Article pubs.acs.org/EF
X‑ray Spectral Line Shape Analysis of Asphalt Binders K. Gebresellasie,† J. C. Lewis,† and J. Shirokoff*,‡ †
Department of Physics and Physical Oceanography, ‡Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada A1B3X5 ABSTRACT: An X-ray diffraction (XRD) technique has been applied to the study of asphalt binders. XRD patterns were obtained using a Rigaku DMax 2200 V-PC (personal computer). Jade software (version 6.1) was used to perform the peak search, full width at half-maximum, and profile fits with Pearson VII and pseudo-Voigt mathematical functions over the areas of interest. The XRD spectra were also modeled in Mathematica using a generalized Fermi function (GFF). Analysis shows a mixed correlation in the data in terms of mathematical fit, analysis of fit, and plotting. Aromaticity and crystallite size parameters were calculated for the asphalt binders using the data from the three functions (Pearson VII, pseudo-Voigt, GFF). Results are compared and discussed with respect to aging effects, asphalt binder models (Yen, Yen−Mullins), and fuel technology.
1. INTRODUCTION Asphalt binder is a dark brown to black colored semiviscous material that can be derived from crude oil refining, natural deposits (Trinidad Lake), bitumen, and coal, all of which differ in chemical composition. However, a vast majority of asphalt binders in modern day use are derived from crude oil refining. Crude oil feedstock to these refineries have traditionally refined conventional light crude oil, with worldwide reserves estimated at about 1 trillion barrels of oil and declining versus unconventional (bitumen, heavy oil, extra heavy oil, shale oil) crude oil at 5.6 trillion barrels of oil.1 Unconventional oils have recently begun playing an increasing important role in world energy supply and consumption. These oils differ in viscosity and API (American Petroleum Institute) gravity with unconventional oil at about API 10−20°, and conventional oil > 38°.2 Unconventional oils tend to be heavy and complex, with a higher volume of heavy hydrocarbons (i.e., including asphalt binder and asphaltene molecules), require more intensive processing, and yield more byproducts that contain large amounts of carbon.3 As a consequence, these materials are of growing research interest in terms of converting the heavy components into the more desirable lighter fuel fractions by way of heavy oil upgrading, process modeling, stimulation, bioprocessing, catalysis, and petroleum refining technologies. The structure of asphalt binder can be measured in terms of length scale units by XRD. Scientists who perform research on breaking down the size of asphalt binder into smaller carbon units by way of petroleum refining catalysts gain insight into the mechanism (i.e., scission of chains and units of asphaltene) and the next steps to engineer the petroleum upgrading (i.e., heavy oil upgrading into larger quantities of lighter oils). As with most petroleum products, they have multiple industrial uses. Asphalt binder is found not only in fuel technologies but also in construction materials. Asphalt binder, when mixed with ingredients such as crushed stones and gravel, produces what is commonly referred to as asphalt or asphalt cement pavement or some abbreviation thereof. The material termed “asphalt binder”, which chemically includes asphaltene molecules, is an essential constituent of “asphalt” or “asphalt cement” as used in pavement. The two different sets of © 2013 American Chemical Society
terminology are important to keep track of because the former material is used in making the latter material. It is necessary to keep track of the different uses of the word “asphalt”, as the binder is used to make the paving material, so that the two materials are closely related. In the past, molecular models have been used to try and describe asphalt binders. Two popular competing asphalt binder molecular models differ in their distribution of the fused aromatic rings and aliphatic moieties. On the basis of the chemical structure of asphalt binder that has been investigated by, for example, fluorescent spectroscopy, it