle morphology.  45  3.2  Vehicles and testing methods  Emission tests were conducted on 30 CNG-fueled three-wheeled auto-rickshaws with two- or four-stroke spark-ignited engines with engine displacements of 145 and 175 cm3, respectively (Reynolds, Grieshop, et al. 2011). A subset of the four-stroke vehicles were equipped with backup gasoline fuel systems and were also tested operating on gasoline. Vehicles were recruited from the in-use fleet in Delhi and tested on a chassis dynamometer at the International Centre for Automotive Technology (ICAT) in Haryana, India (www.icat.in). Full tests on the Indian Drive Cycle (IDC) allowed measurement of emission factors for gaseous pollutants (CO2, CH4, NOX, THC, and CO) and fine particulate matter (PM2.5 and organic and elemental carbon components). Full-flow dilution of vehicle exhaust via a Constant Volume Sampler (CVS) resulted in testaverage dilution ratios between 15 and 40 and sample collection at ambient temperatures (23±2 °C). The IDC, used for regulatory testing of Indian vehicles, consists of six 108-second subcycles and runs the vehicle through a range of realistic operating conditions (Figure 15). Further details on sampling methods and vehicle recruitment are published in related papers (Reynolds, Grieshop, et al. 2011; Reynolds, Kandlikar, et al. 2011).  46  Figure 15: The Indian drive cycle (IDC). IDC is used for regulatory emission testing of light-duty vehicles in India. TPS sampling occurred during the acceleration between 0 and 12 m/s in either the second or third sub-cycle, as indicated by the bold line.  Thermophoretic Sampling (TPS) was used to collect particles on Transmission Electron Microscope (TEM) grids from a slipstream of the CVS dilution tunnel. TPS samples were collected over a sampling period of between 10 and 60 seconds during an acceleration portion of the IDC. Due to the unknown delay between engine-out emissions and TPS sampling, it is impossible to establish the precise engine operating condition (i.e., engine RPM, load) corresponding to the TPS samples. Also, acceleration occurs at slightly different times depending on the driver’s ability to follow the ideal IDC test parameters and driver’s variation in gear shifting time. However, the acceleration time period was long enough (greater than 20 seconds) to ensure that TEM grid loading occurred during acceleration of the vehicle.  47  The TPS draws 0.2 to 0.3 LPM of sample flow through a 70 mm long heated tube (Figure 16). The air is heated to approximately 200 oC before it impinges as a laminar jet onto the TEM grid held at room temperature. Particle deposits are always higher at the stagnation point (where the heat transfer coefficient is highest). By imaging regions of the grid away from the stagnation point, where the distance between particles is much greater than the particle diameter, we virtually eliminate the possibility of creating artificial agglomerates on the grid. This device has been used extensively for compression ignition engine exhaust sampling (Soewono and Rogak 2009) and is similar in principle to that used by Rogak et al. (1993). In an earlier study of engine soot using the same TPS, it was found that particle projected area equivalent diameter measurements from the TPS/TEM method were within 5% of the mobility diameter (Soewono and Rogak 2009).  48  Figure 16: Thermophoretic Sampler. Resistance-heated tube is 70 mm long with 1.02 mm ID.  49  TPS samples were collected on 3-mm copper grids (Ted Pella, Inc. Prod. No. 01813-F) covered with 30 to 50 nm of amorphous carbon film. Thirteen TEM grids were analyzed with a Hitachi H7600 TEM. Although this instrument has sub-nanometer resolution, in practice particles below about 10 nm are often missed by the human operator given the particle selection process described below. High-resolution images were obtained with a FEI Tecnai G2 TEM at 200kV.  TEM grids were surveyed starting from nine evenly-spaced positions on the grids to avoid the possibility that the TEM operator would select only the “interesting” particles. At each position, one grid-square was surveyed along the diagonal. Approximately 10 images were taken for each grid-square. Scans across the grid-squares used the same magnification for all points (usually at 40-80 × 103 depending on the focus depth required). However, additionally magnified images were taken for particles if necessary for clarity or focus; these imaged particles were not doublecounted when more than one image was taken.  Particles in over 1200 images were analyzed with the aid of a MATLAB™ program to find the greatest length (L) and width (W) and subsequently the root mean squared (RMS) of the length and width for each particle. A total of 2121 particles were imaged and classified into the following categories: CNT/FUL, open agglomerates, closed agglomerate, spherical and ‘other’ (for otherwise unclassified particles). Examples from these categories are shown in Figure 17. Open access to the online image database from this study is available, according to the directions in Appendix H.  50  Figure 17: Representative particle for each of the particle shape categories: a. CNT/FUL, b. open agglomerate, c. closed agglomerate, d. spherical, e. ‘other’. Scale bars are 100 nm in images a, b, c and e, and 500 nm for image d.  51  3.3  Results  3.3.1 Fuel-specific emissions For each vehicle tested, fuel-based emission factors were determined for gaseous pollutants, PM2.5, organic carbon (OC) and elemental carbon (EC) (Reynolds, Grieshop, et al. 2011). Table 6 gives emission factors for the sub-selection of vehicles for which TPS/TEM sampling and analysis were conducted. These vehicles include: 6 with four-stroke engines running on CNG, 4 with four-stroke engines running on gasoline, and 3 with CNG two-stroke engines. Elemental carbon (EC) emissions were very low for the two-stroke vehicles. However, substantial numbers of solid carbon particles were observed on the TEM grids for these vehicles. The apparent lack of EC on the quartz filter samples may be an artifact of the OC/EC analysis methodology, which was conducted using a modified version of the NIOSH 5040 thermal-optical transmittance (TOT) protocol in a Sunset Laboratory OC/EC Analyzer (Subramanian et al. 2004). The much greater proportion of organic matter (OM) may compromise the instrument’s ability to distinguish smaller amounts of EC due to OC charring.  52  Table 6: Emissions for vehicles with TPS sampling included. Test No., Vehicle No., Model Year CNG four-stroke engines T04-V02-2009 T07-V04-2008 T09-V05-2001 T11-V06-2001 T13-V07-2001 T23-V14-2009 a  CO2 (g kg-1)  CO (g kg-1)  2480 ± 190 2540 ± 190 2570 ± 200 2530 ± 190 2380 ± 180  168 ± 13 127 ± 10 28 ± 2 195 ± 15 223 ± 17  2580 ± 200  Gasoline four-stroke engines T03-V02-2009 2220 ± 160 T08-V05-2001 750 ± 50 T12-V07-2001 1090 ± 80 a T22-V14-2009 3050 ± 230  a  CNG two-stroke engines T27-V17-2000 T30-V20-2000 T31-V21-1998  2170 ± 160 2040 ± 150 1660 ± 120  THC (g kg-1)  NOX (g kg-1)  CH4 (g kg-1)  PM2.5 (g kg-1)  OC (g kg-1)  EC (g kg-1)  Fuel Cons. (g km-1)  53 ± 4 57 ± 4 100 ± 8 22 ± 2 66 ± 5  19.7 ± 1.5 25.5 ± 1.9 31 ± 2.4 12.3 ± 0.9 7.8 ± 0.6  59 ± 4 48 ± 4 75 ± 6 32 ± 2 55 ± 4  0.035 ± 0.004 0.95 ± 0.05 0.34 ± 0.02 0.91 ± 0.05 0.35 ± 0.02  0 ± 0.01 0.57 ± 0.05 0.148 ± 0.015 0.73 ± 0.06 0.165 ± 0.016  0.016 ± 0.003 0.148 ± 0.013 0.126 ± 0.011 0.081 ± 0.01 0.062 ± 0.006  20.8 ± 1.2 21.6 ± 1.2 20.3 ± 1.2 21.9 ± 1.2 27 ± 1.5  41 ± 3  101 ± 8  23.5 ± 1.8  97 ± 7  0.023 ± 0.002  0.018 ± 0.005  0.006 ± 0.002  17.9 ± 1  565 ± 40 1191 ± 83 1325 ± 94  71 ± 5 243 ± 17 47 ± 3  15.8 ± 1.1 0.2 ± 0 0.5 ± 0  11.5 ± 0.8 15.7 ± 1.1 9.1 ± 0.6  0.52 ± 0.03 2.15 ± 0.34 0.63 ± 0.03  (n/a) 1.05 ± 0.07 0.19 ± 0.02  (n/a) 0.53 ± 0.04 0.24 ± 0.02  39.2 ± 2 48.3 ± 2.3 43.1 ± 2.2  76 ± 6  60 ± 5  27.3 ± 2.1  0.4 ± 0  0.017 ± 0.003  0.004 ± 0.006  0.002 ± 0.002  23.4 ± 1.4  98 ± 7 138 ± 10 181 ± 13  213 ± 16 236 ± 17 349 ± 24  2.3 ± 0.2 2.3 ± 0.2 1.1 ± 0.1  366 ± 27 373 ± 27 271 ± 19  7 ± 0.3 3 ± 0.1 4 ± 0.2  5.3 ± 0.4 2.2 ± 0.2 2.7 ± 0.4  0.002 ± 0.003 0.004 ± 0.006 0.004 ± 0.001  21.6 ± 1.1 22.2 ± 1.1 27.5 ± 1.4  b  New vehicle (V14); Uncertainty estimates are based on instrument error propagation. (n/a) = data not available b  53  3.3.2 Identifying carbon nanotube/fullerenic particles Particles were placed in the CNT/FUL category based several lines of evidence. First, Energy Dispersive X-ray (EDX) showed that the particles are carbon with only traces of metals or oxygen. Secondly, particles were stable under the electron beam in a vacuum. Thirdly, the nanotube and fullerene morphologies reported in the literature were observed in our samples. Finally, high resolution TEM (HRTEM) of a subset of the samples showed the expected wall structures of nanotubes and fullerenes (Ajayan 1999). These lines of evidence are discussed further below.  