Investigating the Interactions of the Saturate, Aromatic, Resin, and

Dec 19, 2014 - Shangxian Xie , Qiang Li , Pravat Karki , Fujie Zhou , and Joshua S. Yuan ... Road Materials and Pavement Design 2017 18 (sup3), 249-25...
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Investigating the Interactions of the Saturate, Aromatic, Resin, and Asphaltene Four Fractions in Asphalt Binders by Molecular Simulations Peng Wang,†,‡ Ze-jiao Dong,*,† Yi-qiu Tan,† and Zhi-yang Liu† †

School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150090, People’s Republic of China ‡ School of Transportation Engineering, Shandong Jianzhu University, Jinan, Shandong 250101, People’s Republic of China S Supporting Information *

ABSTRACT: Molecular dynamics provides a powerful tool to understand the elusive structure−performance relationship of asphalts. The combined molecular models were selected to investigate the interactions of the saturate, aromatic, resin, and asphaltene (SARA) four fractions and the correlation between fractions and the “bee-like structures” by atomic force microscopy in asphalts. The results showed that van der Waals was the main force to control intermolecular interactions. The arrangement of SARA fractions largely conformed to the modern colloid theory. However, some alkanes, sulfides, and condensed aromatics had different behaviors. Long-chain alkanes inserted into layers of asphaltenes, and small sulfides without long alkyl chains adhered to large sulfides or asphaltenes; nevertheless, counterpart condensed aromatics became much closer to those molecules. Strong interactions between the dispersed phase and continuous phase generated a larger size and greater number of “bee structures”. Asphaltenes played as a core, and long-chain paraffin played as an inducer, to affect the distribution of “bee structures”.

1. INTRODUCTION

However, the knowledge about the detailed chemical component is only of limited help for understanding asphalt. Wiehe and Liang reported that straight asphalt contained about 105−106 different molecules.5 This implied that the process of obtaining the detailed information on chemical components was torturous and impossible; therefore, researchers using modern separation techniques divided asphalt into different fractions, which had similar polar and molecular characterization. The well-known separation methods cut asphalt into four fractions, namely, saturate, aromatic, resin, and asphaltene (SARA) fractions. It is believed that these different fractions provide different physical and mechanical properties, and they chemically and physically interact with each other, giving rise to rich and complex behaviors of an asphalt binder.6 Recently, the progress of microscopic techniques helps us to recognize the microphase structures of asphalts more closely; i.e., atomic force microscopy (AFM) offered the distribution of the microphase structure by examining the intermolecular force between the AFM probe and the surface of the measured sample. AFM was introduced in 1986 by Binning et al. to measure surface topography and surface forces.7 In heat-casted asphalt samples with AFM−phase detection microscopy (PDM), three phases were identified in the surface topography at room temperature, including the catana phase (bee-like structures), the peri phase, and the para phase.8 The “bee-like structures” were defined as a dispersed phase, surrounded by the peri phase, and the para phase was the outermost part of the AFM

The Materials Genome Initiative (MGI), launched in 2011 in the U.S.A., is aimed at creating a new era of material design based on the structure−performance relationship.1 This important relationship is considered a basic rule to cut down the designing cycle and improve the performance of materials. In the asphalt field, the term “structures” is near to the chemical component or molecular component. The abstruse correlation between the chemical component and mechanical properties has been a hotspot in this field. Therefore, the microphase structure, which depends upon the chemical component and acts as a bridge to link the two, has also incurred much attention. The goal of this paper is to understand this link by investigating the chemical component and microphase structures of asphalts using molecular simulations. The difference in performance of asphalts is now mostly attributed to the chemical component. It is reported that longchain n-alkanes form strong molecular interactions to lead cracking at a low temperature, so that they crystallize inside the asphalts.2 Large molecules as well as large polyaromatic systems give higher molecular interactions and, thus, a higher viscosity to bitumen.3 Even asphalts with the same upper temperature of performance grade (PG), a rheological specification proposed by the U.S. at the end of the Strategic Highway Research Program (SHRP) in the early 1990s, exhibit different strains after repeated stress loadings.4 It has sometimes been assumed that once we know the chemical component of asphalts, we will be able to predict its performance and specify the properties of “good” asphalts. This mysterious and very complicated relationship could not yet be fully understood without a detailed chemical component. © 2014 American Chemical Society

Received: September 26, 2014 Revised: December 16, 2014 Published: December 19, 2014 112

