This may be the author’s version of a work that was submitted/acceptedfor publication in the following source:

Savic, Natascha, Rahman, Md Mahmudur, Miljevic, Branka, Saathoff,Harald, Naumann, Karl-Heinz, Leisner, Thomas, Riches, Jamie, Gupta,Bharati, Motta, Nunzio, & Ristovski, Zoran(2016)Influence of biodiesel fuel composition on the morphology and microstruc-ture of particles emitted from diesel engines.Carbon, 104, pp. 179-189.

This file was downloaded from: https://eprints.qut.edu.au/94809/

c© Consult author(s) regarding copyright matters

This work is covered by copyright. Unless the document is being made available under aCreative Commons Licence, you must assume that re-use is limited to personal use andthat permission from the copyright owner must be obtained for all other uses. If the docu-ment is available under a Creative Commons License (or other specified license) then referto the Licence for details of permitted re-use. It is a condition of access that users recog-nise and abide by the legal requirements associated with these rights. If you believe thatthis work infringes copyright please provide details by email to qut.copyright@qut.edu.au

Notice: Please note that this document may not be the Version of Record(i.e. published version) of the work. Author manuscript versions (as Sub-mitted for peer review or as Accepted for publication after peer review) canbe identified by an absence of publisher branding and/or typeset appear-ance. If there is any doubt, please refer to the published source.

https://doi.org/10.1016/j.carbon.2016.03.061

1

Title: Influence of biodiesel fuel composition on the morphology 1

and microstructure of particles emitted from Diesel engines 2

N. Savica,c, M. M. Rahmana, B. Miljevica, H. Saathoffc, K. H. Naumannc, T. Leisnerc, J. Richesb, B. Guptab, 3N. Mottab, *1Z. D. Ristovskia 4

aILAQH, Queensland University of Technology, QUT, Brisbane, QLD 4001, Australia 5bCARF, Queensland University of Technology, QUT, Brisbane, QLD 4001, Australia 6

cIMK-AAF, Karlsruhe Institute of Technology, Karlsruhe, Germany 7

8

Abstract: 9

This study investigates the morphology, microstructure and surface composition of Diesel 10

engine exhaust particles. The state of agglomeration, the primary particle size and the fractal 11

dimension of exhaust particles from petroleum Diesel (petrodiesel) and biodiesel blends from 12

microalgae, cotton seed and waste cooking oil were investigated by means of high resolution 13

transmission electron microscopy. With primary particle diameters between 12-19 nm, 14

biodiesel blend primary particles are found to be smaller than petrodiesel ones (21±2 nm). 15

Also it was found that soot agglomerates from biodiesels are more compact and spherical, as 16

their fractal dimensions are higher, e.g. 2.2±0.1 for 50% algae biodiesel compared to 1.7±0.1 17

for petrodiesel. In addition, analysis of the chemical composition by means of x-ray 18

photoelectron spectroscopy revealed an up to a factor of two increased oxygen content on the 19

primary particle surface for biodiesel. The length, curvature and distance of graphene layers 20

were measured showing a greater structural disorder for biodiesel with shorter fringes of 21

higher tortuosity. This change in carbon chemistry may reflect the higher oxygen content of 22

biofuels. Overall, it seems that the oxygen content in the fuels is the underlying reason for the 23

observed morphological change in the resulting soot particles. 24

1*Corresponding author at: International Laboratory of Air Quality and Health, Queensland

University of Technology, Brisbane, QLD 4001, Australia

Phone: +61 7 3138 1129, Fax: +61 7 3138 9079, Email: z.ristovski@qut.edu.au

2

1. Introduction: 1Diesel engine exhaust particles are geometrically very complex. They are primarily composed 2

of black carbon/soot, and formed mainly during diffusion combustion in the fuel rich regime 3

of a combustion chamber [1]. Their toxicity, transport properties, optical properties, chemical 4

composition and oxidation behavior will depend on their morphology and microstructure [2-5

5]. In addition, the performance of modern after treatment devices, especially Diesel 6

particulate filters (DPF) greatly depends on the soot morphology, as the DPFs are 7

regenerated by oxidation of the soot deposited on the filter surface [6]. Therefore, details of 8

soot morphology such as aggregate size, primary particle size, inner structure and surface 9

composition, etc. have a direct effect on the reliability of the DPF, and further on the overall 10

engine performance[7]. 11

Soot particles from Diesel combustion are composed of spherical or nearly spherical primary 12

particles called spherules. These spherules undergo random collisions with each other to 13

agglomerate and form large soot aggregates (hereafter called particles). The size, structure, 14

composition and the total concentration of these aggregated particles varies among engine 15

types and their operating conditions such as load, speed, combustion temperature, injection 16

pressure etc. and fuel types [8, 9]. Sharp reduction of soot particle concentrations for biodiesel 17

fuel is well documented in the literature [10, 11]. However, the mechanism by which this 18

reduction occurs is not fully understood yet. 19

Several studies have shown morphological change in particles due to biodiesel combustion. 20

Compared to petrodiesel, soot produced from biodiesel has similar inner core and outer shell 21

structure but seems to be more reactive than petrodiesel soot [7, 12, 13], with the mechanism 22

of its high reactivity still not clearly understood. Some research proposes that biodiesel soot 23

has a more disordered and amorphous structure [14] while others show the opposite [15, 16]. 24

Lamharess et al. [7] found faster micropore development in soot from a fuel blend containing 25

30 wt% biodiesel (B30) soot during oxidation due to the reaction of initial oxygen groups. 26

When sufficient micropore develops, the more reactive amorphous core is exposed to internal 27

burn off. Lapuerta et al. [6] reported that biodiesel soot is more easily oxidised, but at the 28

same time biodiesel soot displayed more ordered graphite-like structures and lower 29

amorphous carbon concentration, and a higher degree of graphitization. Merchant-Merchant 30

et al found that the soot primary particles obtained with biodiesel fuel were significantly 31

smaller [16], and therefore had higher specific active surface, than those of petrodiesel soot. 32

So the higher curvature of the carbon fringes would increase the probability of collapsing into 33

3

smaller fringes, enabling exfoliation, layer stripping and further oxidation, despite their higher 1

initial degree of graphitization. There are also reports of greater tortuosity and more surface 2

oxygen functional groups in biodiesel soot which might be the cause of its higher reactivity 3

[17]. 4

Variations in PM emissions among different biodiesel feedstocks are also reported in the 5

literature[18]. Rahman et al. [10] found that variations in biodiesel fatty acid methyl ester 6

(FAME) composition are the underlying reason for their variations in PM emissions. 7

Recently, microalgae has gained significant attention as a biodiesel feedstock mainly as it 8

presents a non-edible feedstock and hence minimises the damage to the ecosystem and food 9

chain supply [8], [9], [10]. Furthermore, it has a rapid growth rates and is among the most 10

photosynthetically efficient plants on Earth resulting in biomass doubling within 24 hours 11

[10]. FAME composition of microalgae biodiesel also varies among different species. 12

Therefore it is of great importance to investigate and compare microalgae biodiesel PM 13

emissions with other biodiesels and within that, how the difference in biodiesel feedstocks 14

affects soot morphology and microstructure. 15

The overall purpose of this study is to improve the scientific knowledge on soot particulates 16

emitted from Diesel engines fuelled with different biodiesel mixing ratio and type. In 17

particular, the effect of biodiesel on the soot morphology, such as aggregate morphology, size 18

and microstructure of primary particles, as well as their elemental composition is investigated 19

by means of Transmission Electron Microscopy (TEM) and X-ray Photoelectron 20

Spectroscopy (XPS). TEM enables to characterize soot aggregates for their projected 21

aggregate dimensions, fractal dimension and primary particle size distributions [8, 19, 20]. 22