The auto-rickshaw CNTs appeared as tubes with a wide range of length/width ratios; the median (followed by range 25th percentile – 75th percentile) aspect ratio was 4.83 (5.57.6). CNTs were found in samples from all engines and fuel types (Figure 18). Most of the particles in the CNT/FUL category had structures similar to CNT/FUL particles reported previously (Ajayan 1999; Kadish and Ruoff 2000). For example, we observed cones with a vertex angle of ≈19 degrees (one of five possible fullerenic cone angles (Ge and Sattler 1994)), large tangled balls and bent tubes (Figure 19) (Hafner et al. 1998). About 24% of the particles in the CNT/FUL category had morphologies that we had not found previously reported as fullerenes (Figure 20). EDX showed them to be primarily carbon and extremely stable under the intense EDX electron beam. Due to their unusual morphologies, these particles were not included in calculations for CNT length. Highresolution TEM (Figure 21) confirmed the existence of multi-walled carbon structures with d-spacing of 0.34 ± 0.03 nm consistent with sp2 atomic bonding of multi-walled carbon nanotubes (MWCNT) or fullerenes (Iijima 1991; Ajayan 1999). 54  Of the 2121 particles imaged, 234 particles (11% of total) fit best in the CNT/FUL category, of which 178 (8.4% of total) were identified as “classical” nanotube or fullerene shapes reported previously in the literature. The frequency of CNT/FUL varied among the fuel/engine types, but these differences could not be attributed to specific technologies, fuels or operating conditions (Appendix I). Figure 22 summarizes particle counts by morphology category and size for all 13 vehicle tests. For the whole test set, our best estimate of the CNT/FUL proportion for the Indian auto-rickshaws emissions is 10 ± 7% of the non-volatile particles. The median length of the nanotubes was 168 nm (98-262 nm). This is much shorter than laboratory synthesized CNTs. The shorter length may result from the short engine residence time (2-20 milliseconds at high temperature) compared with more than 100 ms required to produce closed shell fullerenes and tubes from a flame source (Grieco et al. 2000).  55  Figure 18: Carbon nanotubes and nanotube agglomerates from three different fuel-engine combinations: CNG/four-stroke (a and b); gasoline/four-stroke (c and d); CNG/two-stroke (e and f). All scale bars are 100 nm.  56  Figure 19: Fullerenes and CNT types: fullerenic cones with tip angles of approximately 19 degrees from separate 2-stroke engines (a and b); string-like, long carbon nanotubes (c and d); jointed carbon nanotubes from CNG/two-stroke engines and gasoline/four-stroke engines respectively (e and f). Scale bars represent 100 nm.  57  Figure 20: Examples of particles were categorized as CNT/FUL but without the expected features (such as a hollow core) of fullerenes or carbon nanotube. Scale bars are 500 nm.  58  Figure 21: High resolution TEM images of fullerenic wall structures from a CNG/four-stroke engine PM. a, MWCNT and wall structure of approximately 22 layers; b, wide-diameter MWCNT; c, fullerene structure.  59  Figure 22: Size distribution of the imaged particles (N=2121) by shape category. Size corresponds to RMS length parameter from TEM image analysis.  60  3.4  Discussion  3.4.1 Possibility of sampling artifacts When using TEM grids with amorphous carbon films, there is the risk of contamination from carbon nanotubes and fullerenes formed during the electro-deposition manufacturing process of the grid film. Klie et. al. (2004) confirmed previous observations by Harris (2001) that grids with amorphous carbon films can contain contaminant fullerenes. Through surveys of three types of TEM films, Klie et al. determined that about three carbon nanotube or fullerene particles could occur per grid square. To estimate the importance of contamination we have extrapolated our observed CNT/FUL counts to the entire area of a grid square. For example, Table 7 lists the counts and interrogated area fractions for a particular vehicle (test T07). The nine grid squares contain an estimated 2221 CNT/FUL particles, while the expected number of contaminant particles (based on Klie et al. (2004)) is only 27 particles.  We have repeated this analysis for other grids (Appendix F), and for all cases the estimated number of carbon nanotubes is 10 to 100 times higher than the expected contamination. As an alternative assessment of contamination, we have polled several microscopists and the grid suppliers (collectively having ~50 person-years of TEM experience): none had ever observed contaminant carbon nanotube or fullerenes.  61  Table 7: Estimates of total grid square CNT/FUL counts for IARP Test 07 Grid Location Points  CNT/FUL count  Imaged 2 Area [um ]  % Imaged Area/square  1 2 3 4 5 6 7 8 9 Total  5 0 0 30 6 11 0 0 9 61  131.2 78.7 78.7 3112.1 131.2 145.9 78.7 52.5 114.4  1.5 0.9 0.9 34.7 1.5 1.6 0.9 0.6 1.3  Estimated # CNT/FUL per square 342 0 0 87 410 677 0 0 706 2221  3.4.2 Estimates of fuel-specific CNT/FUL emissions We estimated vehicle CNT/FUL emission rates, ECNT (#/kg fuel), using their relative abundance on the microscope grids, NCNT (# / unit area of grid), elemental carbon emissions, EEC (g/kg fuel), and estimated volume of elemental carbon on the grid, VEC. That is, ECNT =  N CNT E EC ρ ECVEC  [7]  where ρEC is the density of elemental carbon. Estimating the denominator of Equation 7 is not straightforward. EC is operationally defined as the portion of PM that does not evaporate in a hot inert atmosphere, but will react to form CO2 in a hot oxidizing environment. Studies of engine PM emissions have shown EC to be composed mainly of soot aggregates (Kittelson 1998). EC analyses include corrections for the organic material converted to char in the evaporation portion of the analysis (Andreae and Gelencsér 2006). The key assumption made here is that the EC measured from the filter samples (summarized in Table 1) corresponds to the fractal-like aggregates visible in the TEM. It is also possible that the large particles in the “other” shape category contribute to EC  62  measurements, so we have estimated VEC with and without this category. Details of the VEC calculations are provided in Appendix J.  To use Equation 7, it is also necessary to assume that the particles collected during the TPS sampling are representative of the entire drive cycle (for which EEC is determined). This assumption can be partially tested by using time-resolved measurements of PM2.5, CO2, CO and hydrocarbons to estimate the PM emissions (per unit of fuel) for the acceleration portion of the IDC and the full IDC (Reynolds, Grieshop, et al. 2011). These indicate that the ratio of the PM emissions (acceleration/ full IDC) is 1.08±0.4.  Considering the difficulties in estimating VEC , CNT/FUL emission factors could be as high as 1012 per kg fuel, but are more likely on the order of 1011 per kg fuel. Previous measurements in vehicle tunnels and other traffic-dominated sites indicate that particle number emissions range from 4 x 1014 to 4 x 1016 particles per kg of fuel burned (Kittelson et al. 2006, 2004; Ban-Weiss et al. 2009). These estimates are for total particle counts (including semi-volatile sulfuric acid and organic particles), whereas ours include only the non-volatile particles corresponding roughly with elemental carbon. For comparison, the proposed Euro 5/6 standards for new light commercial vehicles is 6 x 1011 per km, which translates to 60 x 1011 per kg of fuel using a 0.1 kg/km average fuel consumption (typical for a passenger car).  3.4.3 Are auto-rickshaws the only vehicles producing CNT/FUL? The ubiquity of CNT/FUL particles in our samples is striking: they were found on 11 of 13 grids imaged. The auto-rickshaw engines tested use the same (albeit simpler) 63  technology found in automotive engines worldwide and were in varying states of repair (their conventional PM mass emission rates varied by over an order of magnitude). Considering this, it is unlikely that CNT/FUL emissions are a unique feature of Indian auto-rickshaws.  So why are there not more reports of CNT and fullerene emissions from engines? TEM is the only method available to identify CNT/FUL particles and differentiate them from soot or other prevalent particles in an aerosol mixture, and microscopy is a laborious technique. There are very few studies of SI engine emissions using TEM. Diesel exhaust particles have been examined using TEM by hundreds of researchers, but CNT/FUL are likely rare in diesel exhaust because laboratory studies show that flame conditions leading to soot are less favorable for CNT growth (Height et al. 2004). Few studies (of diesel or gasoline engines) have examined more than several hundred aggregates, so rare CNT/FUL might be dismissed as contamination. After finding the CNT/FUL particles in auto-rickshaw emissions, we reanalyzed TPS/TEM images from an earlier study of diesel and natural gas compression-ignition engines (Soewono and Rogak 2009). Review of 600 images from that study (which used the same sampling methods as used in the present work) yielded only three possible fullerenes with the characteristic hollow core. Prior work has shown that compression-ignition engine emissions are dominated by soot particles (Grieco et al. 2000) but may contain a small number of carbon nanotube structures (Evelyn et al. 2003).  Carbon nanotubes have been noted in only a few studies of particles sampled from the ambient air (Murr and Garza 2009; Murr et al. 2004). However, after emission, CNTs 64  may combine with larger ambient particles, and thereby escape identification or be misidentified as mineral fibers. Given the difficulty of their detection and the sparseness of TEM studies, CNT/FUL particles might contribute more than 1% of the ambient nonvolatile particle count and still have escaped widespread recognition. However, it must be stressed that insufficient data exists to make reliable estimates of regional emissions or atmospheric concentrations.  