is generally thought that asphalt binder consists of condensed aromatic nuclei that carry alkyl and alicyclic systems with heteroatoms of nitrogen, oxygen, and sulfur, as an “island model” description.4−6 The molecular weight (MW) is generally considered to be about 750 g/mol (Da), with a full width at half-maximum of 500−1000 Da. 7 When larger molecular weights were considered, a second competing theory on the chemical structure of asphalt binder portrays their character to be seen as several fused ring systems that are interconnected by alkyl chains, in the form of an “archipelago model”.6,8,9 Methods used for the study of colloids (including X-ray diffraction) have been used to study asphalt binders, in order to describe the system at various length scales.4,6−9 Yen initially studied solid samples of asphalt binders by using XRD.10,11 These studies developed an understanding of asphalt binders by way of the Yen model, which illustrated the aggregation mechanism from the molecular state to the cluster state.6,9−12 Further research has led to a convergence of the overall research data and refinement of the hierarchical picture of asphaltenes in the Yen−Mullins model (modified Yen model).6,9 The model highlights aggregation from the molecule (1.5 nm) to nanoaggregate (2 nm) to cluster (5 nm), and shows the dominant molecular and colloidal structures for asphaltenes in laboratory solvents and crude oils.9 The MW is ∼750 g/mol (Da), an “island” architecture dominant with one Received: November 9, 2012 Revised: March 4, 2013 Published: March 6, 2013 2018
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Table 1. Pertinent Asphalt Binder Propertiesa13 asphalt binders
source
T1 T2 T3 T4 T5 T6 T7
western Canadian unknown unknown unknown unknown unknown unknown
L1 L2 L3 L4 L5 L6 L7
Boundary Lake Montana/Bow River Cold Lake Redwater Lloydminster Lloydminster Cold Lake
H1 H2
Lloydminster Venezuelan
N1 N2 N3 E9
unknown unknown unknown unknown
AAE O1 O2
Lloydminster unknown unknown
modification type Timmins, Ontario RET + PPA oxidized SBS SBS + acid-modified SBS oxidized acid-modified Lamont, Alberta oxidized straight straight straight oxidized straight straight Hearst, Ontario straight straight Northern Ontario straight straight acid-modified acid-modified various oxidized oxidized oxidized oxidized
grades PG PG PG PG PG PG PG
64-34 64-34 64-34 64-34 64-34 58-34 52-34
80/100 A, PG 58-22 150/200 B, PG 52-28 300/400 A, PG 46-34 80/100 C, PG 58-22 80/100 A, PG 64-28 150/200 A, PG 52-28 200/300 A, PG 52-34 150/200 A, PG 52-33 150/200 B, PG 52-33 PG PG PG PG
52-34 52-34 52-34 52-40
60/70, PG 70-22 PG 58-34 PG 58-34
a
Note: (RET) reactive ethylene terpolymer; (PPA) polyphosphoric acid; (SBS) styrene−butadiene−styrene copolymer. Penetration grades are according to the Canadian Government Standards Board specification, whereas PG grades are according to AASHTO M320.14 samples after 1 week of aging were measured by XRD and analyzed. The X-ray diffraction spectral scans were obtained by a Rigaku D/ Max-2200 V-PC using monochromatic Cu Kα radiation at 40 kV and 40 mA. The scan range was 5−110° 2θ at a scan rate of 0.001° 2θ/s and a detector count time of 5 s/step. The XRD instrument employed a divergence slit of 0.5° and a receiving slit of 0.3 mm. The XRD spectra were then peak searched using a parabolic filter (99 raw data points, screened out Kα-2 peaks, peak location summit, threshold sigma 3.0, intensity cutoff 0.1%, range to find background 1.0, points to average background 7) over the angular range of 5− 110°. The full width at half-maximum (fwhm) and profile fits were obtained by using either Pearson VII or pseudo-Voigt functions (fixed background, exponent 1.5 and Lorentzian 0.5) over the ranges of 5− 35° 2θ and 60−110° 2θ on the XRD line spectra of interest. The XRD spectra were also modeled in Mathematica software using a generalized Fermi function (GFF), which was previously described in the literature.20 The XRD is controlled by a personal computer and Jade version 6.1 software linked to the X-ray console computer system and hardware (power supply, X-ray source, and goniometer).