High resolution TEM provides detail information about primary particle microstructure such 23

as fringe length, tortuosity and separation distance [19, 21]. In addition, XPS provides 24

information regarding the surface functional groups on the soot surface. Therefore, a 25

combination of TEM and XPS analysis enables to obtain a more detailed insight of particle 26

morphological changes due to use of biodiesel, and how those changes in morphology depend 27

on different biodiesel feedstocks as well as blend percentages. Moreover, this study used 28

microalgal biodiesel with distinctive FAME composition to investigate the change in detail 29

soot morphology due to change in biodiesel composition. 30

However, it is worth mentioning here that in-cylinder combustion parameters, i.e. ignition 31

delay, rate of combustion, heat release rate, duration of premixed and diffusion combustion 32

4

etc, have a certain influence on soot morphology. Detail engine performance and combustion 1

analysis from the same experimental work reported here have already been published [22]. A 2

closer look at Islam at el [22] reveals almost a similar rate of in cylinder pressure rise, heat 3

release rate for diesel and biodiesel blends. Hence, it would be justifiable to assume that this 4

whole experimental work is not revealing any noticeable correlation between soot 5

morphology and in cylinder combustion parameters. Probably a more robust and sophisticated 6

study is required to reveal any such relationship. Therefore, authors refrained to include any 7

in-cylinder combustion diagnosis results into this manuscript, and focus on the relation 8

between soot morphology and fuel composition and properties. 9

2. Methodology 10

2.1. Experimental Set‐up 11A four cylinder in line, common rail, turbocharged Euro IV car engine (without DPF) 12

equipped with a Froude Holfmann AG150 eddy current dynamometer was used as a test bed 13

for the experiments. A two-stage unheated dilution system, with two ejector diluters (Dekati) 14

connected in series, was used to dilute the engine exhaust with HEPA filtered air prior to 15

sampling. Details of the sampling and dilution condition can be found in Rahman et al[23], 16

and also given in the Supplimentary Information (figure SI-1) for reader conveniency. Once 17

cooled and diluted to atmospheric conditions, the exhaust from the engine was introduced to 18

the instruments. A TSI 3089 Nanometer Aerosol Sampler (NAS) was used to collect particles 19

on Transmission Electron Microscope (TEM) grids for later morphological and 20

microstructural analysis and on silicon wafers for subsequent chemical analysis by X-ray 21

Photoelectron Spectroscopy (XPS). 22

2.2. Sample collection 23Soot samples were collected on TEM grid and silicone wafer from diluted engine exhaust 24

using a TSI 3089 nanosampler. Details of the sampling and dilution condition can be found in 25

Rahman et al.[23]. Sixteen samples of Microalgae, Cotton Seed Oil (CSO) and Waste 26

Cooking Oil (WCO) biodiesels were collected with varying sampling durations from 5 up to 27

25 minutes in steps of 5 minutes and examined immediately for sufficient particle load. For 28

most conditons a sampling time of 5 minutes resulted in appropriate particle concentrations 29

on the grids for TEM analysis. Table 1 shows the biodiesel type and their blends used to run 30

the engine, and engine operating conditions during sample collection. Microalgal biodiesel, 31

derived from the dinoflagellate Crypthecodinium cohnii (Martek, Singapore) was used in 32

5

blending ratios of 5%, 20% and 50% biodiesel to petroleum diesel (v/v) (supplied by Caltex 1

Australia). A single batch of diesel was used to prepare all blends. In addition, a 20% blend of 2

waste cooking oil (WCO) and cotton seed oil (CSO) biodiesel were also used. 3

This study used microalgal biodiesel which is not commercially available, and produced in 4

laboratory scale to conduct this study. As such we did not have sufficient quantities to 5

conduct pure biodiesel tests nor at a wide range of engine operating speeds and loads. 6

Therfore, authors used up to 20% blends (vol) of microalgal biodiesel blends to check and 7

compare its performance with existing commercial biodiesel i.e. waste cook oil and cotton 8

seed oil biodiesel. B20 is also the most widely used blend in biodiesel literature, and mostly 9

recommended by many policy makers and government agencies to use in the future within the 10

existing CI engine technology. In addition, since the composition of microalagal biodiesel is 11

reasonably different than other conventional biodiesel authors used 50% blend to test its 12

performance and usability at such high blending ratio. Moreover, the maximum of torque for 13

each fuel/fuel blends is different due to their difference in heating value. Therefore, the 14

maximum torque for each fuel/fuel blends were determined at the beginning of each fuel/ fuel 15

blend experiment; while the throttle was fully open. Considering this maximum torque at full 16

throttle as 100% load, other loads i.e. 25% and 50% load were calculated. 17

18

Table1:Biodiesel type and their blends used for experimental measurement19

Fuel types Blend percentage

(vol %)

Engine

speed

(rpm)

Engine load

(% of full

load)

Engine load

(N-m)

Petrodiesel (PD) 100 2000 25, 50 63, 126

Microalgae biodiesel (MA) 5 2000 25, 50 63, 125

10 25, 50 62, 123

20 25, 50 61, 120

50 25, 50 60, 118

Cotton seed oil (CSO)

biodiesel

20 2000 25, 50 59, 120

Waste cook oil (WCO)

biodiesel

20 2000 25, 50 58, 119

20

6

In the following, the individual experiments are referenced with their acronym, followed, if 1

applicable by the blend precentage and the engine load seperated by an underscore, e.g. 2

PD_50 or CSO10_25. 3

2.3.  Transmission Electron Microscopy 4A JEOL 2100 transmission electron microscope with a LaB6 was used for his study. Images 5

were acquired on a Gatan Orius SC1000 CCD camera using Gatan Digital Micrograph 6

software. For the determination of soot morphology and microstructure overview images of 7

agglomerates were taken at 15,000 to 40,000 times magnification for TEM micrographs, 8

while higher resolution images were used to decipher primary particles and their internal 9

structure at 80,000 up to 600,000 times magnification. In total 1511 images were recorded, 10

around 50 to 100 images per sample, approximately half of lower magnification and half of 11

higher magnification. Authors made sure to not include agglomerated but single particles into 12

their analysis. The resolution of the TEM used was more than sufficient to enable the authors 13

to distinguish between agglomerated particles and single particles. Only single particles were 14

taken into account for analysis. 15

The aggregate size was calculated from TEM images using ImageJ, which is considered to be 16

an appropriate tool for TEM image analyses [24, 25].The aggregate fractal dimension was 17

also calculated from TEM image using a method proposed by Brasil et al. [26], Here, the 18

actual three-dimensional aggregate sizes and morphologies are recovered from two-19

dimensional TEM images. In particular, the number of spherules within the aggregate can be 20

determined from an empirical correlation which was derived by Monte Carlo simulations of 21

primary particle agglomeration [26-28].Using the approach of Brasil et al. [26], the number of 22

primary particles N, their mean primary particle diameter D, and the projected maximum 23

length L of the entire agglomerate were identified for each of them. A straight line arises from 24

a double-logarithmic particle number (ln N) and quotient of the maximum length and the 25

primary particle diameter (ln L/Dp) which originally results from 26

The fractal dimension DF was derived from the slope. kL represents the fractal pre-factor and 27

is the intercept. The above analysis may yield any fractal dimension in the range of 1-3. 28

For the inner structure analysis, primary particles from different aggregates at several 29

locations on the TEM grids were singled out and studied. Overview images of primary 30

7

particles were taken at 200kX to 600kX magnifications. The images were then processed 1

accordingly to extract information regarding their inner structure i.e. carbon fringe length, 2

fringe tortuosity and fringe separation distance etc. Details about the image processing 3

procedure are given at section 3.2.2 and Supporting Information (SI). 4

2.4. X‐ray Photoelectron Spectroscopy 5X-ray photoelectron spectroscopy was used to identify elements and their bonding states 6

present on the surface of PM. Measurements were carried in an Omicron Multiscan Lab 7