3.5  Conclusion  Our finding that spark-ignited engines can produce significant quantities of carbon nanotubes and fullerenes suggests that these particles may be more prevalent in ambient air than previously believed, and may have always contributed to the health impacts detected by epidemiological studies of air pollution. Hence future studies to explore the population health effects of CNT/FUL will require measurement of the prevalence of CNT/FUL using laborious microscopy with a focus on particles with the characteristics of  combustion  byproducts  rather  than  intentionally  synthesized  materials.  65  4 Ricardo Hydra engine emissions 4.1  Motivation for laboratory engine sampling  With so few morphology studies of PM emissions from SI engines, additional sampling was required to verify that SI engines in general are producing, or have the potential to produce, CNT/FULs. Furthermore, with a range of unusual particles, additional sampling is useful to determine a typical, baseline morphology for certain engine/fuel combinations.  Engine emission samples were collected from the Ricardo Hydra test engine in the UBC Clean Energy Research Centre (CERC). The Ricardo Hydra (RH) is a single-cylinder, natural gas burning, spark-ignited test engine. In addition to investigating the general morphology of SI engine PM, several specific experimental questions were considered including 1) whether carbon nanotubes and other fullerene structures (CNT/FUL) are being produced in SI engines; 2) under what conditions (load) are CNT/FULs formed; and 3) if the production of CNT/FULs is associated with PM indentified as charred engine oil. Each of these points leads to the development of a hypothesis and, in turn, proposed testing conditions for the engines. The hypotheses are discussed below.  Based solely on the results to date on Reynolds et. al investigation of gaseous emissions from the IARP project, it was hypothesized that CNT/FULs are produced from SI engines when the conditions in the combustion chamber are such that emissions consist of high CO and the engine is under a high load (Chapter 3). However, it is assumed that the CNT/FULs are not necessarily unique to these conditions, but are simply one experimental point.  66  In addition, it is hypothesized that CNT/FULs are produced from SI engines when the conditions in the combustion chamber are such that the air/fuel ratio is lean. Laboratory production of CNT/FULs from combustion sources are a common way of producing mass quantities of this crystalline carbon material. In most cases, researchers have found that optimized, fuel-efficient combustion tends to produce carbon nanotubes (Ajayan 1999; Vander Wal et al. 2000). Similarly, a 2004 study of diesel emissions that passed through diesel PM filter, showed fullerene-like soot. Below is an excerpt that paper characterizing the UFPM soot from EuroIV diesel engine:  “The measures introduced for engine-internal emission reduction of commercial vehicles reduce the number of particles and therefore the mass of emitted soot particulate, but produce very fine and reactive fullerene-like soot. The fullerene-like soot may be the result of the optimized mixing behavior of air and diesel fuel (air/fuel >1.3) in the combustion chamber (Su et al. 2004).”  The final hypothesis for CNT/FULs production from SI engine is that charred engine oil produced heavy metal particles to act as nucleating particles for CNT/FUL formation. The formation of CNTs in laboratory setting can be nucleated by nano-sized metal particles (Ajayan 1999). One possible source of metals in the IARP engines was from charred engine oil.  67  4.2  Methods for sampling the Ricardo Hydra test engine  The laboratory-based test engine, known as the Ricardo Hydra (RH), is located at the University of British Columbia in the UBC Clean Energy Research Centre (CERC). The Ricardo Hydra is a single-cylinder, 4-stroke, natural gas burning, SI test engine with a swept volume of 450 cc (Kastanis 2010). Built in 1984, this engine is capable of working as a diesel compressionignition engine with additional equipment or as a SI engine (Kastanis 2010). For the tests described in this chapter, the Ricardo Hydra was configured as a SI engine with a maximum speed of 5400 rpm. Further details on the operation of the Ricardo Hydra engine can be found in E.J. Kastanis’ MaSc. thesis (Kastanis 2010).  4.2.1 Engine conditions and particle sample collection For the RH engine tests run during December 2010, the SMPS and the TPS were used to collect particle emission size distributions and PM samples from the emissions stream. Figure 23 shows the layout of the engine sampling test. The exhaust was diluted using dried, filtered shop air. The dilution ratio ranged between 1:10 to 1:100 at times. TEM and SMPS samples were taken when the dilution ratio was approximately 1:12. (Note: this was not the same for all tests, and due to a software failure, the dilution ratio for each test was not individually recorded as expected.) Two particle samples were taken for each operating conditions which consisted of two different loads and four air/fuel ratios (0.9, 1, 1.3 and 1.6) to attempt to determine a relationship amongst these variables. All the engine conditions which were sampled are outlined in Table 8. 68  Figure 23: Schematic of the Ricardo Hydra sampling test set-up  69  Table 8: Engine operation settings for the Ricardo Hydra testing engine. SMPS profiles for these tests are shown in Appendix K. Electronic files containing gas data are listed in Appendix L. Test #  RH Log  Lambda  Fuel mass flow rate (kg/hr)  TEM Grid ID  1 1 1 1 2 2 5 5 8 8 10 10 13 13 13 16 16 16 16 16 16  101 201 301 401 102 202 105 205 108 208 110 210 113 213 313 116 216 316 416 516 use 516  0.9 0.9 0.9 0.9 1 1 1.3 1.3 1.6 1.6 1 1 1.3 1.3 1.3 1.6 1.6 1.6 1.6 1.6 1.6  0.73 0.73 0.73 0.73 0.73 0.73 0.73 0.73 0.73 0.73 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6  E2 E3 n/a E4 B1 B2 B3 B4 C1 C2 n/a A3 C3 C4 C5 D1 D2 D3 D4 D5 E1  70  4.2.2 Sampling and data collection issues A variety of issues arose during sampling of the RH engine. A large percentage of the gaseous byproduct is steam which can condense in the sampling lines even after the emissions are diluted. As a result, PM sampling was inhibited by periods of water condensation.  Condensed water in the sample lines and in the TPS caused particles to agglomerate or collect into droplets. Some dry sampling of PM was possible when the temperature in the sampling lines was high enough to prevent condensation, but the majority of PM samples imaged under the TEM showed that particles had deposited on the grid in liquid droplets rather than individual particles. As a result, this sample set was not appropriate for determining overall emission rates of particle types, but rather an overall qualitative morphology survey. The results of this survey are in the results section of this chapter.  Two additional issues occurred during sampling of this data set that may have some effect on the quality of sampling collection. First, the arrangement of instruments was such that the low CO2 sensor, the SMPS and the TPS all pulled air from a single emissions line. Even with additional dilution into the flow stream, it was impossible to run the TPS and the SMPS simultaneously without backflow through the SMPS when the pump on the TPS was running. As a result, once the engine was at steady state for a particular engine condition, the data on the SMPS was taken first. Afterward, the TPS samples were taken.  71  4.2.3 Microscopy of RH samples The RH samples were collected onto silicon monoxide film TEM grids (Ted Pella # 01830) to avoid the possibility of carbon nanotubes that might be present in amorphous carbon-based TEM grid films produced during the film manufacturing. As a result, TEM imaging of the RH samples required a higher accelerating voltage which reduced the contrast in the final images.  The RH samples consisted of a complex collection of dense, dark (possibly charred oil) spheres, small primary particle agglomerates, small particle deposits, volatile or semi-volatile droplets, carbon nanotubes and fullerenes and large ‘other’ particles.  Table 9 lists engine samples that were imaged via microscopy. A range of particle types were found, and particle categories were similar to those found in the overall survey of particles from the IARP project. 608 images were taken of the four samples analyzed. However, because of water in the TEM samples, many particles were deposited into droplets and were therefore not countable to determine efficiency with any kind of reasonable accuracy. As a result, there are only quantitative results of particle frequency from RH105. The results for the remaining three samples are in the form of representative particle types without particle frequency information.  