The correlation between diesel soot chemical structure and reactivity

Journal Pre-proof The correlation between diesel soot chemical structure and reactivity Yi Guo, Zoran Ristovski, Elizabeth Graham, Svetlana Stevanovic, Puneet Verma, Mohammad Jafari, Branka Miljevic, Richard Brown PII:

S0008-6223(20)30068-3

DOI:

https://doi.org/10.1016/j.carbon.2020.01.061

Reference:

CARBON 14996

To appear in:

Carbon

Received Date: 23 October 2019 Revised Date:

24 December 2019

Accepted Date: 19 January 2020

Please cite this article as: Y. Guo, Z. Ristovski, E. Graham, S. Stevanovic, P. Verma, M. Jafari, B. Miljevic, R. Brown, The correlation between diesel soot chemical structure and reactivity, Carbon (2020), doi: https://doi.org/10.1016/j.carbon.2020.01.061. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Authorship contribution declaration Yi Guo, responsible for the experiment design, sampling, conducted most part of the measurements, and following data analysis and manuscript drafting. Zoran Ristovski, guided the experiment design, results discussion and manuscript drafting. Elizabeth Graham, participated part of the experiment design (soot reactivity part), measurement, and following data analysis as well as draft correction. Svetlana Stevanovic, participated in the experiment design and sampling. Puneet Verma, responsible for part of the data provision (soot nanostructure from TEM) and sample generation. Mohammad Jafari, participated in the sample generation, statistical data analysis part and draft correction. Branka Miljevic, participated in the results discussion and draft correction. Richard Brown, participated in the experiment design and draft correction.

CO2

Diesel particulate matter

Thermal-induced oxidation

Primary particle less reactive

Aromatic fuel soot

Soot fringes

Polyaromatic layer

more reactive

Oxygenated fuel soot

The Correlation Between Diesel Soot Chemical Structure and Reactivity 1,2

1,2, *

Yi Guo , Zoran Ristovski

3

1,4

,Elizabeth Graham , Svetlana Stevanovic , Puneet

1,2

2, 1

Verma , Mohammad Jafari

1,2

2,1

, Branka Miljevic , Richard Brown

1

International Laboratory of Air Quality and Health (ILAQH) – 2

Biofuel Engine Research Facility (BERF) – 3

Central Analytical Research Facility of Institute for Future Environments Queensland University of Technology, 2 George St, Brisbane City, Queensland 4000, Australia 4

School of Engineering, Deakin University, 75 Pigdons Road, Waurn Ponds, 3216 Victoria, Australia

Abstract Four types of fuels blended with diesel in scaling proportion were used in a diesel engine to generate 13 different soot samples. The samples were characterised for their thermal-induced oxidation process with DSC and TGA from which the mass loss during each of three phases and 6 critical temperatures was obtained per sample. With the same samples, soot chemical structure was characterised by Raman, XPS and TEM. This analysis provided information on different carbon chemical structures, O/C ratio on the sample surface, and nanostructure (fringe length and tortuosity). It was observed that generally for oxygenated fuel blends, the soot samples are more reactive, have more O functional groups on the carbon layer edge plane and have smaller polyaromatic layer size than reference diesel soots, while aromatic fuel blends show the opposite trends. However, the trend was not distinctive for all the samples analysed. Nevertheless, the two groups of data are highly correlated which implies that the chemical structure is the underlying reason dominating the soot reactivity. Specifically, the soot samples with more O functional groups and/or C-C bonds on the edge plane, are more reactive, they lose more mass at the lower temperature range and require lower temperature to initiate oxidation. *

Corresponding author. Tel: +61 7 31381129. E-mail: [email protected] (Zoran Ristovski) 1

Nomenclature DPM

diesel particulate matter

ROT1st

first phase reaction onset temperature

DPF

diesel particulate filter

ROT2nd

second phase reaction onset temperature

PAHs

polyaromatic hydrocarbons

SOT

start oxidation temperature

XPS

X-ray photoelectron spectroscopy

TOT

triggering oxidation temperature

TEM

transmission electron microscopy

MMLRT

maximum mass loss rate temperature

DSC

differential scanning calorimetry

FOT

finish oxidation temperature

TGA

thermal gravimetric analysis

O/C

oxygen to carbon ratio

NMR

nuclear magnetic resonance

FWHM(G)

full width at half maximum of the G band

EELS

electron energy loss spectroscopy

A(D1)/A(T)

the D1 raman band relevant area

VOCs

volatile organic compounds

A(D2)/A(T)

the D2 raman band relevant area

ML1st

mass loss in the first phase

A(D3)/A(T)

the D3 raman band relevant area

ML2nd

mass loss in the second phase

A(D4)/A(T)

the D4 raman band relevant area

ML3rd

mass loss in the third phase

1. Introduction Owing to the high fuel efficiency and energy production [1], the diesel engine is widely used in the transport sectors and power generation. However, diesel exhaust emissions are a concern, particularly DPM, which has been classified as a group 1 carcinogen based on “sufficient evidence of carcinogenicity” associated with an increased risk of lung cancer by World Health Organisation [2, 3]. Although promising new energy sources are being developed to replace diesel, the European Automobile Manufacturer’s Association data illustrates that the diesel engine use has increased [4] and are predicted to further increase over the next few years [2] until the development of economic and competitive hybrid technology which is expected to bring the share of diesel and gasoline motor vehicles down to 15–35% by 2030 [5]. This is especially the case for developing countries where existing vehicles without these technological modifications will take many years to be replaced [3]. Therefore, DPM remains a concern and requires further research to reduce the toxicity and meet the stringent emission standards. To reduce the DPM, DPF [3, 6, 7] and biofuels are the two effective strategies [8-10]. When installing DPF in diesel exhaust pipes, the particle emissions have been found to be significantly reduced. However, DPF needs to be periodically regenerated to maintain the fuel economy and engine effectiveness as well as its performance [11]. The common practical approach to DPF regeneration is the thermal-induced oxidation of the trapped particles. Important parameters are, balance temperature, ramp and other set parameters. The optimal 2