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Energy & Fuels images of asphalts by heat-casted samples. Loeber et al.9 first observed the “bee structures”, and Carrera et al. believed those special structures had a relationship with rheological properties of asphalts.10 Lyne et al.11 reported that the three phases had different Young’s modulus and adhesive forces, which depended upon the chemical composition. Pauli et al.12 suggested that the well-known “bees” were a result of the interactions between crystallizing paraffin waxes and the remaining asphalt fractions. Lyne et al. offered that some asphalt with a wax content of 0.6% did not have the typical beelike structures. It was also reported early that bee-like structures were related to asphaltene colloidal particles.13 However, the cause of the “bees” and their impact on the properties of asphalts is still unknown. The links between the chemical component and mechanical properties of asphalts still remain elusive, despite over half a century of intensive research in the two and the recent progress of microscopic characterization of asphalt binders. The microphase structure of asphalt was regarded as a bridge between the chemical component and performance, such as the “bee structures”. However, this microscale could not be fully understood by traditional techniques. More recently, molecular simulations, as powerful computational material methods being promoted by MGI, have brought new hope to this field. This promising skill was used to predict the properties, damages, and modifications of asphalts at the molecular levels. The first molecular model was proposed by Zhang and Greenfield.14 They adopted N−C22, 1,7-dimethylnaphthalene, and asphaltenes to combine the asphalt systems, while the asphaltene molecules were from studies by Artok et al.15 and Groenzin and Mullins.16 The models were used to predict intermolecular packing characteristics of asphaltene molecules, the properties of asphalts,17−19 and the modification mechanism of the copolymer of styrene butadiene styrene (SBS)-modified asphalts.20 Bhasin et al. predicted the self-healing property of asphalts by this three combined Z&G model.21 Subsequently, Hansen et al.22 proposed a new four-component model by adding new resins and resinous oil molecules, which acquired quite different dynamic properties from those of the Z&G model. Lemarchand et al. obtained chemical aging characteristics of COOEE bitumen by the Hansen model.23 Those results showed that different molecular structures as input parameters heavily influenced the properties prediction. Later, Li and Greenfield24 proposed an improved model. The new model made significant progress in showing molecular diversity of asphalts and acquired better agreement with experimental results, but it did not deal well with sulfur content and aromaticity. This could cause some error to estimate properties and mechanisms. Except combined models, average molecular models were also used to explore the surface characterization of asphalt and aggregate, i.e. Yang Lu and Linbing Wang.25 They investigated the deformation and failure properties of the interface of the asphalt-aggregate. In comparison to the two methods, the acquired computer resources of the average models are smaller than the computer resources of the combined models. The average models might have merits to establish a coarse-grained model to obtain a large time scale for properties estimate, but it cannot display the molecular diversity of asphalt binders, especially in describing the interactions of SARA fractions and its contribution to failure or modification of the damage mechanism. In this paper, the combined models are selected to investigate the contribution of fractions to the “bee structures”

and its arrangement within the whole asphalts, which have not been fully studied by previous works. The objective of this paper is to investigate the interactions of SARA fractions and some special subfractions in asphalts by molecular simulations to gain a deeper comprehension of its microstructure, especially about the formation of asphalt topography by AFM−PDM. The investigated interactions by simulations included the dominated intermolecular force, the miscibility of asphaltenes to maltenes and resins to oils, and the arrangement of SARA fractions as well as its subfractions, i.e., sulfuric compounds, fused ring compounds without long side alkyl, and long-chain alkanes. In this paper, the combined models with diversity molecules were selected to investigate the interactions of different fractions in asphalts. The average molecular formula of every fraction could be obtained by modern techinques, but every fraction was composed by millions of molecules. However, it was impossible to gain detailed molecules of asphalts even by modern separation techniques; therefore, the models with similar microcharacterization of three specific asphalt binders were built by representative molecules from the literature. The verification of models was conducted by element composition, SARA fraction analysis, structure parameters, solubility parameters, density, and topography. This work will provide guidance to understand fully the colloidal structure of asphalt binders and to establish the structure−performance relationship of asphalt binders in the future.

2. EXPERIMENTAL AND SIMULATION DETAILS 2.1. Experimental Details. Three asphalt binders were selected to investigate their microcharacteristics (Table 1). The binders with a

Table 1. Conventional Properties and PG of Three Asphalt Binders binder

penetration at 25 °C (dmm)

softening point (°C)

PG level

Q70 P90 H90

70 83 81

46.5 48.5 47.5

58-22 58-22 58-22

similar content of asphaltenes were selected in this paper for eliminating the impact of the asphaltene content on the surface topography of asphalts by AFM. Therefore, the selected asphalts had a different penetration grade but the same PG level. The experiment is aimed to verify if the proposed molecular models have a similar microcharacterization with the selected asphalt binders. The investigations included element composition, SARA four fraction analysis, structure parameters, solubility parameters, and surface topography by AFM. A Vario EL-III elemental analyzer was used to examine C, H, S, and N contents and decomposition temperature of 950−1200 °C. SARA fraction analysis was conducted by the Corbett method, and n-heptane was used as the precipitated solvent of asphaltenes. Structure parameters were obtained by improved Brown− Ladner (B−L) methods with proton nuclear magnetic resonance (1H NMR), gel permeation chromatography (GPC), and Fourier transform infrared spectroscopy (FTIR). The equations of B−L methods are shown in Table 2. The aromaticity ( fa), describing in the ratio of aromatic carbons/total carbons in asphalt, was from 1H NMR and the aliphatic chain length (L) from FTIR. 1H NMR spectra were performed on a Bruker Advance DMX500 spectrometer in DCCl3 (99.5%), with tetramethylsilane (TMS) as the internal standard. Molecular weights were obtained by GPC on Waters 1515 gel filtration chromatography, with tetrahydrofuran (THF) as the solution. The sample for FTIR was prepared by the infrared window Daub method on a Thermo Nicolet FTIR instrument, with KBr as the window material, and scanned 32 times. The surface topography of 113

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Energy & Fuels Table 2. Structure Parameters of Asphalt Binders by B−L Methods26 parameter

equation

parameter

molecular weight, Mn (g/mol) total hydrogen, HT (%) total carbon, CT (%) aromatic carbon, CA (%) naphthenic carbon, CN (%)

from GPC HT = H% × Mn/MH CT = C% × Mn/MC CA = CT × fa CN = 4RN

paraffin carbon, CP (%)