Table 9: Ricardo Hydra (RH) engine samples imaged under TEM Air / fuel Fuel mass Engine Sample TPS TPS Power Number (Grid ID) ratio flow rate Sample (lambda) (load) Time (s) (kg/hr) 105 (B3) 1.3 0.73 50 0.9 amps @ 1.2 V 108 (C1) 1.6 0.73 45 0.8 amps@ 1.2 V 208 (C2) 1.6 0.73 45 0.6 amps @ 1.2 V 213 (C5) 1.3 0.6 45 0.89 amps @ 1.0 V  Number of Microscopy Images 169 255 148 36  72  4.3  Morphology results  For the four imaged RH samples, open and closed agglomerate, spherical (or oil droplet) particles, CNT/FUL or fibrous particles, semi-volatile particles and “other” particle categories were distinguishable. As mentioned above, only RH105 could be counted with any rough accuracy. The results from RH 105 are shown in Figure 25: Morphology and size distribution of RH105 engine sample Figures 24 and 25. Morphology results from samples RH 108, RH 208 and RH 213 are included here in the form of qualitative particle types.  4.3.1 Results from RH 105 (λ=1.3; high load) Six particle morphology categories were found in the RH 105 sample – open or closed agglomerates, spherical, fiber, semi-VOC and ‘other’ (Figure 24). Rough particle counts show that VOC particles dominate the number of particles found (Figure 25). Given the problems with condensation in the sampling lines, this result is to be expected because particles in the emission stream could get caught in water droplets during condensation in the stream. The distribution of particles centers around 500 nm, but this is most likely an artifact of particle collection in water droplets rather than an indicator of actual particles size. In contrast, an SMPS profile of RH 105 and 205 (which had the same engine conditions), showed that particle diameters were roughly 30 - 90 nm. This may be a more accurate estimate of the general particle diameter. The agglomerates make up a small fraction of particles overall.  73  Figure 24: Particle categories for the RH 105 sample. a) open agglomerate; b) closed agglomerate; c) spherical or oil droplet particles; d) possible nanotube or fiber particles; e) VOC or semi VOC particles; and f) ‘other’ or otherwise unidentified particles.  74  Figure 25: Morphology and size distribution of RH105 engine sample (λ=1.3)  75  Figure 26: SMPS profile of the Test 105 particles and the Test 205 particles showing extreme variation in samples.  76  4.3.2 Results from RH 108 (λ=1.6; high load) The particles found in RH 108 provided the strongest evidence that nanotubes are being produced by CNG SI engines. While frequency could not be determined due water droplet coagulation of particles on the TEM grid causing significant particle overlap, an enormous number of nanotubes were found near the stagnation point of this TEM sample (Figure 27-e,f). In addition, agglomerates, semi-VOCs and other particles were also found (Figure 27 27). SMPS profiles of Test 108 and Test 208 show particle diameter concentrations at roughly 10, 40 and 100 nm (Figure 28). It is possible that the multiple peaks at three different particle sizes, rather than a smooth distribution of particle diameters, is due to the SMPS detecting water droplets rather than individual particles.  77  Figure 27: Typical particles found in the RH 108 sample. a,b) agglomerate particles; c) spherical or oil droplet particles; d) VOC or semi VOC particles plus some ‘elongated’ droplets; e, f) carbon nanotubes and fullerenes  78  Figure 28: SMPS profiles of Tests 108 and 208  79  4.3.3 Results from RH 208 (λ=1.6; high load) While engine conditions were identical for the RH 108 and the RH 208 samples, considerably different particles were found upon imaging the samples. The standard particles of agglomerates and spherical oil droplet (Figure 29) were found, along with numerous semi-VOC, massive compound or ‘other’ particles and what appeared to be a single, poorly formed nanotube (Figure 30). However, the imaging of this sample also revealed unidentified large crystalline particles imbedded in other material. In Figure 31 31 locations of stratified material, which typically indicates crystalline substances in the TEM, are identified by arrows. These embedded particles would need to be isolated to determined their composition or source.  80  Figure 29: Agglomerate and oil droplet samples from RH 208 sample  81  Figure 30: Particles found in the RH 208 sample. a) possible nanotube ; b) semi VOC particles; c,d) compound particles.  82  Figure 31: Unidentified crystalline particles embedded in compound particles of the RH208 sample.  83  4.3.4 Results from RH 213 (λ=1.3; low load) The only TEM data currently available from the RH engine tests running with a lower engine load is RH 213. TEM imaging was limited on this sample due to loose TEM film on the grid. Nevertheless, the same typical particle types were found including small agglomerates, spherical particles and ‘other’ particles. Examples of those particles are found in Figure 32. SMPS distributions (Figure 33) of Test 113 and Test 213 show that the majority of particles were around 100 nm, though the total number of particles counted for each consecutive test was dramatically different. This may have been due to the variety of engine sampling problems mentioned earlier in this chapter.  84  Figure 32: Particles from RH 213 sample. a) agglomerate particles (lower and right sides); b) spherical or oil droplet particle; c) ‘other’ particle. Note that the RH 213 samples were temporarily labels RHC5 indicating the grid number, rather than the test number.  85  Figure 33: SMPS profile of Tests 113 and 213 which shows considerable variability between samples at the same conditions.  86  4.3.5 Apparent abundance of carbon nanotubes While evidence of nanotubes appears in three of the four RH samples, the tubes seem in RH 105 (Figure 24-d) and RH 208 (Figure 30-a) cannot be clearly identified as nanotubes or specifically carbon nanotubes and should only be considered as possible nanotubes. However, an abundance of elongated, tube structures were found in the RH 108 sample. Counting the total number of these particles was not attempted due to the enormous number of particles and the particle overlap on the grid (Figure 34). Nevertheless, it is clear that relatively short nanotube particles are being emitted from the RH test engine.  87  Figure 34: Expanse of nanotube particle deposition. (top images) HR TEM of nanotubes at 600K and 500K times respectively. (bottom image) TEM image of particles at 100K times magnification.  88  4.4  Conclusions from laboratory testing  It appears that, in general, SI engines are producing tube structures, and there are several pieces of evidence that indicate these particles are MWCNTs. First, each particle shows the characteristic hollow core of a shell-like structure of 2-D graphene formed into 3-D structures. Second, there are several examples of these particles deforming under stress (Figure 35) in ways predicted by the literature for carbon nanotube structures (Ajayan 1999).  Finally and most  importantly, HR TEM of the RH 108 samples show these abundant tubes are composed of layers of crystalline material sheets typical of multi-walled carbon structures. These walls have the characteristic d-spacing of 0.34 ± 0.04 nm (Figure 36, a-b) consistent with sp2 atomic bonding of multi-walled carbon nanotubes (MWCNT) (Iijima 1991; Ajayan 1999). Furthermore, there appear to be larger carbon nanotubes in the RH 108 samples as well with distinctive carbon graphene layer wall structures (Figure 37).  The original hypotheses described in Section 4.1, that CNT/FUL particles are produced when SI engines are operated under high load or when the engine emits high CO2 could not be confirmed or disproven with this data. Further research and data collection will be required. However, the hypothesis that CNT/FUL particles are produced in SI engines running at a high air-fuel ratio could be considered plausible with the CNT findings in Test 108, while the subsequent lack of CNT/FUL particles in Test 208 is cause for concern. Hence, the hypothesis that CNT/FUL particles are produced in SI engines under high air-fuel ratio could not be proved or disproved. Further microcopy of the remaining samples and further sampling is required.  89  Figure 35: Bent carbon nanotubes which appears to have buckled under stress due to a hollow center.  90  a  b  Figure 36: High resolution TEM images of the tube structures from in the RH 108 samples. Insert ‘a’ (below) shows a parallel wall structure with atomic spacing consistent with carbon graphene sheets. Insert ‘b’ (below) shows similar wall structures of a tube that is likely behind the larger tube in the center of the image above because wall structures would not show up in the center of a tube. Note that the graininess of these images that obscures a clear view of the parallel wall structures is due to the silicon monoxide film on the TEM grid.  91  92  Figure 37: Larger carbon nanotube in the RH 108. Insert shows wall structure of tube.  93  While CNT/FUL particles were found in abundance from a lean air/fuel ratio engine sample (RH 108), which is consistent with the literature on the intentional production of CNT/FULs, these particles were only found in one of the two samples from that same engine condition. Further research is required to predict the conditions in which these particles are likely to be formed in an SI engine. Nevertheless, it appears that carbon nanotubes are produced in abundance under some conditions in the combustion chamber of spark-ignited engines.  