set of the regeneration parameters requires a balance [11] between the heat provided, including soot oxidation heat release and heat required to burn the appropriate amount of trapped soot which enables the DPF to have satisfying filtration efficiency [7, 12-15] but not too high pressure drop [16]. Therefore, it is essential to investigate the critical temperatures and mass loss profile-the reactivity of particles coming from diesel engines being fuelled with different types of biofuels. Considering the regeneration parameters requirement, the biofuel diesel particulate physicochemical properties and the associated impact on reactivity have been studied extensively. For example, several publications demonstrated that biofuel soot, which is the main part of the DPM [17] requires a lower temperature to burn due to a higher amorphous structure which is exhibited by the shorter fringe length [18-20] or wider fringe distance [21] as measured by the high resolution TEM images of soot spherule. In addition, some other properties such as surface area [22] and chemical composition [23] have been utilised to explain the soot reactivity. In each of the previous publications, samples from only a few different fuels were tested and the results were presented in a qualitative way. Furthermore, in previous literature several properties of the soot have been involved. However, the question of a common underlying property that correlates with these physicochemical properties but dominates the soot reactivity has not been addressed. It is widely known that DPM is composed of agglomerated near-spherical primary particles, and the primary particle is constituted of the graphene-like domain in turbostratic structure [21]. Each graphene-like domain formed by polyaromatic layers in plane with types of defects and oxygen functional groups on the edge. It has been evidenced that the nanostructure of diesel particles impacts on its oxidation rate and burnout trajectory [23]. Furthermore, during the oxidation process when the soot structure changes, the impact on the subsequent oxidation process will be further updated. For soot structure analysis, various physico-chemical techniques exist for the identification and quantification of the chemical structure of graphite like material [24], utilising for example NMR [25], XPS [26] or other EELS techniques [27]. Among them, Raman is a promising characterisation tool, owing to its high sensitivity and resolution, giving an abundance of structural and electronic information for carbonaceous material with extensively study over the past four decades. Based on these fundamental studies of the Raman mechanism [28-30] and application on graphite relevant materials [30-36] along with

3

the attribution of raman bands which allows the band fitting of the Raman spectra [37-46], it has been employed for combustion generated soot structure analysis in the last 15 years [20, 47-49]. In this study, a raman microspectrometer was employed together with the XPS and TEM to examine the soot structure and defects. In regard to the soot reactivity analysis, the temperature programme oxidation analysis is the most commonly used technique and the soot start oxidation temperature [7, 50] along with oxidation rate [19, 51, 52]

are the parameters to which most attention has been paid.

However, only a few publications have discussed the whole oxidation process [53-57], acknowledging that the reactivity is relevant to other features appearing in the process, e.g. transition phase, the triggering of oxidation temperature. It has been agreed that soot oxidation is a multi-step process [52, 54, 58]. Therefore, to analyse the role that biofuels play there is a need to scan the thermally induced oxidation process for the various biofueloriginated particles and analyse their mass losses and critical temperatures during the whole process. This will lead to a comprehensive understanding of soot oxidation and give the potential develop the link between the soot structure. A correlation between the carbon structure and reactivity has been a well-known research topic in carbon science and a number of studies have been conducted on carbonaceous materials like coal [59] , char [60, 61] and graphite [62] or black carbon [54]. However, the combustion generated soot structure differs from the industry carbonaceous materials mentioned above. Few publications have been generated in this area in the recent years. Hao et al. investigated the correlation between the soot reactivity and Raman structure, by comparing three types of soot generated from blends of diesel and PODEn (polyoxymethylene dimethyl ethers) in scaling proportion [49]. They concluded that PODE24 improves the reactivity of the generated soot which was due to the increase of amorphous carbon content. Song et al. demonstrated the positive correlation of soot oxidation rate with raman band and oxygen functional groups [63]. Knauer et al. conducted the thermal decomposition of Euro IV diesel soot and spark discharge soot [64]. They investigated the difference in the mass loss profile, by analysing the soot structure using Raman spectroscopy, and attributed the amount of the mass loss in the lower temperature to the amorphous carbon segment in soot (the D3 band) [64]. Nevertheless, with different fuel blends, the soot nano structure varies slightly [51] depending on the surrogates and the concentration in the fuel. Therefore, due to the limited number of soot samples and properties analysed in the previous

4

literature, they were unable to build up a general model to explain all the soot oxidation processes. In the present study, four types of fuels along with an additive were blended with diesel in scaling proportion, generating various blends of fuels and subsequent soot with differing properties. The soot thermal oxidation process was systematically studied, and the chemical structure was characterised by raman micrspectroscopy combined with other supportive techniques. Based on these analyses, the correlation between these two groups of data was investigated and discussed, with the aim of refining the understanding of the general nature of the soot reactivity and its underlying dominating factor for soot from the diesel engine.

2. Methodology 2.1 Engine, fuels and sample collection In the present study, soot samples were generated by a Euro III Cummins diesel engine, operating at 1500rpm, 75% load and 100% load condition. The specifications of the engine are shown in Table 1. Table 1: Test engine specifications Model

Cummins ISBe220 31

Cylinders

6 in-line

Capacity (L)

5.9

Bore × Stroke (mm)

102 × 120

Maximum power (kW/rpm)

162/2500

Maximum torque (Nm/rpm) 820/1500 Compression ratio

17.3

Aspiration

Turbo-charged & after-cooled

Fuel injection

Common rail

After-treatment devices

None

Emissions certification

Euro III

5

To investigate the fuel composition impacts on the soot structure and subsequent soot reactivity, two types of oxygenated fuels (butanol and coconut biodiesel) and two types of aromatic fuels (xylene and toluene) have been blended with two different conventional diesel fuels, composing two groups of blending. Group 1 is the diesel 1 blended with xylene, butanol and toluene respectively, with 20% and 30% proportion by volume. Group 2 is the diesel 2 blended with coconut biodiesel with increasing proportion of biodiesels (20%, 50%, 100%). As coconut biodiesel contains fatty acid methyl esters, which increases the oxygen content of the fuel blends. To further increase the oxygen content in the blend, the triacetin was added to biodiesel. Therefore, we can see scaling oxygen content in the diesel 2 group. In contrast, diesel 1 group butanol blends contain oxygen but in different chemical functional groups. The blend details are outlined in Table 2. Table 2. Experimental design and the composition of the tested fuel blends

Diesel 1 group

Fuels blended

Diesel 1

Blended fuel percentage Blends Abbreviation Oxygen content (%) Fuels blended

0 D1 0 Diesel 2

Xylene 20

30

Xy20 Xy30 0

0

Butanol 20

30

20

30

Bu20

Bu30

To20

To30

4.32

6.48

0

0

Coconut biodiesel

0

20

50

100

Coconut biodiesel +Triacetin 96+4 90+10

D2

B20

B50

B100

B96T4 B90T10

0

2.19

5.51

11.01

12.02

Diesel 2 group Biofuel & additive percentage Blends Abbreviation Oxygen content (%)

Toluene

14.00

The sampling for each blend started after 30 minutes of warming up the engine, when stable operational conditions have been achieved. The samples were collected on a polycarbonate filter (Millipore, Ireland) from the dilution tunnel that reduced significant condensation of the organic vapours onto the soot. The flow rate was 10 lpm, under atmospheric pressure. After collecting the sample, it was immediately stored in the fridge until further analysis.