(Omicron Nanotechnology) at a pressure better than 2×10–10 mbar, where x-rays are generated 8

by a non-monochromatic Mg-K (1253.6 eV) x-ray source (DAS 400, Omicron 9

Nanotechnology), operated at 300 W, incident angle at 65° to the sample surface and 10

electrons are collected by a 125 mm hemispherical electron energy analyser (Sphera II, 7 11

channels detector, Omicron Nanotechnology). Survey scans were taken at an analyser pass 12

energy of 50 eV and high resolution scans at 20 eV. The survey scans were carried out with 13

0.5 eV steps and a dwell time of 0.2 s, whereas high resolution scans were run with 0.2 eV 14

steps and 0.2 s dwell time. XPS peaks were analysed by using the CasaXPS ™ software [29]. 15

3. Results and Discussion 16

3.1. Results of the TEM analysis 17

3.1.1. Aggregate Size and Fractal Dimension 18The sizes of all aggregates investigated ranged between 55±2.2 nm and 115±4.2 nm. 19

Approximately 100-300 aggregates per fuel composition and engine load were analyzed to get 20

the aggregate size mentioned in Table 2. In general we found smaller aggregates for higher 21

biodiesel fractions. For microalgal biodiesel blends, the aggregate size reduced with the 22

increase of bidiesel content in the blends at 25% engine load. For 50% engine load, MA 23

B20_L50 aggregates show a mean size of 98±2.3 nm and MA B50_L50 aggregates sized 24

60±1.8 nm. However, no conclusive monotonic relation can be derived, since MA B0_L50 25

and MA B5_L50 produced aggregates with a mean size of 57.6±4 and 72±2.8 nm 26

respectively. The smallest particles observed at half engine load were for CSO B20_L50 27

55±2.2 nm. In addition, the engine load was found to significantly affect the aggregate size as 28

well. Higher loads are more likely to produce smaller particles. In summary, it can be said 29

that biodiesel and petrodiesel fuels are producing particles in a similar size range with a slight 30

tendency to smaller aggregates for biodiesel at 25% engine load. This is consistent with 31

previous literature [10, 30, 31]. Aggregates electrical mobility size distribution measured by 32

8

DMS500 from the same experimental campaign can be found in Rahman et al[23]. The 1

difference between TEM and DMS500 measured aggregate mean sizes for few blends can be 2

explained by the presence of a nucleation mode peak in the DMS 500 measurements. 3

More pronounced differences were found for the fractal dimension of the soot particles. The 4

fractal dimension of biodiesel soot determined by the Brasil method [26] ranges between 5

1.80±0.1 (MA B5_L50) and 2.18±0.1 (MA B50_L50). The petrodiesel are within a range of 6

1.71±0.1 to 1.74±0.1. These values are in excellent agreement with recent TEM soot studies 7

emitted from Diesel engines [25, 31] 8

The fractal dimensions show a marked trend to increase with the increase of biodiesel content 9

in the blends. This is consistent with previous research [25, 32]. In addition, DF calculated 10

using the Brasil method showed a smaller spread of values for the fractal dimension. The 11

highest value for the fractal dimension obtained is DF=2.12 for MA B50_L50. Hence, the 12

results show that soot particles can exhibit a round and compact structure but cannot be 13

considered as spherical. The average values deduced from TEM image analysis for 14

petrodiesel and different blends of biodiesel are listed in Table 2 and compared to values from 15

literature. As different feedstocks of biodiesels did not show significant distinctions in their 16

fractal dimensions they are summarized in their blends. 17

Table 2: Comparison of particle fractal dimension DF and primary particle size between 18petrodiesel and biodiesel blends 19

Fueltype Biodiesel

Blend

(vol%)

Engine

Load

(%)

Aggregate

size(nm)

DF(Brasil) DF

Literature

Primary

particle

size(nm)

Petrodiesel 0

0

25

50

115.3±4.2

57.6±4.0

1.72±0.09

1.74±0.11

1.67‐1.83

[28]

1.7‐1.9[33]

1.69‐1.72

[25]

18.3±1.2

21.3±1.9

Microalgae 5

5

20

20

50

50

25

50

25

50

25

50

80.5±2.8

71.8±2.8

71.1±6.2

97.6±2.2

69.7±3.9

59.8±1.8

1.81±0.07

1.80±0.10

1.85±0.05

1.85±0.12

2.02±0.06

2.12±0.10

25.6±2.2

18.9±0.5

17.8±0.8

13.1±0.8

14.3±3.1

12.4±0.5

CSO 20 25 97.9±2.2 1.94±0.08 13.4±0.4

9

20 50 55.1±2.2 1.91±0.08 15.1±1.1

WCO 20

20

25

50

88.1±4.5

79.4±2.3

1.86±0.08

1.82±0.08

14.1±1.0

14.4±1.0

The standard errors of aggregates and primary particles size were calculated from their Gaussian fit by using FWHM=2.335.

3.2. Results of the HRTEM analysis 1

3.2.1. Primary Particle Size 2To obtain the primary particle diameter 200 to 400 primary particles of each fuel composition 3

across all engine loads were chosen randomly from different parts of the TEM grids in high-4

magnification images (80 000 to 400 000 times for 50 to 10 nm resolution). Outliers near the 5

edges of the grid were rejected manually. This number of particles was considered to be 6

sufficient to provide reasonable statistics [20]. In order to determine the size of the primary 7

particles, an ellipse was fitted to their shape using the image processing software. The longer 8

axis of the fitted ellipse is considered as the diameter of the primary particle. Hence it needs 9

to be pointed out that the actual diameter of the primary particles will be slightly smaller than 10

the reported calculated primary particle size of the tested fuels. But it will definitely not 11

impact the overall trend. The average size of the primary particles and its standard12

deviationisgiveninTable2forallexperiments. The average primary particle diameter of 13

885 petrodiesel particles was determined as Dp=21±2 nm at 50% engine load, which is 14

comparable with published data ranging from 20 to 35 nm [34, 35]. Blends with 20 % 15

biodiesel had an average soot primary particle diameter of Dp=15.2±1.0 nm for Microalgae, 16

CSO and WCO fuel. However, primary particles from microalgae fuel are more likely to be 17

larger than those of CSO and WCO, whereas CSO and WCO show similar primary particle 18

sizes. Soot particles from MA B50_L50 have the smallest primary particles among all 19

measurements. Their diameter was determined to Dp=12.4±0.5 nm. According to the obtained 20

results biodiesel is more likely to produce smaller primary particles which are consistent with 21

previous research [34, 36, 37]. However, it is worth mentioning that the mean primary particle 22

sizes obtained in this study are smaller than that of other reported studies in the literature 23

regardless of fuel types. 24

10

1

Figure1: Primary particle size distributions of petrodiesel and biodiesel blends. 2

To obtain this primary particle size distribution, Histograms of primary particles were3

created for each fuel/fuel blends, and were transformed into a log normal size4

distribution tobe able to fitwith aGaussiandistribution.Figure SI‐2 represents such5

fittingprocedureforonefuelasanexample(AlgaeB5). Figure 1 depicts primary particle 6

size distribution of petrodiesel and used biodiesel blends for different engine loads. The 7

primary particle sizes become smaller with increasing biodiesel content. This may be related 8

to the fact that biodiesel contains more oxygen than petrodiesel fuel. Figure 2 shows the 9

moderate correlation found between the primary particle size and the oxygen content in the 10

fuels at 50% engine load (R2=0.75). However, this correlation becomes weaker when 11

considering 25% load data (R2=0.38). Therefore, further investigation is required to prove this 12

hypothesis. The oxygen content was calculated from biodiesel FAME composition. 13