94  5 Conclusions While SI engines generally emit significantly lower overall PM emissions (by mass), the research presented here shows that pollutants from this source are significant to understanding overall PM pollution from motor vehicles and may pose health risks due to the complex morphology of particles not previously known. SI engines are shown here via TEM to produce a wide range of morphologically distinct particles that would otherwise be indistinguishable using any other method of measurement. Ultimately, these images may serve as a benchmark for SI engine PM morphology since previous studies have not documented the scope of this PM morphology to date.  The efficiency of the TPS device is clearly dependant on a large number of parameters and the potential for measurement errors in even a few of these parameters makes these efficiencies range widely and applicable to only specific sets of conditions from physical device setting, temperature ranges, and flow rates. The TPS efficiency values reported here should be taken as only rough estimates.  Carbon nanotubes and fullerenes were found to be emitted by in-use Indian auto-rickshaws running on either gasoline or compressed natural gas and in various conditions and ages. Although CNT/FUL emissions can be as high as 27% for some vehicles, the average emission rate is 10% ± 7% of the non-volatile particles. Currently, it is unknown how these crystalline carbon particles are formed in the engine. Nanotubes are formed in similar combustion environments in the laboratory under carefully controlled conditions. There is no indication why these auto-rickshaws are emitting carbon-nanotubes in large quantities. Widely variable 95  engine conditions make the parameters for carbon nanotube production nearly impossible to identify without further research.  However, subsequent sampling of the laboratory CNG SI engine confirmed many previous reports of particles other than typical engine soot are emitted from SI engines including brief references to carbon nanotubes and fullerenes. The prediction that CNTs are produced by SI engine of high air/fuel ratio could not be exclusively confirmed or rejected with this dataset, but the discovery of CNT/FUL particles from a laboratory engine further supports conclusion that the CNT/FUL particles found in the IARP samples were not simply an artifact of sampling. Instead, it appears that carbon nanotubes and fullerenes represent a significant portion of the composition of particulate matter from spark-ignited engines.  96  References Ahmaruzzaman, M. (2010). A review on the utilization of fly ash, Progress in Energy and Combustion Science 36(3): 327-363. Ajayan, P. M. (1999). Nanotubes from Carbon, Chem. Rev. 99(7): 1787-1800. Andreae, M. O., Gelencsér, A. (2006). Black carbon or brown carbon? The nature of lightabsorbing carbonaceous aerosols, Atmos. Chem. Phys. 6(10): 3131-3148. Ban-Weiss, G. A., Lunden, M. M., Kirchstetter, T. W., Harley, R. A. (2009). Measurement of Black Carbon and Particle Number Emission Factors from Individual Heavy-Duty Trucks, Environ. Sci. Technol. 43(5): 1419-1424. Baughman, R. H., Zakhidov, A. A., de Heer, W. A. (2002). Carbon Nanotubes--the Route Toward Applications, Science 297(5582): 787 -792. Blom, A., Nolan, T., Storey, J., Whitney, K. (2000). Characterization of Exhaust Emission Particulate Matter by Transmission Electron Microscopy, 2000 Diesel Engine Emission Reduction Workshop, San Diego, California, August 20-24, www.osti.gov/bridge/purl.cover.jsp;jsessionid=9A0A449401CF08FF8DF3856239AFF3 C7?purl=/827863-JeNKoY/native/, . Brown, B. (n.d.). Theoretical and Experimental Evaluation of a Thermophoretic Aerosol Collector, . Chakrabarty, R. K., Moosmüller, H., Arnott, W. P., Garro, M. A., Walker, J. (2006). Structural and Fractal Properties of Particles Emitted from Spark Ignition Engines, Environ. Sci. Technol. 40(21): 6647-6654. Dallmann, T. R., Harley, R. A. (2010). Evaluation of mobile source emission trends in the United States, J. Geophys. Res. 115: 12 PP. 97  Donaldson, K., Murphy, F. A., Duffin, R., Poland, C. A. (2010). Asbestos, carbon nanotubes and the pleural mesothelium: a review of the hypothesis regarding the role of long fibre retention in the parietal pleura, inflammation and mesothelioma, Part Fibre Toxicol 7: 5. Elghawi, U., Mayouf, A., Tsolakis, A., Wyszynski, M. (2010). Vapour-phase and particulatebound PAHs profile generated by a (SI/HCCI) engine from a winter grade commercial gasoline fuel, Fuel 89(8): 2019-2025. Englert, N. (2004). Fine particles and human health--a review of epidemiological studies, Toxicol. Lett. 149(1-3): 235-242. Etissa, D., Mohr, M., Schreiber, D., Buffat, P. (2008). Investigation of particles emitted from modern 2-stroke scooters, Atmos. Environ. 42(1): 183-195. Evelyn, A., Mannick, S., Sermon, P. A. (2003). Unusual Carbon-Based Nanofibers and Chains among Diesel-Emitted Particles, Nano Lett. 3(1): 63-64. Ge, M., Sattler, K. (1994). Observation of fullerene cones, Chem. Phys. Lett. 220(3-5): 192-196. Goel, A., Hebgen, P., Vander Sande, J. B., Howard, J. B. (2002). Combustion synthesis of fullerenes and fullerenic nanostructures, Carbon 40(2): 177-182. Grieco, W. J., Howard, J. B., Rainey, L. C., Vander Sande, J. B. (2000). Fullerenic carbon in combustion-generated soot, Carbon 38(4): 597-614. Hafner, J. H., Bronikowski, M. J., Azamian, B. R., Nikolaev, P., Rinzler, A. G., Colbert, D. T., Smith, K. A., Smalley, R. E. (1998). Catalytic growth of single-wall carbon nanotubes from metal particles, Chem. Phys. Lett. 296(1-2): 195-202. Hansen, A., Rosen, H., Novakov, T. (1984). The aethalometer -- An instrument for the real-time measurement of optical absorption by aerosol particles, Science of The Total Environment 36: 191-196. Harris, P. J. F. (2001). Carbonaceous contaminants on support films for transmission electron 98  microscopy, Carbon 39(6): 909-913. Height, M. J., Howard, J. B., Tester, J. W., Vander Sande, J. B. (2004). Flame synthesis of single-walled carbon nanotubes, Carbon 42(11): 2295-2307. Iijima, S. (1991). Helical microtubules of graphitic carbon, Nature 354(6348): 56-58. Incropera, F. P., DeWitt, D. P. (2002). Fundamentals of Heat and Mass Transfer, 5th ed. John Wiley & Sons. Ishiguro, T., Takatori, Y., Akihama, K. (1997). Microstructure of diesel soot particles probed by electron microscopy: First observation of inner core and outer shell, Combust. Flame 108(1-2): 231-234. Jander, H., Wagner, H. G. (2006). Formation of flame ions, clusters, nanotubes, and soot in hydrocarbon flames, Combust Explos Shock Waves 42(6): 696-701. Kadish, K. M., Ruoff, R. S. (2000). Fullerenes: chemistry, physics, and technology. Wiley-IEEE. Kanda, K. (1991). Energy dispersive X-ray spectrometer, . Kastanis, E. J. (2010). Jet squish motion in a homogeneous-charge spark-ignition engine fueled by natural gas. Kittelson, D. B. (1998). Engines and nanoparticles: a review, J. Aerosol Sci. 29(5-6): 575-588. Kittelson, D. B., Watts, W. F., Johnson, J. P. (2004). Nanoparticle emissions on Minnesota highways, Atmos. Environ. 38(1): 9-19. Kittelson, D., Watts, W., Johnson, J., Schauer, J., Lawson, D. (2006). On-road and laboratory evaluation of combustion aerosols--Part 2:: Summary of spark ignition engine results, J. Aerosol Sci. 37(8): 931-949. Kleeman, M. J., Riddle, S. G., Robert, M. A., Jakober, C. A. (2008). Lubricating Oil and Fuel Contributions To Particulate Matter Emissions from Light-Duty Gasoline and HeavyDuty Diesel Vehicles, Environ. Sci. Technol. 42(1): 235-242. 99  Klie, R., Ciuparu, D., Pfefferle, L., Zhu, Y. (2004). Multi-walled carbon nanotubes on amorphous carbon films, Carbon 42(10): 1953-1957. Köylü, Ü. Ö., Faeth, G. M., Farias, T. L., Carvalho, M. G. (1995). Fractal and projected structure properties of soot aggregates, Combust. Flame 100(4): 621-633. Kroto, H. W., Heath, J. R., O'Brien, S. C., Curl, R. F., Smalley, R. E. (1985). C60: Buckminsterfullerene, Nature 318(6042): 162-163. Lam, C., James, J. T., McCluskey, R., Arepalli, S., Hunter, R. L. (2006). A review of carbon nanotube toxicity and assessment of potential occupational and environmental health risks, Crit. Rev. Toxicol 36(3): 189-217. Lee, J., Altman, I., Choi, M. (2008). Design of thermophoretic probe for precise particle sampling, J. Aerosol Sci. 39(5): 418-431. Lee, K. O., Cole, R., Sekar, R., Choi, M. Y., Kang, J. S., Bae, C. S., Shin, H. D. (2002). Morphological investigation of the microstructure, dimensions, and fractal geometry of diesel particulates, Proceedings of the Combustion Institute 29(1): 647-653. Mathis, U., Kaegi, R., Mohr, M., Zenobi, R. (2004). TEM analysis of volatile nanoparticles from particle trap equipped diesel and direct-injection spark-ignition vehicles, Atmos. Environ. 38(26): 4347-4355. Mazzoleni, C., Kuhns, H. D., Moosmüller, H., Keislar, R. E., Barber, P. W., Robinson, N. F., Watson, J. G., Nikolic, D. (2004). On-road vehicle particulate matter and gaseous emission distributions in Las Vegas, Nevada, compared with other areas, J Air Waste Manag Assoc 54(6): 711-726. Megaridis, C. M., Dobbins, R. A. (1987). Morphology of Flame-Generated Soot as Determined by Thermophoretic Sampling, Langmuir 3: 254-259. Miller, A. L., Stipe, C. B., Habjan, M. C., Ahlstrand, G. G. (2007). Role of lubrication oil in 100  particulate emissions from a hydrogen-powered internal combustion engine, Environ. Sci. Technol 41(19): 6828-6835. Muller, J., Huaux, F., Moreau, N., Misson, P., Heilier, J., Delos, M., Arras, M., Fonseca, A., Nagy, J. B., Lison, D. (2005). Respiratory toxicity of multi-wall carbon nanotubes, Toxicol. Appl. Pharmacol. 