Volume 2022, Issue 1 5318906

Research Article

Open Access

Computational Study on the Microscopic Adsorption Characteristics of Linear Alkylbenzene Sulfonates with Different Chain Lengths on Anthracite Surface

Xuanlai Chen,

Xuanlai Chen

orcid.org/0000-0002-1099-5449

School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China tyut.edu.cn

Search for more papers by this author

Guochao Yan,

Corresponding Author

Guochao Yan

[email protected]

orcid.org/0000-0002-2829-1909

School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China tyut.edu.cn

Search for more papers by this author

Guang Xu,

Guang Xu

Department of Mining Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA mst.edu

Search for more papers by this author

Xianglin Yang,

Xianglin Yang

Hunan Provincial Key Laboratory of Fine Ceramics and Powder Materials, School of Materials and Environmental Engineering, Hunan University of Humanities,Science and Technology, Loudi 417000, China huhst.edu.cn

Search for more papers by this author

Jinfa Dong,

Jinfa Dong

School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China tyut.edu.cn

Search for more papers by this author

Zidong Liang,

Zidong Liang

School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China tyut.edu.cn

Search for more papers by this author

Shuai Wei,

Shuai Wei

Taiyuan Research Institute Co.,Ltd., China Coal Technology & Engineering Group, Taiyuan 030006, China

Search for more papers by this author

Xuanlai Chen,

Xuanlai Chen

orcid.org/0000-0002-1099-5449

School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China tyut.edu.cn

Search for more papers by this author

Guochao Yan,

Corresponding Author

Guochao Yan

[email protected]

orcid.org/0000-0002-2829-1909

School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China tyut.edu.cn

Search for more papers by this author

Guang Xu,

Guang Xu

Department of Mining Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA mst.edu

Search for more papers by this author

Xianglin Yang,

Xianglin Yang

Search for more papers by this author

Jinfa Dong,

Jinfa Dong

School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China tyut.edu.cn

Search for more papers by this author

Zidong Liang,

Zidong Liang

School of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China tyut.edu.cn

Search for more papers by this author

Shuai Wei,

Shuai Wei

Taiyuan Research Institute Co.,Ltd., China Coal Technology & Engineering Group, Taiyuan 030006, China

Search for more papers by this author

First published: 22 February 2022

https://doi.org/10.1155/2022/5318906

Citations: 1

Academic Editor: Mohd. Sajid Ali

Share a link

Email
Wechat
Bluesky

Abstract

In order to explore the influence of different lengths of hydrophobic carbon chains on the diffusion characteristics of surfactants on the surface of anthracite, six linear alkyl benzene sulfonates with different hydrophobic carbon chain lengths were selected (mC, m = 8, 10, 12, 14, 16, 18; m represents the numbers of carbon atoms in the hydrophobic carbon chain), and molecular dynamics (MD) simulations were adopted. Models of surfactant-anthracite, surfactant-graphite layer, and water-surfactant-anthracite were constructed. After analyzing a series of properties such as adsorption energy, diffusion coefficient, radial distribution function (RDF), and hydrophobic tail order parameters, it was found that 12C had the highest adsorption strength on the surface of anthracite; the reason was that 12C had the highest degree of aggregation near the oxygen-containing functional groups on the surface of anthracite. Further studies had found that the hydrophobic tail chain of 12C had the strongest isotropy. The study fills the gap in the systematic study of the diffusion characteristics of linear alkylbenzene sulfonates (LAS) with different chain lengths on the surface of anthracite, enriches and develops the basic theory of coal wettability, and also provides technical ideas for the design of new surfactants and new dust suppression agents.

1. Introduction

Surfactants are amphiphilic compounds that contain both hydrophilic and hydrophobic groups. This special structure makes it one of the most versatile chemical products [1–8] (e.g., in the oil industry, cosmetics, coal industries, and polymer paint). In recent years, surfactants have also been widely used in high-tech fields. Such as electronic printing, microelectronics technology, nanomaterials, and biopharmaceutical research [9–17]. From the overall consumption of global surfactants, the consumption of anionic surfactants is the largest [18]. From the aspects of surfactant applicability, cost performance, and overall consumption level, anionic surfactant linear alkylbenzene sulfonates (LAS) are the most important surfactant series in the world [19–22].