6

2.2 Soot reactivity thermal analysis studies The following thermal analysis were conducted on a Netzsch Jupiter 449F3 Simultaneous Thermal Analyser (STA). The STA instrument simultaneously performs DSC and TGA. It records the mass change and the calorimetry signal. The samples were analysed in alumina crucibles with pin holed lids, under synthetic air (20% O2 mixed with 80% N2) at a flow rate of 50 ml/min. Samples were ramped from 25 °C to 700 °C at 10 °C/min. Baselines were collected on a pair of empty crucibles before every sample run. Each of the collected samples was gently scratched off the polycarbonate filter then loaded into a clean Al2O3 crucible. After this operation all tools that were in contact with the sample (including knife, tweezer, etc) were washed with ethanol to avoid cross contamination between samples. After all samples were prepared, weighed, recorded and capped they were placed onto the automatic sample changer of the STA. Due to the insufficient amount of mass it was not possible to analyse the B100, B96T4 and B90T10 samples. Thus, in the soot reactivity analysis, 10 samples were analysed. DSC is a thermo analytical technique in which the heat flow difference between a sample and a reference is measured as a function of temperature. Both the sample and reference are maintained at nearly the same temperature throughout the experiment. Thus, DSC traces are the heat flux versus temperature in comparison to the reference. It precisely monitors heat effects associated with phase transitions and chemical reactions as a function of temperature and is a very informative method in physical characterization of a material. As such, DSC is a suitable screening method to identify the soot oxidation process. After obtaining the thermal traces, the data analysis was performed using NETZSCH Proteus software. Soot reactivity is quantified by the oxidation rate and the temperature required to initiate oxidation [63], and oxidation rate is presented by the amount of mass loss during the specific temperature range. Thus, the mass loss in each phase and a series of critical temperatures were determined to estimate the reactivity of each sample. Figure 1 displays all the DSC traces from the diesel particulate samples. From these curves, we can see clearly the exothermic events during the heating of the samples. During the whole process, the value of heat flux fluctuates at least three times for most of the samples. This implies that there are at least three reactions occurring during the thermal-induced oxidation process of particles generated from the diesel engine [65, 66]. From the literature, the 1st 7

reaction is due to the evaporation of organic compounds which condensed on the soot, and the 3rd reaction is the soot oxidation [67, 68]. Although the 2nd phase was predicted [69] and observed and as “fast transient reaction” [66], the type of reaction or phase change that occurred during this phase has not been clearly defined in the literature. This is probably part of complex exothermic reaction due to the soot oxidation. This will be initially studied in the present manuscript based on the soot chemical structure analysis. For each phase, the reaction onset temperature (ROT) was determined by the analysis software. Based on the three onset temperatures from each curve, the temperature range of each phase for all the samples was obtained. The corresponding sample mass loss for each reaction can be obtained from the TGA (Figure 1s).

Figure 1. Differential Scanning Calorimetry traces of DPM and the schematic of the three reaction phases. The critical temperature parameters were analysed from the TGA (mass loss) and DTG traces. The DTG is the derivative of the TGA trace by temperature and represents the rate of mass loss (Figure 1s). The DDTG which is the derivative of the DTG was also used to pinpoint reaction temperatures. An example of the TGA trace and the corresponding DTG and DDTG of diesel 1 sample can be seen in Figure 2. The critical temperature parameters for analysis were the start of oxidation temperature (SOT), triggering oxidation temperature (TOT), maximum mass loss rate temperature (MMLRT) and finish oxidation temperature (FOT). The definition of these parameters is shown in figure 2 and is self-explanatory except for the TOT. The TOT is defined as the temperature at which the mass loss rate (DTG) exhibits an inflection [70]. This temperature could be more relevant than the others for DPF regeneration, 8

because temperatures around SOT are unable to provide a sufficient rate of regeneration,

140

0.04

100

0

60

-0.04

20

-0.08

-20

-0.12 0

250

500

TOT

SOT

Mass loss rate (%/°C or mass rate per centi degree [%/(°C•°C)]

Sample Mass / %

while MMLRT could be unnecessarily high.

FOT 750

MMLRT

Temperature / °C TGA

DTG

DDTG

Figure 2. Characterization of the soot oxidation key temperatures (diesel 1 soot as the example)

2.3 Raman microspectroscopy The Raman microspectroscopy is a laser-based microscopic technique used to perform Raman spectroscopy [71], which is a spectroscopic technique typically used to determine vibrational modes of molecules [72]. It is commonly used to provide a structural fingerprint by which molecules can be identified. Owing to the fast, non-destructive, high resolution spectral and carbonaceous structural and electronic provision [30], it has been in use nearly 40 years [73] as one of the most popular techniques for the characterization of the ordered, disordered and amorphous carbons [74]. The Raman technique is particularly useful for graphene because the absence of the band gap enables all wavelengths of incident radiation resonance, thus the Raman spectrum provides the information of both atomic and electronic properties [33, 75]. As diesel soot is composed of the oxidised graphene flakes [76, 77], Raman spectroscopy is an ideal technique to characterise the soot carbon structure. In this study, the inVia Raman microscope was employed. It consists of a light microscope and a Raman spectrometer with two selective excitation lasers. The microscope focuses a laser source onto a specific area of the sample then collects the light scattered off the surface of the sample and directs it through the Raman spectrometer. In the present study a 50 × objective microscope and the diode pumped doubled Nd-YAG laser (λ0=532 nm) was applied. Under the hardware used, the diameter of the laser spot on sample surface is 1 µm, and the 9