207(3): 221-231. Murr, L., Bang, J., Esquivel, E., Guerrero, P., Lopez, D. (2004). Carbon Nanotubes, Nanocrystal Forms, and Complex Nanoparticle Aggregates in common fuel-gas combustion sources and the ambient air, Journal of Nanoparticle Research 6(2/3): 241-251. Murr, L., Garza, K. (2009). Natural and anthropogenic environmental nanoparticulates: Their microstructural characterization and respiratory health implications, Atmos. Environ. 43(17): 2683-2692. Neer, A., Koylu, U. O. (2006). Effect of operating conditions on the size, morphology, and concentration of submicrometer particulates emitted from a diesel engine, Combust. Flame 146(1-2): 142-154. Oberdörster, G., Oberdörster, E., Oberdörster, J. (2005). Nanotoxicology: An Emerging Discipline Evolving from Studies of Ultrafine Particles, Environ Health Perspect 113(7): 823-839. Ortner, H. M., Hoffmann, P., Weinbruch, S., Stadermann, F. J., Wentzel, M. (1998). Chemical characterization of environmental and industrial particulate samples†, Analyst 123(5): 833-842. Palotás, A. B., Rainey, L. C., Feldermann, C. J., Sarofim, A. F., Vander Sande, J. B. (1996). Soot morphology: an application of image analysis in high-resolution transmission electron microscopy, Microsc. Res. Tech 33(3): 266-278. Patashnick, H., Meyer, M., Rogers, B. (2002). Tapered element oscillating microbalance 101  technology, Proceedings of the North American/Ninth U.S. Mine Ventilation Symposium. North American/Ninth U.S. Mine Ventilation Symposium. Reynolds, C. C. O., Grieshop, A., Kandlikar, M. (2011). Climate and health relevant emissions from in-use Indian three-wheelers fueled by natural gas and gasoline (Accepted for publication), Environ. Sci. Technol. In Press. Reynolds, C. C. O., Kandlikar, M., Badami, M. G. (2011). Determinants of PM and GHG emissions from natural gas-fueled auto-rickshaws in Delhi, Transport. Res. D-Tr E 16(2): 160-165. Richter, H., Howard, J. B. (2000). Formation of polycyclic aromatic hydrocarbons and their growth to soot--a review of chemical reaction pathways, Prog. Energ. Combust. 26(4-6): 565-608. Rogak, S. N., Flagan, R. C., Nguyen, H. V. (1993). The Mobility and Structure of Aerosol Agglomerates, Aerosol Sci. Technol. 18(1): 25. Rosen, H., Novakov, T. (1977). Raman scattering and the characterisation of atmospheric aerosol particles, Nature 266(5604): 708-710. Ruppecht, E., Meyer, M., Patashnick, H. (1992). The tapered element oscillating microbalance as a tool for measuring ambient particulate concentrations in real time, Journal of Aerosol Science 23(Supplement 1): 635-638. Russell, L. M. (2003). Aerosol organic-mass-to-organic-carbon ratio measurements, Environ. Sci. Technol. 37(13): 2982-2987. Sadezky, A., Muckenhuber, H., Grothe, H., Niessner, R., Pöschl, U. (2005). Raman microspectroscopy of soot and related carbonaceous materials: Spectral analysis and structural information, Carbon 43(8): 1731-1742. Seinfeld, J. H., Pandis, S. N. (1998). Atmospheric Chemistry and Physics - From Air Pollution to 102  Climate Change (2nd Edition). Singh, D. N., Kolay, P. K. (2002). Simulation of ash-water interaction and its influence on ash characteristics, Progress in Energy and Combustion Science 28(3): 267-299. Soewono, A. (2008). Morphology and microstructure of diesel particulates. Soewono, A., Rogak, S. N. (2009). Morphology and Microstructure of Engine-Emitted Particulates, SAE Technical Paper. Su, D., Müller, J., Jentoft, R., Rothe, D., Jacob, E., Schlögl, R. (2004). Fullerene-Like Soot from EuroIV Diesel Engine: Consequences for Catalytic Automotive Pollution Control, Topics in Catalysis 30/31: 241-245. Subramanian, R., Khlystov, A. Y., Cabada, J. C., Robinson, A. L. (2004). Positive and Negative Artifacts in Particulate Organic Carbon Measurements with Denuded and Undenuded Sampler Configurations: Special Issue of Aerosol Science and Technology on Findings from the Fine Particulate Matter Supersites Program, Aerosol Sci. Technol. 38(12 supp 1): 27. Suzuki, S., Kuwana, K., Dobashi, R. (2009). Effect of particle morphology on thermophoretic velocity of aggregated soot particles, Int. J. Heat Mass Tran. 52(21-22): 4695-4700. Ted Pella,Inc. (2011). Substrates, Support Film Grids for Transmission Electron Microscopy [WWW Document]Ted Pella Inc.. URL http://www.tedpella.com/supflm_html/suptfilm.htm Vander Wal, R. L., Ticich, T. M., Curtis, V. E. (2000). Flame Synthesis of Metal-Catalyzed Single-Wall Carbon Nanotubes, J. Phys. Chem. A 104(31): 7209-7217. Wen, J. Z., Richter, H., Green, W., Howard, J., Treska, M., Jardim, P., Vander Sande, J. B. (2008). Experimental study of catalyst nanoparticle and single walled carbon nanotube formation in a controlled premixed combustion, J. Mater. Chem. 18: 1561– 103  1569. Wu, J., Houston, D., Lurmann, F., Ong, P., Winer, A. (2009). Exposure of PM2.5 and EC from diesel and gasoline vehicles in communities near the Ports of Los Angeles and Long Beach, California, Atmos. Environ. 43(12): 1962-1971. Zheng, F. (2002). Thermophoresis of spherical and non-spherical particles: a review of theories and experiments, Adv. Colloid Interfac. 97(1-3): 253-276. Zurita-Gotor, M. (2006). Size- and structure-independence of the thermophoretic transport of an aerosol particle for specular boundary conditions in the free molecule regime, J. Aerosol Sci. 37(3): 283-291.  104  Appendix A: Scaled drawing of the TPS  Figure 38: SolidWorks drawing of the TPS  105  Appendix B: Additional graphs for Indian ink particle distribution  Figure 39: E6 particle count map  106  Figure 40: E6 particle deposition efficiency map  107  Figure 41: E7 particle count map  108  Figure 42: E7 particle deposition efficiency  109  Figure 43: E8 particle count map  110  Figure 44: E8 particle deposition efficiency  111  Appendix C: Observations of grid degradation During TEM analysis of the IARP grids, many of the TEM grids showed significant degradation in the form of holes and broken and peeled carbon film.  When imaging T31, and one of the  spherical particles, which was sitting on an intact piece of carbon film, began to evaporate quickly. Immediately, the film underneath broke apart, and quickly turned into a large split in the previously undamaged film. In addition, I had noticed that most of the split film often has large, dark blob curled up in the film. It appears that the spherical and "other" particles, are causing the film to weaken and split.  112  Appendix D: Fullerenes in compression ignition engine PM The morphology of PM from of diesel and natural gas fuels in compression ignition engines has been studied at length (3). A review of the original dataset from this research consisting of over 600 particles revealed only two particles that that are suspected to be fullerene structures. Both were identified by the characteristic hollow core typically seen in TEM images of carbon nanotubes or fullerene structures. However, the low number of these fullerene-like particles (2 of 600) may indicate that either the conditions for fullerene growth were transient or rare, or that these image are not, in fact, fullerene structures. In addition, the absence of other forms of fullerenes or nanotubes in this large data set in comparison with the IARP dataset, leads to this conclusion as well.  Possible fullerene Soot  Figure 45: Possible fullerene particles from a diesel engine (courtesy of Arka Soewono)  113  Appendix E: Nu number calculations The following equation was used to find the average Nu number in the in Chapter 2 (Incropera and DeWitt 2002).  Nu _ ave = G * (2 * Re 0.5 * (1 + 0.005 * Re 0.55 ) 0.5 ) * Pr 0.42  Where the variables are defined as follows: Tube _ ID 2  Ar =   Stage _ diam  4*  2   (  )  G = 2 Ar 0.5 * (  Pr =  2  (1 − 2.2 * Ar 0.5 )   H  1 + 0.2 *  − 6* Ar 0.5  d     c p * ka v  114  Appendix F: Carbon grid contamination When using amorphous carbon based TEM grids, there is the risk of contamination of carbon nanotubes and fullerenes that were formed during the electro-deposition manufacturing process of the grid film. Clearly, it is problematic to determine if the nanotubes being studied are from the emissions source or native to the TEM film. There are several papers investigating the frequency of native fullerene structures to carbon TEM grids (Harris 2001; Klie et al. 2004). Klie et. al confirmed previous observations by Harris et. al that amorphous carbon film could contain fullerenes and carbon tube structures and could be mistaken for particles in a sample. Through comprehensive surveys of three types of commercially and laboratory manufactured TEM films, Klie determined that carbon nanotubes or fullerenes contamination occurred at a frequency of three particles per grid square (Klie et al. 2004). Based on this estimate, the expected number of particle frequency background noise for the nine grid squares surveyed for this IARP study would be 27 CNT/FULs. Tables 10 and 11 show a summary of the imaged area, CNT/FUL particles counts and estimated number of CNT/FUL for nine full grid squares. Note that the CNT/FUL count listed in the second column of each table corresponds to imaged area (column 3); the estimated number of CNT/FULs is the expected number based on the assumption that CNT/FUL frequencies are consistent throughout the grid square.  