14

11

Figure 2: Primary particle size dependance on the oxygen content in the fuel mix at 50% 1engine load. 2

3.2.2. Primary Particle Microstructure 3For the inner structure analysis, overview images of primary particles were taken at 200kX to 4

600kX magnifications. Figure 3 shows examples of the original grayscale HRTEM images of 5

particle from petrodiesel L50, Microalgae B5_L50, B20_L50 and B50_L50 and CSO 6

B20_L50 and WCO B20_L50 along with a selected binary section of the same image. Dueto7

coalescencetendencyofsoot,mostoftheprimaryparticleswerepartlyoverlapedwith8

its neighbouring primary particles.However, images with independent primary particles 9

were found and then thresholded to be able to clearly depict the morphology of the graphene 10

sheets in the soot particles. Details of the image preparationandbinarizationcanbefound11

inSupplimentaryInformation(SI). It is important to mention that the thickness of the lines 12

does not correspond to the thickness of the actual fringes. By only looking at the six examples 13

some common and some differentiation characteristics can be recognized. Both pure 14

petrodiesel soot particles and those from blended fuel consist of two different parts with 15

different degrees of structural order. The inner part of the spherules is characterised not by 16

graphene layers but by several sections forming crystallites with locally regular arrangements. 17

A general structure of those crystallites could not be detected. This part of the primary particle 18

is commonly referred to as the amorphous core [38]. The outer portion can be distinguished 19

from the inner one by numerous distinct graphene lamellae, also called fringes. For both, 20

petrodiesel and biodiesel the core consistently appears to make up approximately 1/5 of the 21

entire particle volume [38]. This is clearly displayed in Figure 3e where a processed section of 22

an almost complete primary particle is illustrated. 23

(a) (b)

12

Figure 3:HRTEM images of particles from: (a) Petrodiesel L50, (b) Microalgae B5_L50 and 1(c) Microalgae B50_L50, (d) Microalgae B20_L50 (e) CSO B20_L50 and (f) WCO B20_L50 2with 10 nm resolution and with a selected binary section of the same image at 5 nm resolution 3

4

Compared to petro diesel the biodiesel particles have fringes showing a series of defects. This 5

can be most clearly seen in Figure 3(c) for the fuel composition with the highest biodiesel 6

percentage, namely Microalgae B50_L50. Petrodiesel soot particles show fringes (Figure 7

3(a)) that seem to be larger, less curved, and the distance between them appears to be shorter. 8

A higher percentage of crystallites as well as a higher number of well-ordered graphene layers 9

are more likely to occur for pure petrodiesel fuel. In contrast, biofuel soot particles show 10

fringes appearing more distorted, they have higher curvage and are found at larger distances 11

from each other. For a direct comparison of biodiesel- and petrodiesel-derived soot, 12

quantification of the length and degree of graphene curvature listed as tortuosity is necessary. 13

With the help of the “AnalyzeSkeletons” plugin of ImageJ, the fringe length, tortuosity and 14

separation was calculated. For this the image was binarized and converted into a skeletonized 15

image. Skeletonization is a procedure which repeatably removes pixels from the edges of an 16

object in a binary image until they are reduced to single-pixel wide shapes. This kind of 17

microstructure analysis process has been applied by several authors [39, 40]. The skeletonized 18

image is converted into a colour photograph in order to display the voxel and pixel 19

classification of the skeleton image. With the voxels representing the fringes it is possible to 20

characterize the graphene layers by their length, tortuosity and separation distance. 21

(e) (f)

(d)(c)

13

3.2.3. Fringe Length and Fringe Tortuosity 1For fringe length, tortuisity and separation distance analysis, 50 images were taken. out of 2

those, 600 fringes were chosen for each characteristic (for length and toruosity, for the 3

distance 200 fringes were established). For this 50-100 particles were analysed, also again 50-4

100 particles per characteristic.The results of the measurements and calculations of the fringe 5

lengths of particles obtained from petrodiesel and all biodiesel blends when operating the 6

engine at half load are illustrated in Figure 4. Fringes which were shorter than 0.5 nm have 7

been separated from clearly recognizable fringes as smaller objects by having no sufficient 8

features to distinguish them from noisy structures [39]. There is no accepted value of the 9

minimum size of a structure to qualify as a fringe. However, values between 0.2 to 0.5 nm are 10

found in the literature. Rouzaud and Clinard [39] considered fringes shorter than 0.246 as the 11

size of a single aromatic ring and removed from the count. Shim et al. [40] measured 12

crystallite sizes by X-ray diffraction and decided to use 1.5 nm as the minimum length. 13

Vander Wal et al. [41] chose to use a minimum length of 0.4 nm. In our work we choose to 14

select a minimum fringe length of 0.5 nm, giving rise to the sharp onset of our fringe size 15

distribution function at that value. We find the mean fringe length (±standard deviation) to 16

range between 1.008±0.799 nm for microalgae B50_L50 to 1.601±1.099 nm for petrodiesel 17

L50. Samples at idling conditions, especially CSO B10 and WCO B20, show even shorter 18

fringe lengths but these conditions of incomplete combustion will not be analyzed here. The 19

largest fringe lengths were determined to range from 5 nm up to 7 nm depending on the 20

sample type. The distributions of the fringe lengths for all biodiesel soot particles except for 21

Microalgae B5_L50 appear to resemble each other by a high number of lengths below 1 nm 22

and a rapid decrease to 2 nm fringes. Just a few fringes show lengths above 2 nm. Fringe 23

lengths of Microalgae B5_L50 and petrodiesel L50 indicate similar graphene layer length 24

distributions. They appear to be significantly flatter and about 50 lamellae lengths out of 600 25

(8 %) show a length of 0.5 to 0.6 nm. In contrast, soot particles from Microalgae B50_L50 26

have around 160 fringes out of 600 (27 %) which are in the range of 0.5 to 0.6 nm. 27

14

Figure 4: Mean fringe length of (a) Petrodiesel L25, (b) Microalgae B5_L50 and (c) 1Microalgae B50_L50, (d) Microalgae B20_L25, (e) CSO B20_L25 and (f) WCO B20_L25. 2

In addition, in Figure 4 the percentage of fringes larger than 1 nm is highlighted in each 3

graph. Petrodiesel soot particles exhibits twice as many fringes longer than 1 nm as 4

Microalgae B50. The different types of biodiesel B20 show similar fringe lengths where CSO 5

appears to have the shortest fringes. This could be related to the particular chemical 6

composition (oxygen content) of the fuels. Summed up, around 40 % of biodiesel B20_L50 7

fringes appear to be longer than 1 nm. This value is slightly higher compared to the 36 % of 8

fringes larger than 1 nm produced by Microalgae B50_L50. Consequently a linear relation 9

between fringe length and biodiesel content can be assumed. It is also notable that standard 10

deviations of petrodiesel and Microalgae B5 are higher than other fuel mix. 11

(a) (b)

(d)(c)

(e) (f)

15

In addition to the fringe length, the fringe tortuosity is an important property characterizing 1

graphene layers. The tortuosity measures how curved and twisted a straight line is. Its 2

mathematical definition is the ratio of the length of the curve L to the shortest linear distance 3

between the ends of it. A tortuosity of value 1 represents a straight line. The higher the more 4

curved a fringe is. All fringe characteristics are given in Table 3. 5

Table 3: Calculated fringe length and tortuosity for soot particles from different fuels. 6

Fuel type Biodiesel

Blend

(vol%)

Engine

load

(%)

Mean fringe length

with standard

deviation

(nm)