CP = CT − CA − CN

aromatic rings, RA naphthenic rings, RN

RA = (CA − 2)/4 RN = RT − RA

total rings, RT aromaticity, fa naphthenic carbon rate, f N paraffin carbon rate, f P number of structural units in an average molecule, n ratio of the number of hydrogen to carbon without being replaced in aromatic rings, HAU/CA aliphatic chain length, L

equation

δ=

O

N+S C N+S 2.8 C

7.0 + 63.5fa + 63.5 C + 106 C + 51.8 H

O

− 10.9 + 12fa + 13.9 C + 5.5 C −

(1)

2.2. Simulation Methods. The multi-component molecular models were proposed in this paper. The choice of molecules for a model asphalt system is to represent the different polarities and sizes that are found in the SARA four fraction solubility classes. The specific choices of molecules and quantities of each type of molecule to fit experimental data are described in section 3. The dominated intermolecular force, the miscibility of asphaltenes to maltenes and resins to oils, and the arrangement of SARA fractions and their subfractions were investigated by molecular dynamics (MD) and Monte Carlo (MC). The dominated intermolecular force was inspected by solubility parameters (δ), and the miscibility of asphaltenes to maltenes and resins to oils was examined by Flory− Huggins parameters (χ). MD was used to obtain the reliably values of δ, χ and visualizing a shot photo of the molecular models. MC was to examine the volumetric data by the task of atom volume and surface. The volumetric data provided the topography of the potential fields and the distribution of free volume, which all depended upon the intermolecular interactions. The value of δ is defined as the root of cohesive energy density (CED), where CED is the ratio between the calculated cohesive energies, Ecoh, and volumes, V.27 The calculated value of δ is from eq 2, and Ecoh = −⟨Einter⟩ = ⟨Eintra⟩ − ⟨Etotal⟩. The symbol of Einter is the total energy between all molecules; Etotal is the total energy of a system; Eintra is the intramolecular energy; and the brackets ⟨...⟩ represent an average over a NPT or NVT ensemble.

δ=

CED =

Ecoh V

(3)

⎛ ΔE ⎞ V mix ⎟⎟Vm = m (δA − δ B)2 χ = ⎜⎜ ϕ ϕ RT RT ⎝ A B⎠

(4)

L = Cp/NCH3, NCH3 = (CT − CA)/(2 + NCH2/NCH3), NCH2/NCH3 = 2.93(A1460/A1380) − 3.70

3. RESULTS AND DISCUSSION 3.1. Establishment and Verification of Molecular Models. 3.1.1. Establishment of Molecular Models. The molecular models were combined by representative molecules to investigate the interactions of SARA fractions. The detailed molecular compositions are shown in Figure 1, with simplified symbols given to every molecule. Four models were proposed for three specific binders shown in Table 2. Asphaltenes molecules of At-N + S + O and At−N + 2S + O in Figure 1 were provided by Takanohashi et al. from NMR as a kind of Kuwait asphalt,29 with the same oil resource as Q70 and H90. For sulfur-existing conditions, Woods et al. suggested more thiophenic sulfur than thiol sulfur by the highperformance liquid chromatography (HPLC) technique,30 so that only At-S was selected from the D&G model. R-Benzothio-S was the fruit of Coelho et al., who studied Middle East petroleum asphaltenes and resins.31 Originally, one contained a high-energy structure just shown in the red solid line cycle because of the so-called “pentane effect”;24 the red dotted line cycle did not have those adverse effects but also lead to high energy. The new structure, shown in molecule 5 of Figure 1, was adopted to replace the original structure in the asphalt

(2)

⎛E ⎞ ⎛E ⎞ ⎛E ⎞ ΔEmix = ϕA ⎜ coh ⎟ + ϕB⎜ coh ⎟ − ⎜ coh ⎟ ⎝ V ⎠A ⎝ V ⎠B ⎝ V ⎠mix

HAU/CA = (HA/HT + Hα/2HT)/[C/H − (Hα + Hβ + Hγ)/2HT = 3/CA* + 1/2

constant; T is the temperature of the simulation in kelvin; Mn is average molecular weight; and ρ is the density. It should be explicit that δA and δB are the cohesive interactions of the pure component in eq 3. However, there is no phase separation when the molecules of asphaltenes, maltenes, resins, and oils mix into one fraction. It demonstrates that those molecules in one fraction are completely soluble or the values of (Ecoh/V)mix for one fraction are approximately equal to zero. Therefore, it is hypothesized that the fractions, i.e., asphaltenes, maltenes, resins, and oils, are pure components. COMPASS force field was selected to conduct MD and MC. It is the first high-quality force field to consolidate parameters of organic materials, involving a Lennard−Jones 6-9 potential for the inter- and intramolecular dispersion−repulsion interactions.27 In this paper, intermolecular force mainly involved van der Waals (vdW) force and electrostatic (elect) Coulomb force, but the hydrogen bonding was neglected because little hydroxyl groups or strong electronegativity groups existed in asphalts to produce a large change of the total energy. MC and MD were conducted by Materials Studio 6.0 of Accelrys. The periodic cell was adopted. Dynamics of every molecular model was followed by steps 1−4: (1) energy minimization, (2) annealing stage with a decrease in the temperature from 200 to 500 K, with 5 heating ramps per cycle, (3) geometry optimization to ensure energy minimization, and (4) volume shrinking: T = 298 K, NVT ensemble, total simulation time of 0.5−1.0 ns, time step of 0.5 fs, and Nosé− Hoover thermostat; T = 298 K, P = 0.0001 GPa, total simulation time of NPT ensemble of 2−4 ns, time step of 0.5−1.0 fs, Nosé−Hoover thermostat, and Andersen barostat.