115  Table 10: Estimates of total grid square CNT/FUL counts for IARP Test 07 Grid Location Points  CNT/FUL count  Imaged Area [um^2]  % Imaged Area/square  1 2 3 4 5 6 7 8 9 Total  5 0 0 30 6 11 0 0 9 61  131.2 78.7 78.7 3112.1 131.2 145.9 78.7 52.5 114.4  1.5 0.9 0.9 34.7 1.5 1.6 0.9 0.6 1.3  Estimated # CNT/FUL per square 342 0 0 87 410 677 0 0 706 2221  Table 11: Estimate of total grid square CNT/FUL counts for IARP Test 22 Grid Location Points  CNT/FUL count  Imaged Area [um^2]  % Imaged Area/square  1 2 3 4 5 6 7 8 9 Total  0 1 2 6 1 2 2 4 1 19  242.5 446.1 144.8 207.8 152.2 209.9 307.5 490.2 266.6  2.7 5.0 1.6 2.3 1.7 2.3 3.4 5.5 3.0  Estimated # CNT/FUL per square 0 20 124 259 59 85 58 73 34 713  The estimated number of carbon nanotubes from these nine grids squares in the IARP samples is typically one or two orders of magnitude higher than the background noise indicating that the majority of the CNT/FULs in this study were from a particle source other then the TEM film.  116  Appendix G: Emissions for full IARP vehicle set Emissions for the full 30-vehicle IARP fleet are provided here to set the context for the small test set that underwent TPS sampling. For each vehicle tested in the IARP, fuel-based emission factors were determined for gaseous pollutants, PM2.5, organic carbon (OC) and elemental carbon (EC). Table A1 gives the drive cycle average emission factors (mean, 95% confidence interval) for each fuel-technology class, and disaggregated by age-group for the vehicles with four-stroke engines. Inter-vehicle variability in the total tested sample was high. Within the fourstroke age-groups, for most pollutants there was no significant difference (95% confidence level) between ‘old’ (1998-2001) and ‘new’ (2007-2009) vehicles (Reynolds, Grieshop, et al. 2011).  The fuel-based PM2.5 emission factor (mean, 95% confidence interval) for the two-stroke CNG vehicle group (14.2 g kg-1 [6.2-26.7]) was almost thirty times higher than for the four-stroke CNG vehicles (0.5 g kg-1 [0.3-0.9]) and twelve times higher than for four-stroke gasoline-fuelled vehicles (1.2 g kg-1 [0.8-1.7]) (Reynolds, Grieshop, et al. 2011). For the two-stroke CNG vehicles, organic matter (OM = 1.2 × organic carbon, OC) emissions account for most (>90%) of the PM2.5 emissions (Russell 2003). For the vehicle groups with four-stroke engines, PM2.5 from CNG vehicles was ~70% OM and ~20% EC, and PM2.5 from gasoline vehicles was ~60% OM and ~33% EC.  117  Table 12: Average fuel-based emission factors for all vehicles in each fuel-technology group. Four-stroke vehicles are disaggregated by age. Ranges shown are 95% confidence intervals. CNG four-stroke Gasoline four-stroke CNG two-stroke All (N=17) New (N=9) Old (N=8) All (N=11) New (N=7) Old (N=4) All (N=13) 2600 2630 2660 1580 1820 1170 1920 (2560(2510(2550(1260-1980) (1410-2300) (890-1420) (1820-2030) 2690) 2680) 2760) CO (g kg-1) 83 75 93 823 680 1073 81 (54-116) (48-106) (37-151) (630-1013) (458-863) (817-1264) (47-125) 57 (40-77) 75 (49-98) 38 (19-59) 158 (97-217) 156 (96-213) 161 (34-288) 312 (276-348) HC (g kg-1) NO (g kg-1) 22 (18-26) 26 (22-31) 18 (12-24) 9.3 (5-14) 12.7 (7-18) 3.4 (0.3-7.3) 2.6 (2-3.2) 50 (38-62) 62 (46-78) 36 (21-52) 10 (8-12) 10 (6-13) 11 (8-14) 312 (281-344) CH4 (g kg-1) 0.5 (0.30.6 (0.20.5 (0.21.2 (0.8-1.7) 1 (0.5-1.8) 1.6 (1-2) 14.2 (6.2-26.7) PM (g kg-1) 0.9) 1.2) 0.7) OC (g kg-1) 0.3 0.34 0.26 0.62 0.53 0.74 10.7 (0.14, 0.5) (0.08, 0.7) (0.12, 0.44) (0.3, 1.0) (0.16, 1.0) (0.4, 1.0) (5, 19) EC (g kg-1) 0.09 0.11 0.08 0.37 0.33 0.43 0.009 (0.05, 0.15) (0.03, 0.2) (0.04, 0.12) (0.24, 0.5) (0.18, 0.45) (0.24, 0.67) (0.001, 0.025) FC (g km-1) a 21 (20-23) 21 (19-22) 22 (21-24) 38 (34-42) 35 (31-39) 43 (36-47) 25 (24-27) a Fuel consumption (FC) estimates are based on carbon balance in the exhaust, and can be used to calculate distance-based emission factors by multiplying with the fuel-based emission factors. Emission CO2 (g kg-1)  118  Appendix H: Summary of image database for IARP samples A database was created for all images recorded in this study. Each particle was assigned a unique identification number and recorded along with the associated test number, image number, position on the TEM grid (positions 1-9), particle length and width, shape category, observation notes and primary particle size (for agglomerates only). Test-specific TPS sampling information and particle morphology statistics were also recorded in this database.  A summary of particle counts is presented in Table B1. Open agglomerates are soot aggregates with a fractal dimension of about 1.8. Here and in the main article, particle size and shape distributions are characterized by the median and interquartile range, i.e., median (25th percentile – 75th percentile). Following this convention, open agglomerates overall had a size of 386 nm (197 – 1310 nm), and were composed of primary particles with diameter of 55 nm (44 – 69 nm). Each agglomerate included 10-200 primary particles, but only counts as a single aggregate particle in the statistics reported here. At large sizes, open soot aggregates may collapse, usually due to the action of liquid coatings that would evaporate after collection. These are categorized as closed agglomerates; they had a median size of 117 nm (57 – 457 nm). The median primary particle size of the closed agglomerates (44 nm [33 – 63nm]) is close to that of open aggregates.  Spherical particles were likely to have been produced from partially pyrolized lubricating oil droplets (Miller et al. 2007), and had median diameters of 34 nm (20-101 nm). They were more prevalent in samples from CNG/4-stroke engines. Using the TPS/TEM method, it was not  119  possible to indentify the chemical composition, phase and internal structure of the spherical particles.  An ‘other’ category was used for unidentified particles which included many super-micron aggregated particles. These might have included hidden CNT/FUL particles scavenged from the emissions stream. Minerals derived from lubricating oil, wear particles from the engine, and sample contaminants could also be included in this category. Some of these particles were unstable under the electron beam during EDX, and some appeared to evaporate under the TEM.  All  TEM  images  for  this  dataset  are  publicly  available  for  viewing  at  http://sites.mech.ubc.ca/~lagallyc/. The image files are labeled with the test number as well as the TEM image number. An image file number from the Hitachi H7600 TEM such as “T31V21CNG2S003” consists of the following five parts in this order:  T31 ≡ Test number which is unique to the test V21 ≡ Vehicle number; note that some vehicles were tested more than once CNG ≡ Fuel types for this test; compressed natural gas (CNG) or gasoline (denoted P for ‘petrol’, as it is known in India) 2S ≡ Engine type 2S (two-stroke) or 4S (four-stroke) 003 ≡ Image number which is unique to this image  High resolution TEM images from the FEI Tecnai G2 have image file names such as T07_07 which consists of the test number (T07) and the HRTEM image number (07).  120  Table 13: Particle counts and shape category percentages for all morphologies for each vehicle sampled. Total Test code PM (#) CNG four-stroke engines T04V02CNG4S 106 T07V04CNG4S 335 T09V05CNG4S 39 T11V06CNG4S 64 T13V07CNG4S 29 T23V14CNG4S 148  CNT/FUL (# (%))  Open Aggl. (# (%))  Closed Aggl. (# (%))  Spherical (# (%))  Other (# (%))  5 (4.7%) 61 (18.2%) 1 (2.6%) 4 (6.3%) 0 (0%) 37 (25.0%)  6 (5.7%) 4 (1.2%) 3 (7.7%) 22 (34.4%) 6 (20.7%) 0 (0%)  51 (48.1%) 142 (42.4%) 1 (2.6%) 17 (26.6%) 7 (24.1%) 12 (8.1%)  2 (1.9%) 57 (17.0%) 14 (35.9%) 20 (31.3%) 3 (10.3%) 17 (11.5%)  42 (39.6%) 71 (21.2%) 20 (51.3%) 1 (1.6%) 13 (44.8%) 82 (55.4%)  Gasoline four-stroke engines T03V02P4S 49 T08V05P4S 111 T12V07P4S 79 T22V14P4S 132  6 (12.2%) 0 (0%) 21 (26.6%) 19 (14.4%)  11 (22.5%) 13 (11.7%) 3 (3.8%) 26 (19.7%)  16 (32.7%) 72 (64.9%) 4 (5.1%) 42 (31.8%)  7 (14.3%) 10 (9.0%) 3 (3.8%) 11 (8.3%)  9 (18.4%) 16 (14.4%) 48 (60.8%) 34 (25.8%)  CNG two-stroke engines T27V17CNG2S 281 T30V20CNG2S 311 T31V21CNG2S 437  8 (2.9%) 36 (11.6%) 36 (8.2%)  5 (1.8%) 42 (13.5%) 12 (2.8%)  170 (60.5%) 109 (35.1%) 319 (73.0%)  2 (0.7%) 32 (10.3%) 19 (4.4%)  96 (34.2%) 92 (29.6%) 51 (11.7%)  Compound particles (Figure 46), where particles of different shape category are found as one particle, were recorded as two particles in their appropriate categories, but marked as a ‘compound particle’ in the database. Only 60 particles of the 2121 recorded were compound particles.  Figure 46: Carbon nanotubes attached to other particles may be difficult to locate in heavily sooting engines such as diesel engines. Shown is a. long nanotube attached to an agglomerate of other particulate matter; scale bar is 500 nm, and b. a nanotube embedded inside another type of particulate matter (indicated by arrow); scale bar is 100 nm.  121  MWCNT with exceptionally wide diameter of 50 to 150 nm were also found (Figure 47). These partially collapsed structures have the appearance of a crumpled tube. These wide-diameter nanotubes were confirmed to be MWCNT by high-resolution TEM. A similar particle was reported in a brief paper by Evelyn et al. in 2003 that used TEM to examine PM from diesel engines (Evelyn et al. 2003).  