spectral resolution is approximate ~4 cm-1. Every day before running the samples the instrument was calibrated against the Stokes Raman signal of pure Si at 520.5 cm-1 with a silicon wafer ((111) crystal plane surface). Instrument operation was controlled by the software package Renishaw WiRE. All the spectra were collected under optimum focus on the sample spot with lowest background [38] . The spectrum range was set as 100-3200 cm-1, with standard confocality. To avoid the sample being burnt by the laser [78], the laser power was set to ~0.8 mW, the exposure time was 40s for each scan and 4 times accumulations being stacked into the spectrum. In order to determine the spectral parameters, after collecting the spectrum, the band fitting was performed in GRAMS/AI (Thermo Fisher Scientific, Levenberg-Marquardt algorithm). The goodness-of-fit was tested by the reduced χ2 value, which indicates agreement between the calculated fit curve and the observed spectrum. Only values between 1 and 3 were accepted [37, 47]. For each sample, at least 8 valid spectra were collected on different spots located evenly on the membrane, and all the samples spectra in the present study were performed under consistent measurement conditions. Though the Raman measurement is quite simple, the toll for the simplicity is paid by arduous spectrum interpretation, as the spectra of all carbonaceous materials display just few prominent features regardless of the original material structure. Moreover, the shapes, positions, intensities, width and area of the spectra provide a considerable amount of information, which makes the selection and attribution of the parameters very challenging. However, in recent decades, the understanding of the graphene Raman spectra has been moved by significant steps, through new results on graphene edges [32, 33, 35], oxidation [79], hydrogenation [34] and chemical functionalization [31, 80]. Thereafter, the understanding of the basic Raman process has been improved. It has been well established that Raman active vibrational modes of graphite are attributed to in-plane atomic displacement [30, 40]. The raman spectrum is a vibrational density of states (VDOS) modified by a coupling coefficient, which incorporates various resonances [31]. Empirically, the visible Raman spectra of disordered carbons show two prominent features which are G and D1 peaks, plus some minor modulations with the D2, D3 and D4 bands (Figure 3).

10

The G peak corresponds to the high wavenumber E2g phonon modes, that are related to the relative stretch of the C atom graphene basal plane [30], whereas D1 peak is due to the “breathing” modes for six-atom rings and requires a defect for its activation [31, 73, 81]. Moreover, it is observed that only “armchair” chemical structure can activate the D1 bands in Raman [30, 31]. As the G peak potentially shifts with the number of graphene layers [30], for the diesel soot the G peak was chosen as 1580 cm-1, which is the value used for multiple layers. The D1 band arises from the symmetric breathing mode of aromatic carbon atoms [30, 31], so it is attributed to the carbon atoms on the edge of graphene. Hence, D1 does not belong to the basal plane, but is due to the border zone of the total disorder modification graphite [31, 33, 82]. Also, the D1 band peak position moves depending on the excitation energy [83]. As the 532nm laser beam was used, the corresponding excitation energy was 2.33 eV, so the D1 band should peak at around 1345 cm-1. In addition to the dominant G and D1 bands, the weak bands D2, D3 and D4 are apparent which brings up the main peaks dispersion [30, 37]. They are the constitutes of the carbonaceous material Raman spectrum. Each of the band falls at the specific vibrational frequency which is due to the atomic displacement. Therefore, each band stands for the corresponding vibrational mode. The D2 band which is located at around 1620 cm-1. It was found in natural graphite extensively ground in air by an agate mortar, and its intensity in graphite increases with an increase in grinding time [43] or edge polishing [44]. Thus, the 1620 cm-1 band was inferred as the chemical bond arising from intensive grinding, which leads to an increase of the surface area of the ground samples where oxygen in air is easily chemisorbed on the free valence bonds of the edge carbon atoms [84]. The generated conjugated carbonyl groups attached to the edge carbon atoms of the layer planes is the attribution of the stretching vibration of the D2 band. Also, this band has been observed in oxidized glassy carbon (GC3OS) [85], oxidized compression annealed pyrolytic carbon (CAPC) and ion-etched CAPC [44], and it was considered to arise from a hexagonal ring stretching mode which is slightly modified by the formation of carbon-oxygen complexes near the surface or edges plane [85]. The arising spectrum seems to suggest that the 1620 cm-1 band is associated with the oxygen complexes formed on the carbon surfaces covered with very fine particles produced by the surface treatment. The formation of strong chemical bonds between carbon and oxygen atoms

11

may cause n-electrons to localize partially on chemical bonds near the edges plane sites. This would result in a decrease in intralayer carbon bond distance and, therefore, an increase in intralayer force constant. Such a change would be responsible for the observed increase in the frequency of the basal plane. Also, it is probably the reason why the D2 band was attributed to interlayer defects [44, 86]. The D3 band arose with the annealed carbon film and it disappeared with graphitization, so it was attributed to the tetrahedral carbon (sp3) which can be graphitized based on electron displacement [87-89]. Also, it remains after decomposition in symmetric G and D bands, so it is attributed to the defects on the graphene edge plane. Other evidence regarding to the D3 band attribution includes the analysis of the G peak shift with increasing the sp3 proportion in the glassy carbon [90, 91] and sputtered amorphous carbon [92], with sp3 content determined by EELS and NMR respectively. Their results show that Raman peaks at between 1580 and 1500 cm-1 (D3) change with altering the sp3 content in the samples. Thus, this presents fairly strong evidence that D3 is the source of the sp3 carbon bond in the carbonaceous materials. Nevertheless, there is other literatures that suggest the D3 band implies the presence of fivemembered rings [51, 93] or other non-six-membered carbon rings [94, 95]. The D4 band has been observed as a shoulder of the D1 band in carbonaceous material [96, 97] and tentatively attributed to the polyenic chains attached to the edge of polyaromatic basal or ironic impurities, which has little chance for the diesel soot. Most publications [30, 31] report its location at 1180 cm-1. In order to obtain the components of the atomic bonds in the samples and to compare the bonds proportion among the range of the samples, peak fitting is necessary. In addition, due to the difference of lifetime of the broadening, two shape lines are applied for the five-peak fitting. Specifically speaking, crystal structure (G, D1, D2 and D4) which leads to finite lifetime broadening for disordered graphite, can be well illustrated by the Lorentzian line shape. Whereas the D3 band originates from a disordered segment which is expected to generate the random distribution of phonon lifetimes so Gaussian line shape is applied [31]. For simplicity, based on line shape and the position, we build up a peak fitting model, which is a combination of four Lorentzian-shaped bands (G, D1, D2, D4) at 1580, 1345, 1610, 1180 cm-1, respectively, with a Gaussian shaped [98] D3 band at 1500 cm-1. This model is well accepted and used in the analysis of the carbonaceous material characterised with the Raman technique [21, 47, 49].