AFM−PDM was conducted by the Bruker Dimension FastScan. The asphalt films for AFM were obtained by the heat-casted method. A hot asphalt binder was casted into glass slides after it obtained better flowing, with a heating temperature of about 160 °C. Bitumen-covered sample holders were left overnight at room temperature before AFM testing was performed. The experimental solubility parameters (δ) were obtained by eq 1 from SHRP-A-675, which was based on wellknown group contribution methods. This method was chosen because it had small deviancy in its results. H

RT = CT + 1 − HT/2 − CA/2 fa = [C/H − (Hα + Hβ + Hγ)/2]/(C/H) f N = CN/CT f P = Cp/CT n = CA/CA*

The value of χ is calculated from ΔEmix or δ, according to eq 4 from the literature,28 where ΔEmix is the mixing energy of different molecules and is calculated from eq 3, ϕi is the volume fraction of a mixture, δ is the solubility parameters from eq 2, and Vm is the molar volume of asphalt binders. This value is Vm = Mn/ρ; R is the molar gas 114

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the old models and saved calculation resources, but R-benzothio-2S still performed the best. 3.1.2. Molecular Model Verification. Four models had similar mass percentage values of four fractions and element compositions, as seen in Table 4. Q1 and Q2 had composition similar to Q70; P was a model of P90; and H was a model of H90. The sulfur content was almost the same as the target values of the experiment. The mass percentage values of SARA fractions and the other element contents had slight errors, but the deviations were all within 2%. Again, it could be strange for the data of 70 and 90 in Tables 1 and 4 that 70 had the lower value of asphaltenes and lower penetration than that of 90. The phenomena were attributed to the different mass percentages of SARA fractions and average molecular weight. The asphaltene percentages of H90 and P90 were slightly higher than that of 70#, but the H90# had the slowest average molecular weight and P90 had the highest saturate content of all. In comparison to 70, the higher values of penetrations of 90 were ascribed to the higher content of light fractions and lower average molecular weight. In other words, there were more small molecules to dilute the large molecules; therefore, the relative hardness of 90 was smaller than that of 70 and the values of penetration were higher than that of 70. Structure parameters are used to confirm the content of structure units in a complex mixture. The basic units include alkyl, naphthenic base, and aromatic base for construction of a petroleum product. Structure parameters illustrated the relative amount of those units but not their arrangement; therefore, the topography was investigated by AFM, and volumetric data were investigated by MC. From Table 5, the three binders, Q70, P90, and H90, had the average molecular formula of C54H76O0.4N0.5S1.2, C56H82O0.3N0.5S0.2, and C47H66O0.2N0.2S1.2, respectively. The total atom numbers in one average structure were 132, 139, and 105, respectively. The values of molecular weight and total carbon were similar for all models and its counterpart binders, but the values of total hydrogen for Q1 and Q2 were slightly smaller than that for Q70. For aromaticity, high sulfur contents of Q70 and H90 were higher than the target values, while P90 and its models had similar values. Although the difference lead to a slight deviation of RT, RA, and RN, it just had one or two more rings in model systems for an

Figure 1. Representative molecules of asphalt molecular models: (1− 3) asphaltene molecules, (5−10) resin molecules, (11−14) aromatic molecules, and (15 and 16) saturate molecules. Molecules 1, 6, 8, 9, 10, 13, 14, 15, and 16 were all from the G&D model, and the others were new molecules.

molecular models. The aromatic molecules of A-2-benzo + S and A-1-benzene came from the research of high-boiling petroleum distillates of Kuwait crude oils by Lira-Galeana and Hammami32 and Al-Zaid et al.,33 respectively. The distillates of high-boiling petroleum were raw materials for asphalt processing and could exist in asphalt binders. Four asphalt models, named Q1, Q2, P, and H, respectively, were proposed to simulate the three asphalt binders (in Table 3). New models had more sulfur-regulated molecules than the D&G model,24 including A-2-benzo + S, R-benzo-thio-S, and Rbenzo-thio-2S. New aromatic molecules, A-2-benzo + S, hold a nature similar to R-benzo-thio-2S. Those molecules made it easy to regulate the sulfur content without significantly increasing atoms or molecules for a high-sulfur asphalt binder (as seen in Table 3), such as Q70 and H90. The new models kept the balance of polarity and aromaticity much better than

Table 3. Molecular Compositions of the Proposed Four Asphalt Molecular Models number in the model fraction

molecule

S-alkane S-cycle aromatics A-2-benzene A-3-benzene A-1-benzen A-2-benzo + S resins R-benzo-thio-S R-benzo-thio-2S R-benzo R-pyridino-1N R-quinolino-1N R-oxane-1O asphaltenes At−S At−N + S + O At−N + 2S + O total atoms of whole models saturates

Q1

Q2

P

H

molecular density

molecular mass

fa

S (%)