Figure 47: Two examples of wide-diameter, multi-walled CNT. a. Scale bar is 200 nm; b. Scale bar is 100 nm.  122  Appendix I: Inter-vehicle variability The percentage of CNT/FUL varied considerably between engine tests, and we consider here whether or not the differences were statistically significant. If the true CNT/FUL fraction was actually the same for all vehicles, the observed number of CNT/FUL particles for each test would vary according to the binomial distribution. The best overall estimate of the CNT/FUL fraction based on all 2121 particle images is 10%. The hypothesis to be tested statistically is that differences from this mean fraction are due only to chance (i.e., all samples come from the same underlying population). In fact, this hypothesis must be rejected at a significance p<0.05 for 10 of 13 grids (Table 14). That is, the differences in the CNT/FUL fractions for different vehicles were statistically significant in most cases. This is not surprising because the conventional emissions (CO, NOx, PM2.5) showed enormous inter-vehicle variability, even with in a particular technology class (i.e., CNG/four-stroke). We were unable to find a clear correlation between CNT emissions and vehicle characteristics.  123  Table 14: Binomial distribution results of the hypothesis that there is a single underlying CNT/FUL fraction (10%), and that the number observed on each grid is governed by the binomial distribution. Test code  X (#) CNTs CNG four-stroke vehicles T04V02CNG4S 5 T07V04CNG4S 61 T09V05CNG4S 1 T11V06CNG4S 4 T13V07CNG4S 0 T23V14CNG4S 37  b Probability  Hypothesis Accept/Reject  106 335 39 64 29 148  0.02 0.000002 0.07 0.1 0.05 0.00000009  reject reject accept accept reject reject  Gasoline four-stroke vehicles T03V02P4S 6 T08V05P4S 0 T12V07P4S 21 T22V14P4S 19  49 111 79 132  0.2 0.000008 0.00002 0.03  accept reject reject reject  CNG two-stroke vehicles T27V17CNG2S 8 T30V20CNG2S 36 T31V21CNG2S 36  281 311 437  0.000003 0.05 0.03  reject reject reject  n Trials  124  Appendix J: Volume of elemental carbon on grids Volume and mass estimates of non-volatile PM on the TEM grids were required to estimate CNT/FUL emission factors. The following methods were used to estimate the volume of elemental carbon for each shape categories. For all categories, the mass was found by multiplying the total volume by a soot density of 2 g/cc. See Table D1 for volume values.  Fibers, fullerenes and nanotubes (CNT/FUL) Fibers, fullerenes and CNT were approximated as thin-walled tubes with an average wall thickness of 20 carbon layers (each 0.34 nm thick) using equation J1. The volume from this equation includes only the carbon material, and does not include the enclosed volume of a closed fullerene structure or nanotube. However, this has only a trivial influence on the CNT emissions factors because the CNT/FUL volumes are so small.  VCNTF  2 2 d    (d − (20 * 0.34))   = LCNT π   −  LCNTF π    2  2       (J1)  Open and closed agglomerates Open agglomerates are assumed to be made of primarily elemental carbon. Equation J2 is used to estimate the number of primary particles in the open agglomerates (Soewono 2008).  N p = k fLW   ( L * W )1/ 2  *  dp    Df  (J2)  125  N p is the number of primary particles; k fLW is the fractal pre-factor, a measure of the relationship between an agglomerate projected area and the number of primary particles (Köylü et al. 1995) taken to be 1.30 for the IARP samples; L and W are the length and width of the agglomerate; d p is the diameter of the primary particles; D f is the fractal dimension of the agglomerates (assumed to be 1.8 for open agglomerates in the IARP samples (Soewono 2008)).  The aggregate volume, Vaggl. , is estimated as:  Vaggl. = N p * v p  (J3)  where v p is the mean volume of the primary particles from which the aggregates are composed. The same approach for volume calculations was used for closed agglomerates and open agglomerates. However, the fractal dimension for closed aggregates was assumed to be 2.8; this is a plausible estimate for compact agglomerates. Although this estimate is a source of uncertainty (fractal dimensions can be as high as 3.0), the propagated error in emissions rate is minimal compared to the overall EC volume uncertainty discussed later in this section.  Spherical and other particles Spherical particles and ‘other’ particles were approximated as spheres. The RMS of the particle length and width was used as the diameter in the volume determination.  126  Table 15: Volume totals for all particles imaged. Test code PM 1) Open 2) Closed # Agglomerate Agglomerates EC EC Volume Volume (µm3) (µm3) # # CNG four-stroke vehicles T04V02CNG4S 106 6 1.33E+00 51 3.59E+01 T07V04CNG4S 335 4 1.05E-01 142 3.15E+01 T09V05CNG4S 39 3 3.04E-13 1 2.16E-01 T11V06CNG4S 64 22 6.44E-01 17 7.19E+00 T13V07CNG4S 29 6 3.41E-01 7 6.50E+02 T23V14CNG4S 148 0 0.00E+00 12 6.73E-03  3) Spherical  #  EC Volume (µm3)  2 57 14 20 3 17  4) CNT/ FUL  5) Other  #  EC Volume (µm3)  #  EC Volume (µm3)  9.33E-02 4.22E-03 2.77E-05 1.75E-04 2.10E+00 1.69E+00  5 61 1 4 0 37  5.58E-02 5.69E-03 2.33E-02 1.55E-03 0.00E+00 9.28E-03  42 71 20 1 13 82  2.72E+04 2.12E+01 7.54E-01 4.45E-04 2.23E+01 2.11E+02  Gasoline four-stroke vehicles T03V02P4S 49 11 T08V05P4S 111 13 T12V07P4S 79 3 T22V14P4S 132 26  1.71E+00 4.98E-01 6.11E-02 4.96E-02  16 72 4 42  9.95E+01 6.01E-02 4.09E-01 1.66E-01  7 10 3 11  4.09E-03 2.06E-04 1.82E-01 1.82E-01  6 0 21 19  2.68E-04 0.00E+00 1.43E-01 2.70E-02  9 16 48 34  2.17E+04 2.44E-01 5.14E+00 1.82E+01  CNG two-stroke vehicles T27V17CNG2S 281 5 T30V20CNG2S 311 42 T31V21CNG2S 437 12  7.12E-04 2.94E-02 2.53E-02  170 109 319  1.93E+02 7.23E-01 1.36E+02  2 32 19  1.91E-01 1.43E+00 1.23E-01  8 36 36  6.40E-03 9.63E-03 1.66E-02  96 92 51  2.01E+05 4.96E+01 1.32E+02  Total particles  2121  127  These estimates were based on the number of CNT/FUL particles per estimated volume of EC; determination of EC volume assumes that particles included are composed of purely carbon. However, EDX analysis showed that many of the particles in the ‘other’ category were not primarily EC, but consisted of many elements. Therefore, when calculating the EC volume in a sample, only the volumes of the CNT/FUL, agglomerates and spherical particles were included. Since the ‘other’ particles may contain EC, this is a potential source of error in the estimated CNT/FUL fuel-based emission rate. It is possible the these “other” particles are over-represented in our samples because, on average, they have a larger diameter and, hence, may have been collected on the grids by impaction rather than only thermophoresis. For comparison, the average emission rate (380 x 109 particles/kg) estimated assuming that only the CNT/FUL, agglomerate and spherical particle volumes contributed to EC is substantially higher than that estimated when particles in the “other” category are assumed to be EC (60 x 109 particles/kg). Therefore, our estimate of the CNT/FUL emission factor is highly dependent on which particles are assumed to contribute to EC particle mass measured via Thermo-optical OC/EC analysis. The influence of this uncertainty was included in our range for estimated CNT/FUL emission factor.  128  Table 16: Summary of CNT emissions based on only the volume of CNT/FUL, agglomerates and spherical particles. Test code  #CNT-FUL per volume of EC (# µm-3)  #CNT-FUL per kg fuel (# kg-1) * 109  CNG 4-stroke vehicles T04V02CNG4S T07V04CNG4S T09V05CNG4S T11V06CNG4S T13V07CNG4S T23V14CNG4S  0.13 1.93 4.17 0.51 0.00 21.70  1.05 143 263 20.7 0.00 67.3  Gasoline 4-stroke vehicles T03V02P4S T08V05P4S T12V07P4S T22V14P4S  0.06 0.00 26.40 44.80  3.56 0.00 3,220 44.0  CNG 2-stroke vehicles T27V17CNG2S T30V20CNG2S T31V21CNG2S  0.04 16.40 0.26  (no data) (no data) (no data)  8.95 0.51  376 32.3  Mean Median  129  Appendix K: SMPS profiles for the Ricardo Hydra samples Following are the SMPS profiles for the Ricardo Hydra tests listed Table 8.  Figure 48: SMPS profiles for December 2010 Ricardo Hydra samples listed in Table 8.  130  Figure 49: SMPS profiles for December 2010 Ricardo Hydra samples listed in Table 8.  131  Appendix L: Supplementary electronic files Electronic files accompanying this thesis are listed and described below.  File name and extension  Title or Description  ThermophoreticSamplerPaper_Brown Theoretical and Experimental Evaluation of a Thermophoretic Aerosol Collector IARP Database  An MS Access database of all particles found and analyzed in the IARP morphology study.  Ricardo Hydra Test data.xlsx  December 2010 sampling notes, TEM grid information, SMPS file information associated with each engine sample.  Ricardo Hydra Test Data 04Dec2010. XML  December 2010 engine and gas data from the Ricardo Hydra emissions bench and engine monitoring systems.  air-test1.S80  SMPS data file for December 2010 Ricardo Hydra samples.  132  

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.24.1-0071718/manifest

Comment

Related Items