12

Due to the inclusion of the oxygen functional groups the D2 band being variable, the position shifts to some extent around 1620 cm-1. In the present study, as almost all the spectra fit better with 1610 rather than 1620 cm-1, when other bands positions are stabilised, we have chosen the 1610 cm-1 as the D2 band position. Regarding the selection between the intensity (height) or integrated intensity (area), we chose the area of each band as the difference is not significant for disordered graphite, which the diesel soot belongs to. The reason is that the area represents the concentration of the atomic structure which band stands for [30]. Theoretically, the anharmonic and electron-phonon coupling and other factors like e, h dispersion anisotropy and electronic momentum uncertainty all contribute to the width of the Raman bands [30]. Moreover, the experimental results of the G band from the single layer graphene and graphite exhibit big difference of the band area but have similar intensity. The soot structure is a turbostratic structure [99] which is a type of the randomly ordered multi layers oxidised graphene, in which all these considered factors might be involved to some extent in the Raman spectra.

Figure 3. The schematic of peak fitting for diesel soot Raman spectra An example of a typical diesel soot Raman spectra is shown in Figure 3. It can be seen that the D1 band area dominates the diesel soot Raman spectra. The reason is that visible Raman spectroscopy is 50-230 times [100, 101] more sensitive to sp2 sites than sp3 as well as other sites, as visible phonons preferentially excite the π bond in sp2. This also points out that even the band area does not represent the absolute concentration of the corresponding chemical bond. However, due to the sensitivity of the instrument to each type of atomic site being kept constant as the measurement is conducted under the consistent condition for all the samples, the band area among the samples is still comparable. Based on this, we will compare the

13

relative band area of the range of samples for discussion, which is an important point in the methodology part.

2.4 XPS X-Ray Photoelectron Spectroscopy (Kratos AXIS Supra) was employed to examine the oxygen sites on the soot flakes and to verify the raman band fitting result. A monochromated Aluminium K alpha X-ray source at 1487 eV was used. Survey scans were performed over a binding energy range of -5 eV to 600 eV, using the FOV2 lens mode and 110 µm aperture, Spectra were collected at a pass energy of 160 eV, 120 s per scan, and two scans averaging. The data analysis was performed in CasaXPS, with U2 Tougaard background being applied in region creation for elemental identification (An example of the spectrum can be seen in Figure 4s). For each sample on the substrate, 5 evenly scattered spots were selected to scan and averaged (The measurement data are display in Figure 4s).

2.5 TEM For TEM analysis, the soot samples were collected on holey carbon grids (300-mesh size) using a TSI nanometre aerosol sampler. Electron microscopy was carried out using a JEOL 2100 equipped with a CCD camera and the images were processed and analysed in a MATLAB-based code developed based on algorithms reported in the literature [102, 103]. A detailed analysis of the nanostructure of soot has been explained elsewhere [104].

3. Results and Discussion 3.1 Soot reactivity DSC trace and the reaction onset temperatures Figure 4 presents the onset of reaction temperatures of the first two phases. In the 2nd phase, the butanol and biodiesel (oxygenated fuels) samples clearly show a lower reaction onset temperature than the base sample from diesel. The trend exhibited in the 2nd phase has been documented previously in the literature [105, 106] and will be discussed in the subchapter 3.3. It is interesting to observe that Bu30 as the sample with highest oxygen content, of the ones we were able to analyse, has shown the lowest onset temperatures for both phases. For the 1st phase, it exhibits similar trend except the biodiesel part.

14

Figure 4. The reaction onset temperature in the 1st and 2nd phase for the analysed particulate samples 1

DSC trace and the mass loss fractions Figure 5 shows the mass loss in the three phases. As displayed in each plot, we observed significant difference in mass loss among the soot samples, being dependent upon fuel identity basically, especially in the latter two phases. In the 1st and 2nd phase, the trend is similar, in which the xylene and toluene samples lose less mass than diesel, but the butanol and biodiesel samples lose more than their baseline diesel particles (2nd phase is exceptional). As both butanol and coconut biodiesel contain oxygen in the fuel it maybe that the fuel combustion was improved was more complete in the engine cylinder. This result agrees with the literature in terms of VOC emissions, that with the O in the fuel molecule, more organic compounds are generated in the engine cylinder [19, 107]. These organics will condense on the soot surface during cooling and dilution and will be evaporated in the 1st phase DSC test. This organic coating will not affect the soot structure. Furthermore the techniques employed to analyse the soot structure in the present study are either less sensitive to the organic coating (Raman) or most likely will remove a large fraction of the organics within the high vacuum employed (XPS and TEM), therefore the results from these instruments will not be significantly affected by the organic coating. However, for the 2nd phase, the reaction mechanism is still undefined although some literature reported that a “transient reaction consumed almost 20% of the carbon in a soot particle” [66] even when the organics have been removed by heating up to 550 °C under inert 1

Abbreviations for different fuels are in Table 1.

15

air. Therefore, the 2nd phase presumably is relevant to oxidation or other structural change accompanied by mass loss, rather than organic evaporation.

Figure 5. The outline of mass loss fraction in three phases of all soot samples 1 The soot mass loss in the 3rd phase varies with the blended fuel type and the proportion blended. Specifically, blending with the aromatic fuels, led to an increase in the mass loss with the increase of the blend percentage, while the oxygenated fuels have exhibited an opposite trend to the blended fuel proportion. The reason behind expected trend in the 3rd phase could be due to the larger mass loss fraction for all the samples and therefore a smaller error in the measurement. In the first two phases the amount of mass lost is significantly smaller and therefore the non-systematic errors that could have influenced the trends were larger. There is still some left-over mass due to ash formation (see in Figure 2s). Therefore, the mass loss fraction in the 3rd phase is not just what is left over after the 1st and 2nd phase but is also dependent on the fuel type used.

Reaction temperatures during the main soot oxidation stage

16

The critical temperatures analysed from TGA and relevant derivative traces are displayed in Figure 6. The overall trends for all the critical temperatures are similar though the temperature at which they occur are different. Like the total mass loss trend in this phase (see Figure 5.), the critical temperatures for the xylene and toluene groups of samples showed an increase compared with the reference diesel sample, but for oxygenated fuels blends the soot critical temperatures decrease. These results agree with a number of results reported in the literature [19, 52, 63, 105, 106, 108], although not for these specific types of fuels. It is generally accepted that the oxygenated fuels can generate more reactive soot than diesel or other zero oxygen containing fuels [19, 63, 109]. The aromatic fuels (e.g. toluene and xylene in the present study) lower the soot reactivity compared with reference diesel, in which the majority of the fuel compounds are saturated hydrocarbons [110].