4 4 9 8 10 16 4 15 0 4 4 5 2 2 2 6384

3 4 7 6 6 12 4 11 0 4 1 3 3 1 1 4847

6 8 9 10 8 0 0 0 10 3 2 3 3 1 1 5002

5 4 7 6 3 14 4 12 0 2 1 3 3 1 1 4777

0.816 0.975 0.895 0.958 0.865 0.942 0.913 1.099 0.957 0.941 0.937 0.868 0.994 1.004 1.018

422.83 482.88 406.70 464.74 340.59 350.56 699.22 290.40 282.39 553.92 503.86 414.72 707.12 533.77 938.34

0.00 0.00 0.33 0.46 0.24 0.50 0.24 1.00 0.90 0.15 0.28 0.21 0.47 0.47 0.51

0.00 0.00 0.00 0.00 0.00 9.15 4.59 22.08 0.00 0.00 0.00 0.00 4.53 6.01 6.83

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Energy & Fuels Table 4. SARA Mass Fractions and Element Composition of Three Asphalt Binders and Models SARA mass fraction (%)

element mass percentage (%)

asphalt

saturate

aromatic

resin

asphaltene

C

H

O

N

S

H/C

Q1 Q2 Q70 P P90 H H90

9.58 11.18 10.47 23.05 24.70 14.27 13.03

43.33 41.51 42.16 39.74 39.80 40.81 42.53

35.57 34.77 35.99 24.27 22.30 32.24 30.68

11.52 12.55 11.38 12.94 13.20 12.68 13.76

84.54 84.69 83.83 87.95 87.25 84.33 83.67

9.72 9.76 9.91 10.71 10.76 9.71 9.94

0.38 0.28 0.91 0.29 0.53 0.28 0.54

0.44 0.34 0.43 0.35 0.88 0.25 0.39

4.92 4.93 4.92 0.69 0.58 5.43 5.46

1.37 1.37 1.41 1.45 1.47 1.37 1.42

Table 5. Solubility Parameter and Structure Parameters of Three Asphalt Binders and Models parameter

Q1

Q2

Q70

P

P90

H

H90

δ (J/cm3)−0.5 Mna (g/mol) HT (%) CT (%) CA (%) CN (%) CP (%) RT RA RN fa fN fP n Lb

18.13 788 76.01 55.48 21.64 11.01 22.84 8 5 3 0.39 0.20 0.41

17.85 787 76.17 55.47 21.08 12.31 22.08 8 5 3 0.38 0.22 0.40

19.31 776 84.02 56.14 17.4 10.31 28.42 6 4 3 0.31 0.18 0.51 1.36 3.07

18.10 771 81.97 56.49 18.08 13.81 24.61 7 4 3 0.32 0.24 0.44

18.19 774 83.03 56.23 19.52 6.28 30.43 6 4 2 0.35 0.11 0.54 1.02 3.10

18.05 623 59.98 43.72 17.05 9.77 16.90 6 4 2 0.39 0.22 0.39

19.16 676 66.99 47.09 14.98 15.46 16.65 7 3 4 0.32 0.33 0.35 0.62 2.02

a The value of Mn for testing came from GPC, while the value for the models based on the definition of Mn was the ratio of the total molecular weight of the whole model to the molecular number, which was the ratio of total atoms in whole models in Table 3 to the total atom number in one average structure. bThe values of L were from FTIR, shown in the Supporting Information.

Figure 2. Energy and density variation of models, with results coming from NPT after 2 ns volume shrinking by NVT.

average molecular structure. The values of solubility parameters were slightly lower than the target values. However, deviations of solubility parameters were all below 2 (J/cm3)−0.5, which demonstrated that the models had polarity similar to the systems of the real binders. Therefore, the models had power to accomplish effective simulation. The topography of real asphalt binders and their models were also investigated by AFM−PDM and the potential field distribution from volumetric data. The topography of a heatcasted asphalt by AFM−PDM is typically composed of three phases: “bee-like” structures (catana phase), the peri phase, and the para phase. The catana phase was regarded as a dispersion phase, while the other two phases combined as a continuous

phase. The distribution of those three phases mainly depended upon the intermolecular force between the probe and the surface of measured asphalts. The potential field distribution by volumetric data also demonstrated this force in different fractions of asphalts; therefore, it guaranteed that the models complied with the experimental results of the specific asphalt binders if the two images also had similar rules. The premise of effective potential field distribution images was that the model systems relaxed to the desired state point. Figure 2 illustrated that the systems reached satisfactory stable stages, and the densities of the models were all around 1 g/cm3, similar to the target values from experiments. 116

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Energy & Fuels Figure 3 presented the images from AFM and the simulated results. The main factor to produce “bee structures” was still

Table 6. Total CED, van der Waals CED, and Electrostatic CED of Asphalt Models model

CEDtotal (×106, J/m3)

CEDvdW (×106, J/m3)

CEDelect (×106, J/m3)