aromatic ring system per molecule. At the critical nanoaggregate concentration (CNAC), molecules form nanoaggregates with small ( 10, but the latter is not usually observed experimentally in XRD patterns. The generalized Fermi functions (GFFs) were used in Mathematica software in order to fit the raw data points from XRD into a distribution of X-ray intensity versus scattering angle 2θ. The spectrum containing the major peaks (bands) found in the plots of raw XRD data, Pearson VII, and pseudoVoigt have some subtle and small differences. The results of the intensity distributions were then plotted (e.g., see Figure 2), and the values of the integrated intensity of the main peaks, fwhm, and 2θ values were used to calculate aromaticity and crystallite size parameters of the samples. These calculated results were then tabulated and compared to the results from the Pearson VII and pseudo-Voigt profile fitting distributions initially using these two mathematical functions for samples L1−L7 and N1−N317 and three mathematical functions (Pearson VII, pseudo-Voigt, GFF) for samples T2−T7.20 In this paper, we will discuss the comparison of XRD results of asphalt binders and three mathematical functions for samples (L1−L7, N1−N3, E9, AAE, O1−O2, H1−H2, T1) and some of the important aspects of the spectral line shapes next. 3.3. Comparison of XRD Results from Asphalt Binders. The X-ray diffraction patterns observed require fitting of the theoretical distributions, which is crucial in order to get information from the spectral lines. Pearson VII and pseudoVoigt fitting procedures are used on the three major bands: γ, graphene (002), and (100), positioned at approximately 2θ = 20, 25, and 44°. Also, determinations of the major XRD peak fwhm and peak 2θ position values were needed for some of the aromaticity and crystallite size and crystallite parameter calculations. These derived parameters can be found elsewhere for samples L1−L7 and N1−N3 using Pearson VII and pseudo-
Pseudo-Voigt: y(x) = η(CG1/2)/( π H )exp( −CGx 2) + (1 − η)(C L1/2) /(πH′)(1 + C Lx 2)−1
(3)
Pearson: y(x) = PVIII(x) = Γ(β )/(Γ(β ) − 1/2)(C P1/2) /( π H )(1 + C Px 2)−β
(4)
Generalized Fermi: y(x) = A /[exp(−a(x − c)) + exp(b(x − c))]
(5)
In the above equations, H and H′ are the full widths at halfmaximum (fwhm); x = (2θi − 2θk)/Hk, is the Bragg angle of the ith point in the diffraction pattern with its origin in the position of the kth peak divided by the peak’s fwhm; 2θi is the Bragg angle of the ith point of the diffraction pattern; and 2θk is the Bragg angle of the kth Bragg reflection.21 From Figure 3, the Lorentz function portrays a shape that is sharp near its maximum but has long tails on each side near its base relative to the shape of the Gauss function, which falls off rapidly in the wings, especially when compared to the Lorentzian, and which has a taller rounder maximum. Both functions are even or centrosymmetric, that is, G(x) = G(−x) and L(x) = L(−x). The shapes of observed Bragg peaks, which are the result of convoluting multiple instrument, specimen functions and subsequent broadening, are seldom described well by Gaussian or Lorentzian distributions especially in X-ray diffraction research results. Usually, experimental XRD peak shapes lie somewhere between the Gaussian and Lorentzian distributions. These two functions can be convoluted in different proportions; however, this is awkward since it requires numerical integration when peak shape function parameters change. A remedy to this situation of convolution is to use linear combinations of Gaussian and Lorentian functions in the pseudo-Voigt form with the η and η − 1 ratio varying from 0 to 1. 2021
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Voigt without GFF17 and samples T2−T7 using all three mathematical functions (Pearson VII, pseudo-Voigt, GFF)20 and are briefly reviewed in this section. The aromaticity fa was determined by calculating the areas A of the resolved peaks for the γ and the (002)-graphene using the following formula
GFF, and 0.44−0.40 and 0.47−0.46 are obtained using Pearson VII and pseudo-Voigt functions. On the other hand, samples L1 and L7 have an apparent aromaticity of about 0.94 and 0.97 from GFF, which are relatively high compared to the similarity in values (0.53−0.53 and 0.59−0.72) from both Pearson VII and pseudo-Voigt calculation for both samples. Also, the higher aromaticity values for L1 and L7 are likely the result of asymmetry in X-ray diffraction pattern profiles at lower angles that affect the quality of the profile fit. For the rest of the samples, peak profile fitting and the calculation of aromaticity led to more comparable results. The method for obtaining the aromaticity and crystallite parameters using standard X-ray diffraction patterns and standard procedure was initially developed by Yen et al.10 The interlayer distance between the aromatic sheets dM, was calculated using Bragg’s Law and the maximum of the (002) graphene band according to