Figure 6. The soot oxidation key temperature parameters in 3rd phase for the all samples 1

3.2 Soot structure analysis 17

Structurally, it has long been suggested that soot particles are composed of turbostractically arranged polycyclic aromatic molecules (or, in other words, “oxidised graphene flakes” [111, 112]) that have higher edge site positions versus basal plane than conventional pyrolytic graphene [112, 113]. Chemically, this single defected graphitic plane is composed of in-plane polyaromatic layers [112] (G band in Raman), and types of defects and oxygen functional groups, which can be identified and relatively quantified with other bands by Raman spectroscopy. By applying the methodology presented in subchapter 2.3 to the collected 13 soot samples the diesel soot carbon structure was examined and quantified. For each band, by comparing the result among all the samples, we can see the difference of the site segment which the band represents.

The G band Figure 7a displays FWHM(G) for all the samples. The FWHM(G) is one of the common parameters in Raman spectral interpretation for characterising the graphene flake size [47, 114, 115], and it is comparatively more stable than the other parameters. The relative intensity of the D band to the G band (I(D)/I(G)) is another common parameter for graphene flake area characterization [116], but its correlation with the flake size varies significantly

depending on the graphitization degree [31]. Figure 7. (a)The FWHM(G) for all the soot samples; (b) the fringe length of the diesel 2 group soot [117] 1 (For each sample, the line inside the box presents the median of all values

18

of measurements for this sample, the dot presents the average of these values and the rectangle shows the interquartile range of the values) As the G band arises from the E2g mode of the carbon atom in basal plane, so it reflects the soot polyaromatic layer area, aromaticity, degree of order and is identified to become narrower with the basal plane area increase [118]. From Figure 7a, we can see that the soot generated from oxygenated fuels blends has a wider G band therefore has smaller polyaromatic layer area, while the soot generated from aromatic fuel blends is composed of bigger polyaromatic layer area than the baseline diesel soot. To confirm the dependence of the G band on the polyaromatic layer size on Figure 7b we show the TEM fringe length of the second group of soot samples, taken from Verma et al. [117]. The carbon fringe length is the cross section of the graphene area viewed in twodimensions, or its projection on to the TEM viewing plane. The fringe length from high resolution TEM should be comparable to the polyaromatic layer size [23, 119]. The TEM results show that with an increase of the biodiesel blend the fringe length is reduced and therefore the polyaromatic layer size. For biodiesel/triacetin blends the B90T4 and B90T10 the fringes become longer again. The same trend has also been observed in the Raman G band that first increases up to B100 and then decreases due to larger fringe length result as well. So, these two groups of data agree with each other ideally. The difference in polyaromatic layer size among the samples is due to the in-cylinder combustion process. The precursor molecules for soot formation and growth are PAHs that are formed in the engine cylinder due to incomplete combustion. The oxygenated fuels which enhance the combustion generate less amount of PAHs [54], while the aromatic fuels “enhance the soot emission through the facile condensation of aromatic units to form larger PAHs” [94]. This indicates that the chemical composition of the fuel influences soot nuclei, coagulation inception and subsequent aromaticity, which agrees with previous literature observations [108, 120, 121]. However, the formation of the soot is a very complex phenomenon, so a number of factors relating to the different fuels might also be involved. For instance, the in-cylinder temperature is probably another reason. The higher aromaticity in diesel soot might be due to the higher pyrolysis temperature in the combustion process [60]. A similar observation [122] has been made with the soot sampled in different regions of the flame, with varying combustion degree and temperature, that exhibited different graphitic polyaromatic layer size. Other observations, from varied length of alkane fuels, showed that

19

long chain heavy alkane fuel produced soot that was more graphitized [123] and had high stability [60]. After the carbon skeleton structure G band analysis, the defects on the graphene substrate will be discussed. All the defects are characterised by the ratio of integrated intensity of the band (band area) vs the total spectral area, for the D1-D4 bands.

The D1 band

Figure 8. The D1 Band relative area of all the soot samples 1

Figure 8 presents the D1 band relative area. From Figure 8 we can see that the butanol and biodiesel sample groups have smaller defects arising from D1 band, which is the “armchair” aromatic sp2, while the aromatic fuels and triacetin added blends generate soot having more of this type of defects. This result can be linked to the basal plane crystallite area. When the basal plane is smaller (butanol or biodiesel), it will have more defects correspondingly in the certain projected spot area as measured by the Raman laser [122].

The D2 band The D2 band represents the surface functional groups, and it is more relevant to the oxygen functional groups in diesel soot. Figure 9a outlines the relative area of the D2 band for all the analysed soot samples. For the butanol and biodiesel samples, the trend is clear, whereas for the aromatic fuels it is not that obvious. For the biodiesel group of samples, the D2 band relative area is proportional to the increase in the fuel oxygen content. Figure 9b presents the O/C ratio from the same samples, as measured by the XPS. Similar trends are observed for the O/C ratio and the D2 band area. To verify that the D2 band is proportional to the existence of the oxygen functional groups on the surface of soot we have plotted O/C ratio versus the relative D2 band width and observed a good correlation (R= 0.86, Figure 3s (a))especially for oxygenated fuels (R= 0.92, Figure 3s(b)).

20

Figure 9. (a), The D2 Band relevant area; (b), the oxygen content in all soot samples as measured by the XPS1 For the butanol or the biodiesel originated soot, more oxygen functional groups were found, though they did not linearly increase with the scaling of the oxygen content in fuel. This probably signifies the functional groups brought from the fuel originally, which was not converted into aromatics in the cylinder or just converted into oxygenated aromatics then agglomerated with the PAHs. It also might be due to the higher oxygen environment in the cylinder, so the soot was oxidised more or due to the temperature difference which influence the subsequent oxidation of PAHs layers generated in combustion [122, 124]. However they are formed, the oxygen functional groups differ among varied soots. As O/C is an important indicator of the nanocrystalline stability [93, 125-127], it indicates that the soot reactivity will differ considerably. This will be further elaborated in subchapter 3.3.

The D3 band Regarding the D3 band, the literature attributes it more to the amorphous carbon [98], which relates to the sp3 C-C single bond. We can see from Figure 10a that this band relative area varies among the samples, increasing with oxygen content in the fuel blends (either butanol or coconut biodiesel) and decreasing with more aromatics in fuels (xylene, toluene). As it is known from the literature that oxygenated fuels have a higher proportion of amorphous carbon [18], the assumption that the D3 band can be attributed to the amorphous carbon seems to be justified by our results.