Q1 Q2 P H

321.69 319.77 320.59 320.38

321.00 316.00 317.05 318.30

0.06 1.48 1.14 0.75

controlled the interactions of SARA fractions and their subfractions, but the impact of Coulomb interactions was minor. Murgich et al.34 also attributed the majority of the potential energy to vdW (70%) rather than Coulomb interactions (20−30%) or hydrogen bonding (5−10%). Nevertheless, the values of CEDtotal were not strictly equal to the sum of CEDvdW and CEDelect. Some accumulated errors could be responsible for this small discrepancy, because every value was from the values of energy. Another interesting thing to note was that the calculated values of solubility parameters in Table 5 did not differ largely and all fell below the experimental values. This phenomenon could have two explanations. The first explanation was blamed for the effect of heteroatoms, especially sulfur. Model P with a small sulfur content had the smallest deviation in all of the models. Li and Greenfield24 believed that N, O, and S should be responsible for low solubility parameters because of the small amounts of polar atoms that lead to a significantly low δp (nonpolar force) value. The second was that molecular modeling underestimated the strength of the attractive interactions in the real matters, and de Arenaza et al. declared that it would lead to calculated 10−20% below the experimental values in all cases of polymers.28 For asphalts, the deviation of δ was about 5−10% from the results of Table 5. They also pointed out that a similar negative deviation could not be eliminated even using large molecular models. In comparison of Q1 and Q2, Q1 had more molecules than Q2, which had smaller errors in solubility parameters than Q1, although it still had negative deviations. Weak attractive interactions definitely would underestimate solubility; therefore, much more work should be performed to improve this issue in the future. 3.2.2. Miscibility Properties between Asphaltenes to Maltenes and Resins to Oils. The Flory−Huggins interaction parameter (χ) is a measure of the strength of intermolecular contacts in intimately mixed blends. If mixing of two materials lead to a decrease of the free energy of the whole system, the mixture system must be more stable, otherwise it would be separated to two phases. A positive value of the Flory−Huggins interaction parameter indicates immiscibility for blends, and the higher values of χ demonstrate more energy requirement in mixing. In this paper, the index was used to examine the interaction of asphaltene to maltene (marked χA−M) and resins to oils (marked χR−O), in which maltene was a mixture of saturates, aromatics, and resins, using the same models in Table 3. The models Q2, P, and H with similar total atom numbers were selected to research the miscibility of asphaltenes to maltenes and resins to oils of three binders, listed in Table 7. The results in Table 7 suggested that asphaltenes were partly soluble in maltenes and resins also had the same behavior in oils. Redelius et al.2 also provided that some of the molecules in the asphaltene fraction could be insoluble in the maltenes and actually be dispersed rather than dissolved. It showed that

Figure 3. Surface topography of asphalts by AFM−PDM methods named Q70, P90, and H90, and the scan size of big images was 50 μm, while that of small images was 10 μm. Three phases included in the images of asphalts by AFM were the “bee-like” structure, the peri phase (surrounding the bee structures), and the para phase (the remaining part minus the bees and peri phase). Topography of molecular models by the potential field distributions from MC after the systems reached satisfactory states named Q2, P, and H, at about 150 Å, and the values of the field ranged from 0.0 to 2.0, demonstrating the magnitude of potential energy of different molecules, while red, white, and blue represented small, medium, and large. The “bee-like” structure was regarded as a dispersed phase in the images by AFM, while the white−blue part in MC also had taken this job in molecular simulations.

not fully understood, but significant recent work on this phenomenon revealed that asphaltenes, waxes (a kind of longchain alkane), and the interactions between the two and the rest of the fractions could be responsible for the “bees”. For the images of AFM, the bee phase of Q70 was small in size but had larger numbers, which needed more energy to disperse those phases. H90 was the complete opposite, in which the disperse phase was large and irregular. However, P90 had a slightly large dispersed phase with uniform distribution. For the images of MC, the white−blue parts of Q2 and P were much greater than those of model H, just like the bee structures in Q70 and P90. The size of the white−blue parts of model P was diverse, but that of Q2 was relatively uniform. The white−blue parts could be a result of dispersed phases composed of large molecules, which clearly hold a higher energy, were hindered from movement, and then not easily gathered to form a large size. Small molecules contained less atoms and possessed low energy, which was easy to become a continuous phase in asphalt binders. This could be the reason for the different images obtained from AFM for the three binders. At this point, bee structures had more close relationship with the content of asphaltenes. Again, the same rules in topography by models and AFM and composition information suggested that the models had microstructure characterization similar to the real binders. 3.2. Interactions of SARA Fractions. 3.2.1. Dominated Force of SARA Fractions. Solubility parameters describe the attractive strength of the molecules in a material calculating from CED in this paper. CED was used to figure out the main intermolecular forces that exerted strong influence on the interactions of fractions. Table 6 summarized the contribution of different forces. The values of CEDvdW were largely higher than the values of CEDelect. This affirmed that the vdW force 117

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Energy & Fuels Table 7. Results of Flory−Huggins Interaction Parameters of Asphalt Molecular Models model

δasphaltene (J/cm3)−0.5

δmaltene (J/cm3)−0.5

VA−M (cm3/mol)

χA−M

δresin (J/cm3)−0.5

δoil (J/cm3)−0.5

VR−O (cm3/mol)

χR−O

Q2 P H

19.33 19.33 19.33

17.55 18.09 17.65

769.84 783.40 673.98

0.984 0.486 0.767

17.91 18.74 16.99

17.32 17.67 17.66

785.05 797.16 757.40

0.110 0.368 0.177

model P needed the smallest amount of energy when its maltene mixed with the same asphaltenes in MD; H took the second place; and Q2 was last. Although Q70 and H90 had similar sulfur contents, H90 had the smallest average molecules of all and more light fractions or higher ratio of maltenes to asphaltenes. Therefore, model H had a lower value of χA−M. In other words, the dispersed phase of Q70 had slightly worse miscibility than H70, but P90 was the best of all. Images by AFM were also a good proof to display the composition interactions and phase distribution produced by fractions (Figure 3). The smallest energy led the dispersed phase of P90 to increase in size and arrange uniformly. The value of χA−M for H90 was in the middle, but its average molecular weight was the smallest of all, which was favorable for the motion of all molecules and then obtained a scattered distribution of the “bee structures” in asphalts. For Q70, large Mn and χA−M hindered the molecular motions; therefore, the size of “bee structures” was extraordinarily small. The distribution of free volumes of models (Figure 4) also