Tortuosity Mean graphene

layer distances

with standard

deviation (nm)

Microalgae 5

5

20

20

50

50

25

50

25

50

25

50

1.319±0.879

1.430±0.966

1.182±0.816

1.008±0.772

1.151±0.677

1.080±0.753

1.100±0.211

1.110±0.182

1.171±0.251

1.175±0.053

1.182±0.232

1.235±0.436

0.393±0.070

0.395±0.049

0.369±0.044

0.424±0.053

0.433±0.051

0.415±0.051

Petrodiesel 0

0

25

50

1.601±1.099

1.588±1.531

1.072±0.142

1.116±0.152

0.351±0.051

0.353±0.040

CSO 20

20

25

50

1.204±0.850

1.128±0.779

1.271±0.399

1.235±0.319

0.429±0.048

0.397±0.055

WCO 20

20

25

50

1.185±0.878

1.375±1.033

1.194±0.341

1.225±0.304

0.367±0.047

0.388±0.050

7

For each sample, more than 600 fringes were analyzed and for about 200 of them a tortuosity 8

value could be obtained. This success rate was noticeably higher for petrodiesel particles. The 9

highest fringe tortuosities are found for B20 at all loads of CSO and WCO ranging between 10

1.19±0.30 and 1.27±0.40. The considerably higher tortuosity for both compared to 11

Microalgae B20 are referred to their slightly different fuel properties which are give by Islam 12

et al. [22]. No dependence of the fringe tortuosity on the load can be seen. The fringe 13

tortuosity of particles from microalgae fuels shows a clear increase with the biofuel content 14

(cf. Table 3). Looking at the standard deviation Microalgae B50 shows the highest. This can 15

16

be explained by a few but significantly more curved fringes with =4.659 as the highest value. 1

Petrodiesel particles have almost straight fringes. 2

There are also evidences of shorter fringe length and higher tortuosity for biodiesel soot in the 3

literature [37, 39, 40]. Yehliu et al[41] also found shorter fringe length and higher fringe 4

tortuosity for biodiesel soot than diesel soot from both HRTEM and x-ray diffraction (XRD) 5

analysis. However, Lapuerta et al[6] reported larger fringe size for biodiesel soot than diesel 6

soot at high engine load from XRD analysis. Some studies also suggest no significant 7

difference in soot particle nanostructure produced from pure hydrocarbon and oxygenated 8

hydrocarbon fuels [42, 43]. 9

3.2.4. Fringe Separation Distance 10Around 200 results of each sample were used for the separation distance analysis. All fringe 11

pairs having distances greater than 0.5 nm or less than 0.2 nm were classified as unphysical 12

and excluded from the analysis. This limiting of fringe distances are based on the fact that the 13

interlayer spacing of turbostratic graphite (0.344 nm, see [44]) and graphitic layer distances 14

fall within that range. This was confirmed in several previous studies [8, 39]. Up to 30 % of 15

unsuitable data had to be removed for some blends of biodiesel whereas the exclusion rate 16

was below 10 % for pure petrodiesel. 17

As shown in Table 3, the shortest mean fringe separation distance of 0.35±0.05 nm is seen 18

for particles from neat petrodiesel. It is also of importance that distances of fringes from 19

petrodiesel do not significantly differ from each other after sorting out too short and long 20

fringes. It shows that there are only few artifacts compared to fringes from biodiesel 21

compositions. In addition to that it seems to be the closest value to the graphene layer 22

distances of graphite. Microalgae B50_L25 presents the longest mean graphene plane distance 23

with a separation of 0.433±0.05 nm. While there is no significant correlation of the fringe 24

separation distance with the engine load, there is with the biofuel content. Biodiesel blends, 25

especially the ones with a higher percentage of biodiesel, are more likely to be composed of 26

primary particles with a wider graphene layer distance within their inner structure. Yehliu et 27

al [41] also reported slightly higher graphene layer separation distance in biodiesel soot than 28

diesel, where Lapuerta et al[6] reported lower interplanar layer spacing of the graphitic lattice 29

for biodiesel soot than diesel soot. There are other studies which also coincide with the 30

findings of this study [39, 40]. 31

17

3.3. Results of the XPS analysis 1

3.3.1. Qualitative and Quantitative Elemental Analysis 2Distinct elements in soot were identified by means of x-ray photoelectron spectroscopy in a 3

XPS survey scan over a broad range of ejected core-shell electrons energies. The three 4

elements which were found on every soot surface independent from the fuel were oxygen, 5

carbon and silicon. Silicon is detected since the soot particles were gathered on silicon wafers. 6

Consequently, it was important to obtain silicon peaks as it ensured an exact placement of the 7

beam onto the silicon wafer. Peaks not originating from silicon were identified as O1s 8

(around 512 eV) and C1s (around 284 eV), providing chemical information about the actual 9

components of soot. As a rough estimate of the sooth composition, the oxygen to carbon ratio 10

for each fuel at half load has been calculated. The ratios of oxygen to carbon on particle 11

surface for Microalgae B50 and B5 were ROC=6.64 and ROC=2.89 with an accuracy of 10 %. 12

These values are listed in Table 4, along the calculated ratios for all fuels at half load. 13

Table 4: Oxygen to carbon ratio (ROC) on the surface of particles from petrodiesel and 14biodiesel blends 15

Fuel and blend Ratio of oxygen to carbon

(ROC) on soot surface

Petrodiesel_L50 3.21±0.50

Microalgae B5_L50 2.89±0.40

Microalgae B20_L50 4.41±0.62

CSO B20_L50 4.63±0.45

WCO B20_L50 3.19±0.65

Microalgae B50_L50 6.64±0.94

16

For Microalgae B50 oxygen to carbon ratio is more than twice as big as for Microalgae B5 17

resulting in a much higher oxygen concentration on the surface of the particles for the fuel 18

composition with the highest percentage of biodiesel. It shows more than six times larger 19

oxygen amount compared to carbon. Petrodiesel, Microalgae B5 and WCO B20 were found 20

to have the smallest amount of oxygen compared to the other fuels. The amount of oxygen 21

appears to be triple the amount of carbon. In contrast, Microalgae B20 and CSO B20 have 22

values of four times larger surface oxygen content compared to the carbon content. 23

Consequently, it appears that more biodiesel in the fuel leads to a higher oxygen content on 24

the surface. 25

18

3.3.2. Chemical Structure Analysis 1Functional group surface chemistry is complementary to elemental identification. These 2

measures produce a recognizable chemical and physical fingerprint of soot. As applied to 3

partially oxidized carbon, such as soot within combustion exhaust, it supports a variety of 4

oxygen functional species within the C 1s peak region, namely carbonyl (-C=O) and 5

carboxylic (-COOH) groups in addition to the carbon fine structure[45]. Components of the 6

C1s peak also include hybridized carbon single bonds such as C-C sp2 and C-C sp3 structures. 7

These groups are related to distinct bonding states of carbon produced during its 8

formation[46]. The C 1s envelopes were fitted using mixed Gaussian-Lorentzian component 9

profiles after subtraction of a Shirley background using Casa XPS software. The fitting was 10

done with four peaks at all time, each peak representing carboxylic (289.6 eV), carbonyl 11

groups (287.1 eV) the sp2 hybridised C-C bond around 284 eV, and the sp3 C-C around 284.9 12

eV. The0.9shiftbetweenthesp2andsp3isbeyondthetypicalresolutionpowerofthe13

XPSandcanbeclearlyresolved. We notice that Mizokawa et al[47] found a separation of 14

0.8 eV between the positions of the C 1s XPS lines in graphite and diamond and Haerle et al. 15

found one of 0.9 eV, in good agreement with the separation of 0.9 eV obtained in this work. 16