21

Figure 10. (a), The D3 Band relevant area of all soot samples; (b), The fringe tortuosity of the diesel 2 group soot 1 Nevertheless, other researchers attributed the D3 band to the non-six-membered-ring carbon [111, 128]. This is based on observation of the graphene edge sites during its evolution in which the newly established five-member-ring carbon is generated. If the later assumption is true, it can readily be used to expain the correlation between the D3 band and graphene sheet bending. It has been demonstrated that this structure of the carbon (ring sp3) links with the graphene sheet bending [93] , which is related to the fringe tortuosity as observed from TEM microscopy. The Figure 10b below shows the carbonaneous fringe tortuosity of the parrallel samples in diesel 2 group. Data were taken from Verma et al. [117]. Comparing diesel 2 to B100, the soot from biodiesel blends has higher tortuosity, so is more bended than diesel ones and has a higher D3 signal increasing with the scaling of biodiesel content. This is consistent with the results in literature [129]. However, when triacetin is added, the soot polyaromatic layer becomes less bended than B100 and has lower D3 signal. This observation agrees with the results shown in Figure 10b. If the D3 band is attributed to the non-aromatic rings which is from the oxidation of the aromatic sp2 on the edge, then the generation and reduction equilibrium of this bond depends potentially on how high temperature the soot exposed [23, 111].

The D4 band

22

The last Raman band analysed is the D4, which represents the polyenic carbon (chain polycarbon with alternating double and single C bond) on the edge of the polyaromatic layer.

The results are shown in Figure 11.

Figure 11. The D4 Band relevant area of all soot samples 1 It has similar trend as the D1 band, except for the B96T4 and B90T10. This band signal increases with more aromatic fuels being blended and decreases with more oxygenated fuels (including triacetin) in the blend. The polyenic carbon is probably due to the opening of the aromatic sp2 on the edge (the D1 band) during the oxidation process. This process probably depends on the combustion temperature, with higher temperature more edge sites are oxidised and opened. As the diesel generates higher heat (temperature) than oxygenated fuel blends [130], the diesel soot gets more oxidised in the cylinder than biodiesel soots but less than aromatic fuel soot and therefore explaining the trend in the D4 band. In this subchapter, due to separately discussed on band, so the plot of each band is placed individually. However, all the Raman results and corresponding supporting resultsare collectively displayed in Figure 5s for overview and comparison. Another interesting observation is the variation of the Raman spectra for the same sample. For the soot originating from butanol and coconut biodiesel blends, the Raman bands show the increase of variation in various spots of samples, while for soot originated from aromatic blends the variation decreases in comparison to reference diesel. This is probably due to the difference of heterogeneity of the soot [64] graphite flake. Oxygenated fuels soot flakes are more heterogeneous, while aromatic fuels generate more homogeneous soot flakes than reference diesel.

23

3.3 The correlation between soot reactivity and chemical structure As the diesel soot is composed of oxidized graphene flakes, its reactivity is 10-100 times higher than that of the pyrolytic graphite [113], depending on the carbon initial nanostructure [51, 131, 132]. Therefore, in order to explore the dependence, Pearson’s correlation is conducted between the soot oxidation indices and soot structure properties which were discussed in subchapter 3.1 and 3.2 respectively. The calculated correlation matrix is shown in Figure 12.

FWHM(G)

A(D1)/A(T)

A(D2)/A(T)

A(D3)/A(T)

A(D4)/A(T)

Figure 12. Correlation matrix of soot reactivity and chemical structure 1, 2 From the correlation results, we can observe three main features numbered below: (1) The mass loss fraction in first and second phase (ML1st, ML2nd) has high correlation with the D2 and D3 bands relative areas (A(D2)/A(T), A(D3)/A(T)). If we assume the mass loss in the first phase is due to evaporation of organics, then for the second phase the correlation implies that the carbon bonds represented by D2 and D3 bands contribute to the 2nd phase reaction process. Specifically speaking, more D2 and D3 bands carbon bonds in the diesel particles will result in the more mass loss in the second phase, when the temperature is lower than 500 °C. This result agreed with the literature [63, 69, 133, 134]. For the D2 band which arises from the oxygenated function groups, the reaction starts at a lower temperature level because it requires low enthalpy to release the CO2 or CO and is 2

The solid circle denotes the positive correlation, while the hollow circle is the anticorrelation. Circle size denotes the correlation coefficient value.

24

generally the final stage in the soot flakes oxidation process [23, 111]. Technically, the oxygen functionalities are easier to attach by air on the edge site [93, 111, 135, 136]. For the D3 band, which could be attributed to sp3 C-C aliphatic or the non-six-membered aromatic rings, both of which are potential positions and favoured anchoring points for surface functional groups reaction with oxygen [137]. Also, because individual aliphatic atoms are exposed more, i.e., they are more susceptible to oxidative attack [138]. That is why the amorphous carbon segment is more reactive and contributes a significant amount of the mass loss in lower temperature in the tested soots [64]. However, the oxidation of cycloalkane radicals which mainly are five-membered carbon rings is still an open topic. It was suggested that the accumulation of embedded fivemembered rings impedes the oxidation of graphene edges [93], and it is further suspected that this structure requires high temperature to break up and therefore to be oxidised. Nevertheless, more studies agree that the five-membered ring related graphitic curvature makes the graphite more reactive [23, 94] and requires lower activation energy [23] to destruct this group because the curvature imposes the bond strain as the orbitals overlap and hence the resistance toward oxidation decreases [139, 140]. The electronic resonance stabilization is lessened [141], such as observed in carbon nanotubes [139, 140], which contain considerable amounts of bended carbon structure (five-membered rings) and are less resistant toward oxidation [142] than graphite. This can be explained by the presence of non-six membered rings in the soot structures which lead to higher fringe tortuosity. Therefore, higher fringe tortuosity leads to higher reactivity of soot which explains the correlation with the mass loss in the 2nd phase. (2) The mass loss in the 3rd phase (ML3rd) anti correlates with FWHM (G), which represents the soot graphene flake size - with larger flake size the smaller the FWHM(G) value will be (as explained in subchapter 3.2). This indicates that the bigger the flake area, the more soot is burnt off in this phase, that is too say the graphene basal plane is consumed mainly in the 3rd phase. Also, the mass loss in the 3rd phase positively correlates with the D1 band relative area and D4 band relative area ((A(D1)/A(T), A(D4)/A(T)). It is interesting that all the three bands represent the sp2 bond either in the basal (G band), defect (D1) or as polyenic chain (edge). The underlying reason might be due to the double bonds requiring higher energy to break or to other sites convert (e.g. to C-O, C-O-O, or cycloalkane etc., C-C bonds) [23, 111]. 25