Figure 5. Snapshots of asphalt molecular models by MD for the arrangement of SARA fractions. Image A was the whole arrangement of four fractions, while images B and C were the large asphaltene molecular bending. Image B showed the resin molecules inserted into the spaces of bending asphaltenes, and image C also offered the arrangement of resins, aromatics, and saturates.

representative molecules in models had no apparent discrepancy. Therefore, the unreasonableness of the resin molecules could exist in the models. New representative resin molecules of a large size should be added to molecular models in the future. Furthermore, resin molecules with large polarity and small steric hindrance inserted into the gap space of bending asphaltenes, and long alkyl demonstrated similar behavior, in panels B and C of Figure 5. For long-chain alkane, another interesting finding was that saturates were not completely in the outermost region of the colloidal structure, described in panels A−C of Figure 6. Some

Figure 4. Distributions of free volumes of models from volumetric data by MC. The volumes of a defective material in a given condition are composed of substantial volumes, free volumes, and defective place (or gap space). The gray part demonstrated substantial volumes; blue is free volumes; and white is gap space.

demonstrated the possibility degree of molecular motions. The free volumes of H, Q2, and P decreased in turn. It was, therefore, more space for the molecular motions of P to gain large size “bee structures”. However, the values of χR−O were reverse, therefore, the numbers and size of “bees” were very different in the three binders. 3.2.3. Arrangement of SARA Fractions and Special Compound in Asphalts. In this part, three questions were investigated to fully understand the colloidal models and the formation of “bee structures” of asphalts, i.e., the arrangement of SARA fractions, the arrangement of sulfuric compounds, fused ring compounds without long side alkyl chains and longchain alkanes, and the impact of aggregation of the asphaltenes on the distribution of “bee structures”. For the arrangement of SARA fractions, according to noteworthy colloidal models, asphaltenes dispersed in maltenes, which were composed of resins, aromatics, and saturates. The results by MD in Figure 5 suggested that asphaltenes were the core, resins adhered to asphaltenes, and aromatics as well as saturates were surrounded by the two fractions; however, the resins were not all glued to asphaltenes. Some resin molecules mixed with aromatics because molecular sizes of their

Figure 6. Snapshot of asphalt molecular models by MD for the arrangement of subfractions: long-chain alkane showed in panels A−C, sulfuric compounds colored by orange and red listed in panel D, and condensed aromatic molecules colored by light yellow without a long side alkene chain in panel E.

long alkyl chains were interspersed in the gap between resins and asphaltenes, which could be one of reasons for the formation of bee structures in Figure 4. Ungerer et al.35 pointed out that the alkyl chains had a steric repulsion effect, which supported that those long-chain alkanes could have behaviors similar to adhere to the microzone of alkanes in asphaltene regions. Some long-chain alkanes acted as lubricants in the large-energy area of molecular assembles to decrease the energy 118

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Energy & Fuels of whole systems and obtain a system with a minimized energy and a stable state. Different compositions gained different physical and mechanical properties. The works from Lyne et al.11 also supported this conclusion, by reporting that the region surrounding the bee phase and the region in the bee areas had lower values of adhesive force than the para phase but higher Young’s modulus. It was clear that aromatic rings had higher stiffness than alkyls; therefore, the region in the areas of the bees had higher Young’s modulus. Figure 6D showed the behaviors of sulfide and condensed fused ring compounds. R-Benzo-thio-2S was a sulfuric molecule without a long alkyl side, which adsorbed into large molecules or adjoined to other sulfides with a large size, such as A-2-benzo + S. R-Benzo as an example of fused ring compounds without long side alkyl chains had behaviors similar to R-benzo-thio-2S. It also dispersed around asphaltenes or large resin molecules but not far away, as R-benzo-thio-2S did in Figure 6D. RBenzo-thio-2S should be blamed for the low solubility parameters for its small volumes and high polarity, which could be hard to be miscible with other molecules in this model. R-Benzo-thio-S with long alkyl chains in saffron yellow had no chance to access asphaltenes because of large molecular volumes or the steric effect. A-2-Benzo + S was a kind of representative aromatic molecule, where the closed molecules adhered to resins because of higher polarity and small size. Sulfuric molecules with low nonpolar force could be adverse to misciblity. Model P had lower errors than models with a high sulfur content when calculating the solubility parameters. In summary, all of the molecules were arranged by their polarity and volume. Small molecules with a high polarity were easy to move to gap space and be attracted by large molecules, but the large molecules were comparatively motionless. If there were no molecules like those as small as R-benzo-thio-2S and Rbenzo in asphalts, small molecules in saturates would also have the chance to insert into the gap of large molecular assembles. For the impact of aggregation of the asphaltenes on “bee structures”, one asphaltene model without a heteroatom was proposed to display the state of no aggregation because it was inevitable for asphaltene aggregation in vacuum without other solvents, as shown in Figure 7C. Greenfield supported that asphaltenes without heteroatoms could not produce aggregations.36 The new asphaltene models without heteroatoms in Figure 7C were proposed, in which the molecular structure were the same as the asphaltene molecules in Table 3, but all heteroatoms were displaced by carbon. The snapshots A−C in Figure 7 of aggregation and dissolution also implied that the heteroatoms had the main role on the aggregation of the asphaltenes. However, the images from a potential field had no large difference in the different aggregations and dissolutions. The results affirmed that there were very poor correlations between the asphaltene aggregations and the bee structures because the asphaltenes also aggregated into colloidal particles in asphalts. A similar molecular structure had a similar potential energy in MD. Masson et al.37 also reported there was no close relationship with the asphaltene aggregations and the bee structures, who carried out an extensive AFM study on 12 different types of SHRP bitumen to further investigate the bee structures. More work is still needed to understand fully this question in the future.