The C-C bonds are considered to be relevant for this study since C-C sp2 is related to a 17

graphitic structure and refers to elemental carbon whereas C-C sp3 corresponds to defects 18

within the graphitic structure and exposes more edge site carbon atoms [48]. Consequently, 19

the sp3 component gives evidence of a more disordered structure which is more likely to be 20

present in organic carbon [29]. Both C-C bond components are highlighted and displayed in 21

Figure 5 for Microalgae B50 in comparison to Microalgae B20, WCO B20 and Diesel. 22

19

Figure 5: XPS analysis of C‐C bonding in particles produced from combustion of1petrodieselandbiodieselblends.Thepositionof thepeaks for theCarboxylic (‐COOH,2blue), Carbonyl (‐C=O, brown), the sp2 (red) and sp3 (green) hybridised bonds are3showninthetopleftfigure. 4

5

Microalgae B50 and Diesel show a significant difference in the ratio of sp3/sp2 C-C peak 6

components. The sp3 component of Microalgae B50 appears to be considerably more 7

pronounced than in the Diesel. The difference of both components for Microalgae B50 is 8

noticeably larger. Microalgae B20 and WCO B20 show a smaller sp3 to sp2 ratio by contrast 9

with Microalgae B50, but still higher compared to Diesel. The specific fractional contents of 10

graphitic and non-graphitic structures for each fuel and fuel composition are summarized in 11

Table 5. 12

Table 5: C-C sp3 to sp2 ratio in particles emitted from petrodiesel and used biodiesel blends at 1350% load 14

Fuel and blend Ratio C-C sp3 to sp2

Petrodiesel 1.64±0.23

Microalgae B5 2.99±0.35

Microalgae B20 3.50±0.50

CSO B20 2.51±0.35

WCO B20 2.80±0.40

Microalgae B50 5.86±0.82

15

The values obtained lead to the conclusion that particles coming from fuels with a higher 16

biodiesel fraction expose more edge site carbon atoms and consequently have a more 17

disordered structure. Therefore, it can be stated that these two types of carbon bonding are 18

20

integral to the overall soot nanostructure. The amount and spatial relationship of these 1

different hybridized components are intimately linked to the soot formation process and the 2

fuel composition itself. In addition, previous research found that a nanostructure that exposes 3

more edge site carbon atoms will be more readily oxidized and can support a higher number 4

of oxygen groups than a nanostructure exposing basal plane carbon atoms [49]. For this 5

reason we suggest that the increasing sp3 to sp2 ratio for increasing blend percentage, is 6

connected to an increasing oxygen content in the fuel. 7

3.4. Comparison of HRTEM and XPS analysis 8The microstructure of soot particles was investigated by physical as well as chemical 9

characterization. Both methodologies showed the same results. Carbon lamellae structure in 10

soot can be characterized with the help of fringe length and tortuosity measurements as 11

derived from analysis of bright field HRTEM. Those along with a measurement of fringe 12

distance can provide complementary carbon nanostructural characterization. Dependent upon 13

the initial fuel, a variation of degrees of amorphous versus graphitic structure can result. 14

Biodiesel appears to have a more amorphous structure, namely a less ordered arrangement of 15

graphene layers. Fringes of biodiesel are more likely to be shorter and more curved. Algal 16

biodiesel shows the shortest fringes whereas CSO and WCO biodiesel show the most curved 17

ones. Therein, the larger tortuosity is consistent with having a larger mean separation distance 18

between the fringes and appears to be larger for biodiesel (0.367±0.047 nm - 0.433±0.051 19

nm). Within the biodiesels, Microalgae still shows a larger mean separation distance 20

compared to CSO and WCO. The separation distance between regularly successive graphene 21

lamellae in petrodiesel soot ranges from 0.351±0.051 nm to 0.353±0.040 nm and thus is 22

closer to the 0.344 nm lattice parameter in graphite. Contingent on biodiesel blend, the fringe 23

properties changed, as well. This is the reason for a rather large range of separation distance 24

for biodiesel. A decrease of the length, an increase of the tortuosity and a resulting increase of 25

the mean separation distance was investigated for increasing blends. Different types of 26

biodiesel showed only slight differences and microalgae biodiesel did not reveal significant 27

differences to other biodiesels. They all appeared to show rather similar properties for same 28

blends. According to Lapuerta et al.[6] a higher curvature of carbon fringes would increase 29

the probability of collapsing into smaller fringes, enabling exfoliation, layer stripping and 30

further oxidation and leading to higher reactivity of the soot particulate on the surface [6]. The 31

above observations from the HRTEM-analysis in regards to their amorphous structure is 32

consistent with XPS characterization. XPS characterization revealed surface oxygen groups 33

21

such as carbonyl and carboxylic groups for both petrodiesel and biodiesel. Oxygen functional 1

groups can only be associated with sp3 carbon atoms or sp2 carbon atoms located at lamellae 2

edge sites[50]. Both carbon sp3 at approximately 285.4 eV and sp2 close to 284.5 eV generate 3

from the main carbon C 1s peak after resolving the peak. The amount and spatial relationship 4

of these different hybridized components are intimately linked to the soot formation process. 5

Along with engine conditions nascent fuel composition plays a role in determining the 6

mixture of these components contributing to the soot nanostructure[51]. According to Mueller 7

et al. it seems that the presence of reactive carbon–oxygen functional groups like C=O, C–O–8

C, or C–OH on soot samples is less important for the oxidative behavior of soot compared to 9

the nanostructural parameters of the C 1s peak[52]. A higher hybridized carbon sp3 to sp2 10

ratio for biodiesel indicates higher organic carbon content and consequently a higher 11

disordered microstructure for biodiesel. The ratio increased for increasing biodiesel blend 12

percentage. Correspondingly, lower oxygen content led to a more graphitic soot structure such 13

as in the case for petrodiesel fuel. On the basis of an amorphous structure biodiesel-derived 14

soot reveals that it is more prone to oxidation than petrodiesel soot[7, 51, 53]. The distinction 15

in oxidative behavior for biodiesel and petrodiesel is due to the composition of the fuel, which 16

results in the formation of different soot-producing species derived by fuel 17

decompositions[21]. Consequently, the oxygen content of the biodiesel molecule allows for a 18

more complete combustion. Petrodiesel particulates appeared to be larger by a quarter in the 19

mean diameter, but with a broader size distribution. Those differently sized primary particles 20

present in petrodiesel exhaust are more likely to form large and widespread agglomerates. In 21

contrast, particle agglomerates from biodiesel showed to be more spherical and compact, as 22

evident by a higher evaluated fractal dimension. Both the outer and inner structure of 23

aggregates appear to build on one another. Considering the microstructure the interplay of 24

physical structure and chemistry is subsequently punctuated with the correspondence of 25

HRTEM to XPS with each technique pointing out that biodiesel soot has a more amorphous 26

structure compared to petrodiesel soot. The sp3 to sp2 ratio derived from high resolution C 1s 27

XPS spectrum and the mean fringe tortuosity derived from high resolution TEM images were 28

compared directly. It indicates a qualitative agreement between the results from XPS and 29

HRTEM analysis, previously been discussed. The fringe tortuosity, as one of the three main 30

graphene lamellae characteristics, was chosen to be compared directly due to its direct 31

relation to the fringe itself and its increasing behavior for increasing oxygen content. Both 32

aspects can be directly correlated with the sp3 to sp2 ratio. Considering the obtained results 33

from algal biodiesel, they did not considerably differ from the other feedstocks. Microalgae 34

22

B50 indeed always presented the highest degree of disorder based on the shortest and curved 1

fringes and the largest separation distance between fringes and based on the highest sp3 to sp2 2

ratio. Moreover, it reveals the smallest aggregates as well as the smallest primary particles. 3