Looking at the oxidation process of defect carbon material, the graphite material oxidation starts from the edge [23, 143]. As explained in detail in [144], “the oxidation of a free-edge site creates another free-edge site on a large PAH surface, which can lead to the chain oxidation of PAHs comprising soot”. Thus, the active sites (O functional groups and C-C bonds) are more important than other type of sites. (3) The FWHM (G), D1and D4 band relative area correlates with the reaction onset temperature in 2nd phase (ROT2nd), SOT, TOT, MMLRT and FOT. Consistent with these results are the measured graphitized carbon black exhibiting higher threshold temperatures than their non-graphitized precursor [145] which has more C=C atoms, and the activation energy of diesel soot showed positive correlation with FWHM (G) [148]. Diesel soot reactivity negatively correlates with the fringe length detected by TEM [19]. It is therefore presumed that in this phase, when these soot segments are oxidised, the sample with more Carbon double bonds requires higher energy (higher temperature) to proceed with the reaction. To sum up, the samples with larger polyaromatic layer size [125] combined with more sp2 defects (the D1 band), or polyenic carbon (the D4 band), require higher temperature to be oxidised. Thus they are less reactive, which has been observed in the radical burnout of carbon black by TEM analysis [146]. (4) On the other hand, the D2 and D3 band relative area highly anti-correlates with all the critical temperatures (ROT2nd, SOT, TOT, MMLRT, FOT) during thermal induced oxidation process. This is in agreement with the results from the literature [62, 63], in which the reaction mechanism was not explained thoroughly. This potentially implies that with more O on the edge of the soot polyaromatic layer, the soot is more reactive and requires lower temperature for oxidation. This is more understandable, because C to C bonds no matter what they are, they need to be oxidized into C-O [23, 111] and then can proceed to further oxidation resulting in CO2 or CO eventually. Nevertheless, the D2 and D3 are mainly recognised as reactive sites, which are potentially accessible for O-atom or another oxidisers addition [93, 111]. Also, it is these sites that provide the potential rearrangement of the edge by bonding with adjacent atoms in- plane [127] .

4. Conclusion 26

The present study examines various types of soot generated from a diesel engine using a wide range of fuels by analysing the thermal oxidation and chemical structure. It was observed that the results for both the thermal oxidation and chemical structure generally depend on the fuel identity. Soot reactivity studies show that biodiesel-generated soot is more reactive with more mass loss at lower temperature range and requires lower temperatures to reach the critical point for DPF regeneration in comparison with the particles from baseline diesel, which is primarily composed of saturated hydrocarbons. Whereas when the soot is generated from aromatic fuel blends, it is less reactive with less mass loss at the lower temperature range and exhibits higher critical temperatures during the oxidation and DPF regeneration process in comparison with diesel particles. Regarding the chemical structure, the fuel blended with oxygenated biofuels generates more active sites and less sp2 bonds, while the fuel blended with aromatic hydrocarbon fuels generates fewer active sites and higher aromatic soots in comparison with reference diesel. Although both categories of the results mainly depend on the fuel identity, they don’t linearly correlate with the fuel compositions. However, the two groups of results closely correlate with each other, which gives insight that soot chemical structure is the underlying reason determining the soot oxidation. The correlations found are outlined below: The soot mass loss fraction in the 1st and 2nd phase correlates with the D2 band and D3 band relative areas. In addition, the D2 band relative area is consistent with the oxygen content provided by XPS, and the D3 band relative area is supported by fringe tortuosity analysed by TEM, which links with the five-membered rings. It can be concluded that in these two phases, for soot with more oxygen or D3 represented defects, more weight is lost in the lower temperature stage. In this lower temperature stage, before 500 °C, the soot oxidation is tied to active sites initial oxidation or defects removal. Therefore, these results potentially imply that it is oxygen functional groups and/or five-membered rings (or sp3) initially drives soot oxidation in the lower temperature stage. In the 3rd phase of the thermal oxidation, the mass loss fraction highly correlates with the polyaromatic layer size which is characterised by both the Raman and TEM techniques. This point is understandable as in the main soot burning phase the consumed mass should come from the graphene bulk. Thus, the bigger the polyaromatic layer area, the more mass consumed.

27

Also, the mass loss ratio highly correlates with the D1 and D4 relative band areas. This can be explained by noting that in the soot burning phase, these two categories of segment potentially participate as well. The three bands involved in this phase are all the double carbon bonds at the molecular level, which requires higher energy to break down in comparison with O function groups or C-C bonds. Thus, the G, D1 and D4 bands represented bonds become involved in higher temperature regions comparatively. The question is at which temperature the carbon bond gets broken and C atoms get oxidised. The D1 band relative area correlates with ROT2nd, and other critical temperatures at the end. While the D4 band relative area just correlates with the temperature from MMLRT, and only FOT. Based on these results, we presume that that aromatic sp2 armchair sites (D1 band) become involved in the reaction from the 2nd phase to the end, while the polyenic C=C chain bonds (D4 band) just become involved in the higher temperature stage.

Acknowledgement We thank Llew Rintoul, the technologist of Raman microspectroscopy, in Queensland University of Technology (QUT) for the guidance during the measurement and band interpretation, and Peter Hines from QUT for the XPS measurements. The first author gratefully thanks Neal Fairley, the instructor in CasaXPS Ltd for the XPS technical and data interpretation support, as well as Dr Doug Stuart, Director at Suncoast Renewables, for support through a donation of coconut biodiesel. Also, the first author thanks for the financial support from China Scholarship Council (CSC) for the PhD scholarship. The research was partly supported by ARC grant DP180102632.

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39

Authorship contribution declaration Yi Guo, responsible for the experiment design, sampling, conducted most part of the measurements, and following data analysis and manuscript drafting. Zoran Ristovski, guided the experiment design, results discussion and manuscript drafting. Elizabeth Graham, participated part of the experiment design (soot reactivity part), measurement, and following data analysis as well as draft correction. Svetlana Stevanovic, participated in the experiment design and sampling. Puneet Verma, responsible for part of the data provision (soot nanostructure from TEM) and sample generation. Mohammad Jafari, participated in the sample generation, statistical data analysis part and draft correction. Branka Miljevic, participated in the results discussion and draft correction. Richard Brown, participated in the experiment design and draft correction.