fa = CA /C = CA /(CA + CS) = A(graphene) /[A(graphene) + A(γ )]
(6)
where CS, CA, and C are the number of saturated, aromatic, and total carbon atoms per structural unit, respectively. It should be noted that the equation used for the calculation of the fa is based only on the stack cluster aromatic carbon in the (002)graphene peak and not on all the aromatic carbons of asphalt binders. Hence, fa does not represent a true aromaticity of the molecules but is, rather, an estimate. Also, issues arise from the use of the profile fitting program for Pearson VII versus pseudo-Voigt functions where the Pearson VII function favors larger γ-peak areas, A(γ), relative to (002)-graphene peak areas, A(graphene). This, consequently, calculates a higher aromaticity value when using the pseudo-Voigt function compared to the Pearson VII function calculations, as seen in Table 2. Further complications come from comparing calculations from the three functions as it concerns profile fitting the two major peaks in the low-angle spectrum (γ peak and (002)-graphene peak) in the presence of additional peaks (i.e., paraffin peaks). Under these conditions, eq 6 becomes questionable unless it is replaced with more variables to account for three or more peaks over the low-angle spectral range and validated by experimental results. Moreover, the fundamental diffraction method used in this case to calculate aromaticity should be questioned since it assumes that the γ peak comes from all aliphatic carbons present (i.e., paraffins and naphthenes), but in fact, the γ peak arises primarily from paraffins order.19 Backgrounds are a major issue in the X-ray diffraction patterns of asphalt binders, as observed in this work, and they were set as linear. On the low 2θ side, the baseline is not sufficiently well-defined, so one must use the high value end of the X-ray diffraction pattern and fix this as a constant baseline. This introduces the potential for a reasonable assumption and possibly statistical inaccuracies that may influence the outcome of the results if not sufficiently repeated in order to obtain reproducibility in data. The change in baseline, however, had, in most cases, very little influence on the relative results, such as the aromaticity. In all cases where manual refinements were added, sufficient repeated measurements were made in order to document statistically significant data and obtain adequate reproducibility in results. In the case of 3 (L1, L5, L7) of the 17 asphalt binder samples studied using GFF, the GFF gives fits that are formally superior to those from pseudo-Voigt and Pearson VII, but they show less internal consistency and self-consistency. For instance, asphalt binder O1 does look, from the appearance of the XRD profile fitting, as apparently having a larger than normal (002) contribution. When using GFF modeling, the γ peak can sometimes become very broad, and hence the graphene (002) contribution becomes very small, leading to less consistent calculations of the aromaticity (in the range of only 0.31 in this case), whereas the Pearson VII and pseudo-Voigt give a more consistent calculation of aromaticity (approximately 0.48 and 0.51, respectively). The same is observed for asphaltene L2 and L4, having an aromaticity of about 0.31−0.32 when fitted in
dM = λ /(2 sin θ )
(7)
where dM is the interlayer distance, λ is the wavelength of the Cu Kα radiation, and θ is Bragg’s angle. The distance between the saturated portions (aliphatic chains, condensed saturated rings) molecules or interchain layer distance is given by the relationship dγ = 5λ /(8 sin θ )
(8)
The average diameter of the aromatic sheets is calculated from the following formula incorporating the Scherrer equation for measuring mean crystallite size La = 1.84λ /(ω cos θ ) = 0.92/B1/2
(9)
where B1/2 is the fwhm using the (11) band and ω is the bandwidth. The average height of the stack of aromatic sheets perpendicular to the sheet plane was calculated using the following formula Lc = 0.9ω cos θ = 0.45/B1/2
(10)
where B1/2 is the fwhm using the (002)-graphene band. The number of aromatic sheets in a stacked cluster, Me, was calculated from the values of Lc and dM according to the following relationship Me = (Lc /dM + 1)
(11)
The crystallite parameter of the interlayer distance (dM) had much higher values of 17−17 from GFF samples (L1 and N1) compared to Pearson VII (4.4−4.4) and pseudo-Voigt (4.5− 4.5). However, the rest of the results for all other samples show more consistency. The two crucial parameters to understand crystallite size are the average height of the stack of the aromatic sheets perpendicular to the sheet plane (Lc) and the average diameter of aromatic sheets (La). These parameters are very sensitive to fwhm, as observed in (Figure 4), where a theoretical relation between crystallite parameters Lc (Å) or La (Å) and fwhm (bandwidth at one-half height (2θ)) has been calculated.22 It is evident that Lc is more sensitive to small changes in fwhm (bandwidth) and causes the stack height (Lc) to change. By comparison, the sheet diameter (La) data are less sensitive and only for a narrow (110) band will, in effect, be important. Again, we observe that the basic results of spectral line shape profile fitting can become very different in terms of calculating 2022