This was constantly caused by the highest blending ratio of Microalgae, namely B50. The 4

other feedstocks of biodiesel were not utilized blended with less than 80 % petrodiesel. 5

4. Conclusion 6Thisstudyinvestigatedthedetailchangeindieselengineexhaustparticlemorphology7

while using several blends of diesel andmicroalgal biodiesel (B05, B20, andB50), as8

well as CSO B20 and WCO B20 fuel. It reveals the morphological, structural and9

compositional differences in sootwith changing fuel compositions. Aggregates fractal10

dimension,derivedthemethodofBrasiletal.[23],appearedtoincreasefrom1.72±0.0911

(PD_L25) to2.18±0.10(MAB50_L50)with increasingbiodieselcontent.This indicates12

biodieselstoproducemoresphericalandcompactsootaggregates.Theprimaryparticle13

sizealsoreducedwithbiodieselcontentintheblendsfrom18.9±0.5nm(MAB5_L50)to14

12.4±0.5 nm (MA B5_L50). In addition, biodiesel exhibits greater structural disorder15

withparticlesbasedonshorterfringes,morecurvedfringes,andlargermeanseparation16

distance of the graphene layers. The degree of structural disorder increased with17

increasing biodiesel content in the blends, and nearly independent of biodiesel18

feedstocks. XPS analysis revealed that soot produced from fuel with higher biodiesel19

contentholdsa largersp3component,andhasahighersurfaceoxygentocarbonratio20

(ROC). Higher sp3/ sp2 and ROC gives further evidence of a more disordered21

microstructure of biodiesel particles. This change in carbon chemistry in the soot22

particlesmay reflect the higher oxygen content of biofuels. Overall, it seems that the23

oxygen content in the fuels is the underlying reason for the observedmorphological24

changeintheresultingsootparticles.25

The above changes in the microstructure of biodiesel soot, and in general in soot from 26

oxygenated fuels, can have far reaching consequences on the way these particles interact with 27

various surfaces. For example, a decrease in the primary particle size will result in an increase 28

in the total surface area available for reaction or adsorption of toxic substances. While for 29

surfaces, such as human lung cells, this could cause a larger toxicity of particles, for „active“ 30

surfaces such as those in various oxidation catalyst and active filters this could enhance the 31

oxidation of soot and improve the performance of the devices. A similar effect could also be 32

23

observed with a higher biodiesel fraction that exposes more edge site carbon atoms that will 1

be more readily oxidized. This highlights the need for further investigation of the interaction 2

of particles produced from combustion of oxygen rich fuels with both living and nonliving 3

surfaces. 4

Acknowledgements 5The authors sincerely acknowledge PhD students and the technicians in the Engine 6

Laboratory at the University of Queensland (UQ) for their valuable support during 7

experimental work. The authors also acknowledge Dr. Muhammad Aminul Islam for 8

producing microalgal biodiesel and the North Queensland Algal Identification/culturing 9

Facility (NQAIF) at JCU, for providing the microalgal biomass and access to their analytical 10

facilities. 11

References 12[1] Majewski WA. Diesel particulate matter. DieselNet Technology Guide Copyright © 13Ecopoint Inc Revision 201308b. 2013:https://www.dieselnet.com/tech/dpm.php. 14

[2] Cassee FR, Héroux ME, Gerlofs-Nijland ME, Kelly FJ. Particulate matter beyond 15mass: Recent health evidence on the role of fractions, chemical constituents and sources of 16emission. Inhalation Toxicology. 2013;25(14):802-12. 17

[3] Cheng T, Wu Y, Chen H. Effects of morphology on the radiative properties of 18internally mixed light absorbing carbon aerosols with different aging status. Optics Express. 192014;22(13):15904-17. 20

[4] Wu Y, Cheng T, Gu X, Zheng L, Chen H, Xu H. The single scattering properties of 21soot aggregates with concentric core–shell spherical monomers. Journal of Quantitative 22Spectroscopy and Radiative Transfer. 2014;135(0):9-19. 23

[5] Giechaskiel B, Alfoldy B, Drossinos Y. A metric for health effects studies of diesel 24exhaust particles. Journal of Aerosol Science. 2009;40(8):639-51. 25

[6] Lapuerta M, Oliva F, Agudelo JR, Boehman AL. Effect of fuel on the soot 26nanostructure and consequences on loading and regeneration of diesel particulate filters. 27Combustion and Flame. 2012(0). 28

[7] Lamharess N, Millet CN, Starck L, Jeudy E, Lavy J, Da Costa P. Catalysed diesel 29particulate filter: Study of the reactivity of soot arising from biodiesel combustion. Catalysis 30Today. 2011. 31

[8] Lu T, Cheung CS, Huang Z. Effects of engine operating conditions on the size and 32nanostructure of diesel particles. Journal of Aerosol Science. 2012;47:27-38. 33

24

[9] Lapuerta Mn, Ballesteros R, Martos FJ. The effect of diesel engine conditions on the 1size and morphology of soot particles. International Journal of Vehicle Design. 2009;50(1-24):91-106. 3

[10] Rahman M, Pourkhesalian A, Jahirul M, Stevanovic S, Pham P, Wang H, et al. 4Particle emissions from biodiesels with different physical properties and chemical 5composition. Fuel. 2014. 6

[11] Rahman MM, Stevanovic S, Brown RJ, Ristovski Z. Influence of Different 7Alternative Fuels on Particle Emission from a Turbocharged Common-Rail Diesel Engine. 8Procedia Engineering. 2013;56(0):381-6. 9

[12] Buono D, Senatore A, Prati MV. Particulate filter behaviour of a Diesel engine fueled 10with biodiesel. Applied Thermal Engineering. 2011(0). 11

[13] Barbier P, Fasolo B, Faucon R, Vandenplas J. A Study of the Effects of 30% Biodiesel 12Fuel on Soot Loading and Regeneration of a Catalytic DPF. SAE Technical Paper. 2007. 13

[14] Song J, Alam M, Boehman AL, Miller K. Characterization of diesel and biodiesel 14soot. ACS Division of Fuel Chemistry. Philadelphia, PA; p. 767-9. 15

[15] Song J, Alam M, Boehman AL, Kim U. Examination of the oxidation behavior of 16biodiesel soot. Combustion and Flame. 2006;146(4):589-604. 17

[16] Merchan-Merchan W, Sanmiguel SG, McCollam S. Analysis of soot particles derived 18from biodiesels and diesel fuel air-flames. Fuel. 2012(0). 19

[17] Matti Maricq M. Physical and chemical comparison of soot in hydrocarbon and 20biodiesel fuel diffusion flames: A study of model and commercial fuels. Combustion and 21Flame. 2011;158(1):105-16. 22

[18] Surawski NC, Miljevic B, Ayoko GA, Elbagir S, Stevanovic S, Fairfull-Smith KE, et 23al. Physicochemical characterization of particulate emissions from a compression ignition 24engine: The influence of biodiesel feedstock. Environmental Science and Technology. 252011;45(24):10337-43. 26

[19] La Rocca A, Bonatesta F, Fay MW, Campanella F. Characterisation of soot in oil 27from a gasoline direct injection engine using Transmission Electron Microscopy. Tribology 28International. 2015;86:77-84. 29

[20] Lapuerta M, Martos FJ, Herreros JM. Effect of engine operating conditions on the size 30of primary particles composing diesel soot agglomerates. Journal of Aerosol Science. 312007;38(4):455-66. 32

[21] Vander Wal RL, Tomasek AJ, Pamphlet MI, Taylor CD, Thompson WK. Analysis of 33HRTEM images for carbon nanostructure quantification. J Nanopart Res. 2004;6(6):555-68. 34