Figure 7. Snapshots of asphaltene aggregations and dissolutions. Parallel to the plane aggregations are shown in panel A, and the aggregations perpendicular to the plane are shown in panel B. The dissolution listed in panel C was the control groups with the same molecular structure, but all heteroatoms were displaced by carbon. The images of panels D−F were the distributions of the potential field with panels A−C, respectively.

half a century of intensive research and the recent progress of microscopic characterization. The microphase structure of asphalts as a bridge between the two has also incurred a lot of attention, especially about the “bee-like structures” by AFM. In this paper, the combined molecular models were proposed for three specific asphalt binders, and the ability of molecular modeling based on the COMPASS force field to research the interactions of SARA four fractions and their subfractions had been investigated to understand fully the arrangement of fractions and the formation of the “bee-like structures”. For the arrangement of fractions, the SARA fractions basically obeyed the traditional colloidal theory that the asphaltenes as a core dispersed in maltenes, with resins, aromatics, and saturates arranged in turn. The results from the work here showed that long-chain alkenes of saturates were not always in the outermost region of the colloidal structure; some inserted into the gap region of the asphaltene molecules and adhered to the aliphatic area of asphaltenes. Sulfuric compounds with large polarity and small steric hindrance clung to aromatic rings of asphaltenes and other large molecules. The condensed aromatics without long alkyls had behaviors similar to those of sulfides. Murgich38 found that alkyl side chains disrupted the intermolecular packing and energetics in classic aggregations of asphaltenes, and Brandt et al.39 noted the difficulty of stacking asphaltene molecules because of steric hindrance between pendant alkyl chains. At this point, long-chain alkanes displayed the same behaviors as alkyl side chains to insert into the asphaltene molecules and decrease the potential energy. Sulfuric compounds and condensed aromatics also adhered to the region of fused rings to reach a stable state with minimized energy. Those special behaviors resulted in different topographies in the bulk and surface of asphalts. For the formation of the “bee-like structures”, the results here revealed that asphaltenes were the main factor to obtain the bee structures but the long-chain alkanes played as an inductive agent to prompt the distribution of those special structures. Das et al.40 reported that the measured fraction of long-chain alkanes did not correlate well with the observed percentage of bees, but their behavior was as an inducer to

4. CONCLUSION The mysterious correlation between the chemical component and mechanical properties of asphalts is still elusive after over 119

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Energy & Fuels impact the “bee structures”. This could explain why some asphalts containing waxes and little asphaltenes had no bee structures and bitumen binders with a higher wax content had more “bees”.10 Asphaltenes served as a core to form the bee structure, but small molecules with large polarity and small steric hindrance absorbed to the core and impacted the size and distribution of “bee structures”. Those special microstructures gave rise to a rich and complex behavior of an asphalt binder. Dourado et al.41 observed that the overall elastic modulus of the region containing the bees was much lower than the region observed for the smooth surface matrix. The bright areas of the bees presented a lower elastic module than the overall bee area, which, in turn, exhibited a lower elastic modulus than the matrix. Lyne et al.11 and Yu42 all believed that Yong’s moduli in the region surrounding the “bees” and in the “bees” were higher than the modulus of the smooth matrix, but the adhesive force measured had opposite rules. In summary, “bees” had close correlation to asphaltenes and long-chain alkenes. Furthermore, solubility parameters suggested that vdW was the main force to control intermolecular interactions of asphalt binders. The calculated solubility parameters by MD based on the COMPASS force field had 5−10% negative deviations because of the underestimated strength of the attractive interactions in modeling systems compared to the real world. A high sulfur content was adverse to predict solubility parameters of asphalt binders. It needs more work to modify the force field to fit the asphalt materials. However, molecular modeling based on the COMPASS force field provides many clear images of SARA four fraction interactions, which might be a powerful tool to research the failure damage mechanism and modification of asphalt binders in the future.

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ASSOCIATED CONTENT

* Supporting Information S

Results of the GPC curve for three asphalt binders (section 1), 1 H NMR spectrum of three asphalt binders (section 2), results of FTIR (section 3), annealing process (section 4), and geometry optimization test of molecular simulation by Forcite (section 5). This material is available free of charge via the Internet at http://pubs.acs.org.

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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 This work was sponsored by the China National Natural Science Foundation (51278159 and 51478154), which was aimed at numerical simulation in a micro-/mesoscale. The software was supported by the Harbin Institute of Technology.

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REFERENCES

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