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shape functions, profile fitting, and asymmetry of XRD data. A total of 17 asphaltene samples were obtained from different locations and low-temperature aged for 1 week. Analysis of the results suggests that the aging process may have influenced the X-ray data, through changes in chemistry (oxidation), peak shape, and calculation of aromaticity and crystallite parameters studied. The results showed a correlation among Pearson VII, pseudo-Voigt, and GFF for some crystallite size parameters (dγ, La, Lc, Me) more so than others (fa, dM), and the XRD experiments unveiled some features of asphalt binder aggregates. The clusters of associated asphaltene molecules with stacked aromatic sheets are relatively stable up to 150 °C, and the layer distance between aromatic sheets (dM) and the number of aromatic sheets in a stacking cluster (Me) is uniform in these asphaltene species. Analysis of the results obtained in this research work indicates that XRD provides insight into the structural and compositional properties of asphalt binders, which is significant in understanding the aging process of asphalt binders and asphalt cement pavement. Since asphalt binder XRD results have previously complimented civil engineering tests, as reported by some research studies,13 it is reasonable to expect in the future that asphalt binder XRD results can possibly play a role in predicting the performance and durability of asphalt in pavement, and asphalt binder conversion into lighter fuels.
Figure 4. Relationship between crystalline dimension Lc (Å) or La (Å) and fwhm (bandwidth at one-half height (2θ)).22
aromaticity and crystallite parameters, leading to trends when using different mathematical functions of GFF, Pearson VII, and pseudo-Voigt to interpret XRD data. The XRD peak profile fitting procedure may indeed be somewhat oversimplified especially when graphing data involves nonsymmetric peaks formed by several contributions, such as from sample preparation, additional peaks from chemical phases, and noise in the residue of the data. It is possible that the aging of the samples can lead to changes in chemical composition, such as oxidation, crystallinity, atomic ordering of planes, sheets, stacks, aromatics, and aggregation, as interpreted by the Yen−Mullins model and other researchers.7,9−11,13,19 Also, size affects in asphaltene molecules (∼1.5 nm), nanoaggregates (∼2 nm), and clusters (∼5 nm) with respect to the Yen−Mullins model can play a role.9,20 Smaller asphaltene sizes and larger numbers of asphaltenes in asphalt binders will produce line (peak) broadening in the XRD spectra, and it should not be overlooked that nanomaterials can be strengthened by increasing the volume fraction of nanocrystals, as commonly observed in metals due to delocalized electrons. Thus, it is presumed that these asphalt binder materials would experience a change in materials behavior (i.e., stress versus strain) owing to their interactions at asphaltene cluster interfaces and interphase boundaries. When comparing our data to those obtained by small-angle neutron scattering (SANS) and small-angle X-ray scattering (SAXS), we find our values to be slightly lower for Lc, higher for dM, and lower for Me.6 The main reason for this discrepancy requires accounting for the solvated solution asphalt binder samples studied by the authors of ref 6 compared to the dry asphalt binders found in our studies and by Yen et al. in order to understand the more extended stacking of nanoaggregates in dry asphalt binder samples, especially as reported for Yen’s samples.10 Future work on the topic of asphalt binders is highly recommended. The present research work would benefit from additional study on aromaticity calculations and correlation to NMR data18 and XRD data from polar aromatics, napthene aromatics, and saturates,19 as we have previously shown for Middle East (Saudi Arabia, Kuwait) asphalt binder samples.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS Support from the Canadian Foundation for Innovation (CFI) and the Natural Science and Engineering Research Council of Canada (NSERC) is gratefully acknowledged.
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REFERENCES
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4. CONCLUSIONS XRD pattern profile fits by Pearson VII and pseudo-Voigt were directly compared to generalized Fermi function modeled data and measured aromaticity and crystallite parameters with some higher and lower values observed. The mixed results of the generalized Fermi function data relative to the Pearson VII and pseudo-Voigt function results can be explained in terms of peak 2023
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