[22] Islam MA, Rahman MM, Heimann K, Nabi MN, Ristovski ZD, Dowell A, et al. 35Combustion analysis of microalgae methyl ester in a common rail direct injection diesel 36engine. Fuel. 2015;143(0):351-60. 37

25

[23] Rahman MM, Stevanovic S, Islam MA, Heimann K, Nabi MN, Thomas G, et al. 1Particle emissions from microalgae biodiesel combustion and their relative oxidative 2potential. Environmental Science: Processes & Impacts. 2015. 3

[24] Ouf F, Yon J, Ausset P, Coppalle A, Maille M. Influence of sampling and storage 4protocol on fractal morphology of soot studied by transmission electron microscopy. Aerosol 5Science and Technology. 2010;44(11):1005-17. 6

[25] Mustafi NN, Raine RR. Electron microscopy investigation of particulate matter from a 7dual fuel engine. Aerosol Science and Technology. 2009;43(9):951-60. 8

[26] Brasil A, Farias TL, Carvalho M. A recipe for image characterization of fractal-like 9aggregates. Journal of Aerosol Science. 1999;30(10):1379-89. 10

[27] Wozniak M, Onofri F, Barbosa S, Yon J, Mroczka J. Comparison of methods to derive 11morphological parameters of multi-fractal samples of particle aggregates from TEM images. 12Journal of Aerosol Science. 2012;47:12-26. 13

[28] Wentzel M, Gorzawski H, Naumann K-H, Saathoff H, Weinbruch S. Transmission 14electron microscopical and aerosol dynamical characterization of soot aerosols. Journal of 15Aerosol Science. 2003;34(10):1347-70. 16

[29] Estrade-Szwarckopf H. XPS photoemission in carbonaceous materials: a “defect” 17peak beside the graphitic asymmetric peak. Carbon. 2004;42(8):1713-21. 18

[30] Pham PX, Bodisco TA, Stevanovic S, Rahman MD, Wang H, Ristovski ZD, et al. 19Engine Performance Characteristics for Biodiesels of Different Degrees of Saturation and 20Carbon Chain Lengths. SAE Int J Fuels Lubr. 2013;6(1):188-98. 21

[31] Park K, Kittelson DB, McMurry PH. Structural properties of diesel exhaust particles 22measured by transmission electron microscopy (TEM): Relationships to particle mass and 23mobility. Aerosol Science and Technology. 2004;38(9):881-9. 24

[32] Tzamkiozis T, Ntziachristos L, Mamakos A, Fontaras G, Samaras Z. Aerodynamic 25and Mobility Size Distribution Measurements to Reveal Biodiesel Effects on Diesel Exhaust 26Aerosol. Aerosol Science and Technology. 2011;45(5):587-95. 27

[33] Neer A, Koylu UO. Effect of operating conditions on the size, morphology, and 28concentration of submicrometer particulates emitted from a diesel engine. Combustion and 29Flame. 2006;146(1):142-54. 30

[34] Salamanca M, Mondragón F, Agudelo JR, Benjumea P, Santamaría A. Variations in 31the chemical composition and morphology of soot induced by the unsaturation degree of 32biodiesel and a biodiesel blend. Combustion and Flame. 2012;159(3):1100-8. 33

[35] Choi S, Myung C, Park S. Review on characterization of nano-particle emissions and 34PM morphology from internal combustion engines: Part 2. International Journal of 35Automotive Technology. 2014;15(2):219-27. 36

[36] Seong H, Lee KO, Choi S, Adams C, Foster DE. Characterization of particulate 37morphology, nanostructures, and sizes in low-temperature combustion with biofuels: SAE 38Technical Paper; 2012. 39

26

[37] Man XJ, Cheung CS, Ning Z, Yung KF. Effect of Waste Cooking Oil Biodiesel on the 1Properties of Particulate from a DI Diesel Engine. Aerosol Science and Technology. 22015;49(4):199-209. 3

[38] Liati A, Spiteri A, Dimopoulos Eggenschwiler P, Vogel-Schäuble N. Microscopic 4investigation of soot and ash particulate matter derived from biofuel and diesel: implications 5for the reactivity of soot. J Nanopart Res. 2012;14(11):1-18. 6

[39] Yehliu K, Vander Wal RL, Boehman AL. Development of an HRTEM image analysis 7method to quantify carbon nanostructure. Combustion and Flame. 2011;158(9):1837-51. 8

[40] Jaramillo IC, Gaddam CK, Vander Wal RL, Huang C-H, Levinthal JD, Lighty JS. 9Soot oxidation kinetics under pressurized conditions. Combustion and Flame. 102014;161(11):2951-65. 11

[41] Yehliu K, Vander Wal RL, Armas O, Boehman AL. Impact of fuel formulation on the 12nanostructure and reactivity of diesel soot. Combustion and Flame. 2012;159(12):3597-606. 13

[42] Ruiz FA, Cadrazco M, López AF, Sanchez-Valdepeñas J, Agudelo JR. Impact of dual-14fuel combustion with n-butanol or hydrous ethanol on the oxidation reactivity and 15nanostructure of diesel particulate matter. Fuel. 2015;161:18-25. 16

[43] Zhang Y, Boehman AL. Oxidation behavior of soot generated from the combustion of 17methyl 2-butenoate in a co-flow diffusion flame. Combustion and Flame. 2013;160(1):112-9. 18

[44] Shibuta Y, Elliott JA. Interaction between two graphene sheets with a turbostratic 19orientational relationship. Chemical Physics Letters. 2011;512(4):146-50. 20

[45] Vander Wal RL, Bryg VM, Hays MD. Fingerprinting soot (towards source 21identification): Physical structure and chemical composition. Journal of Aerosol Science. 222010;41(1):108-17. 23

[46] Bryg V, Vander Wal R. Jet Soot: Chemical and Structural Characterization via XPS 24and HRTEM. Microscopy and Microanalysis. 2009;15(S2):1032-3. 25

[47] Mizokawa Y, Miyasato T, Nakamura S, Geib KM, Wilmsen CW. Comparison of the 26CKLL first-derivative auger spectra from XPS and AES using diamond, graphite, SiC and 27diamond-like-carbon films. Surface science. 1987;182(3):431-8. 28

[48] Gaudin J, Chalupský J, Toufarová M, Vyšín L, Hájková V, Sobierajski R, et al. Solid-29to-solid phase transition from amorphous carbon to graphite nanocrystal induced by intense 30femtosecond x-ray pulses. arXiv preprint arXiv:11112134. 2011. 31

[49] Frank B, Schuster ME, Schlögl R, Su DS. Emission of highly activated soot 32particulate—the other side of the coin with modern diesel engines. Angewandte Chemie 33International Edition. 2013;52(10):2673-7. 34

[50] Chiang I, Brinson B, Huang A, Willis P, Bronikowski M, Margrave J, et al. 35Purification and characterization of single-wall carbon nanotubes (SWNTs) obtained from the 36gas-phase decomposition of CO (HiPco process). The Journal of Physical Chemistry B. 372001;105(35):8297-301. 38

27

[51] Vander Wal R, Bryg V. Particle Characterization of Environmental Soots via HRTEM 1and XPS Techniques. Microscopy and Microanalysis. 2010;16(S2):1936-7. 2

[52] Müller J-O, Su DS, Jentoft RE, Wild U, Schlögl R. Diesel engine exhaust emission: 3Oxidative behavior and microstructure of black smoke soot particulate. Environ Sci Technol. 42006;40(4):1231-6. 5

[53] Song J, Alam M, BOEHMAN∗ AL. Impact of alternative fuels on soot properties and 6DPF regeneration. Combustion Science and Technology. 2007;179(9):1991-2037. 7

8

 9