LAS consists of a hydrophobic linear alkyl chain and a hydrophilic sulfonate group, which are, respectively, connected to the 1, 4 positions of the benzene ring [23]. Commercial LAS surfactants are usually complex mixtures of homologs [24] (i.e., different lengths of the alkyl chain). Heterogeneous components improve performance, making LAS widely used in different industries, such as cosmetics, detergents, and mineral extraction [25–28]. However, this heterogeneity also makes it more complicated to clarify the detailed functions of the pure LAS. So far, LAS mixtures have been extensively studied [29–33], but systematic studies on pure LAS are relatively rare.

Surfactants have been widely used in the coal industry, such as in the fields of coal flotation and coal dust suppression [34–41]. The research on the interface characteristics of coal relies on the basic theory of coal wettability [42–44]. A large number of experimental studies have shown that the surface active agent diffusion characteristics on the coal surface determine the wetting effect of coal [45–50]. However, the experimental methods have defects in explaining the structural changes, dynamics, and energy characteristics of the molecular adsorption process on the mineral surface.

With the development of science and technology, it is becoming a reality to use computer simulations to explain the macroscopic properties from a microscopic point of view [51–59]. In particular, MD simulation has proven to be a useful tool for analyzing coal interface properties at the molecular level [60–66]. Traditional experiments screen flotation agents and dust suppression agents by trial-and-error method. MD simulation can screen agents in batches more efficiently, save a lot of time and materials, and also help to understand the adsorption mechanism from a molecular perspective. So far, researchers have conducted a large number of MD simulation studies on the diffusion characteristics of surfactants on the coal surface [44, 67–71]. However, there are few researches on the diffusion characteristics of LAS on coal surface; moreover, there is a lack of research on the mechanism and structure of the chain length in the adsorption process. We studied the adsorption characteristics of the four isomers of the linear alkylbenzene sulfonate family (LAS) on the coal surface and systematically compared the adsorption characteristics and microscopic adsorption mechanism of the LAS family members on the coal surface. [71].

In the existing research on the influence of different hydrophobic tail chain length on the properties of surfactants, researchers mostly choose two or three surfactants to compare; most of the conclusions drawn are that various properties change linearly as the length of the hydrophobic alkyl chain changes [46]. We think this is unreasonable, because the selected types of surfactants are too few to get a comprehensive conclusion. Therefore, in this study, we selected six surfactants with hydrophobic chain length m = 8, 10, 12, 14, 16, 18 for research. Since the LAS with even number of carbon atoms in the hydrophobic chain are more widely used and the odd-even effect [72] causes a large difference between the odd-numbered carbon atoms and the even-numbered carbon atoms, we selected the LAS with even number of carbon atoms in the hydrophobic tail chain for research. As far as we know, this is the first attempt to systematically study and compare the diffusion characteristics of LAS family members with different carbon chain lengths on the surface of anthracite.

Surfactants with different hydrophobic chain lengths have different properties, which arouses the interest of a wide range of researchers. Acisli et al. [73] studied the adsorption capacity of five cationic surfactants with different hydrophobic chain lengths on montmorillonite and found that the hydrophilicity decreased with the increases of chain length. Ren et al. [74] study the adsorption behavior of different chain lengths Gemini surfactants on methyl orange and find that, with the increase of the chain lengths, the interlayer spacing, hydrophobicity, and dispersibility of organoclay increase significantly. Kun et al. [75] study the hydrophobic effect of three cationic surfactants with different hydrophobic chain lengths on bovine serum albumin and find that the initial step of the interaction is to attach the nonpolar carbon chain of the surfactant to the nonpolar region of the protein. Jiang et al. [76] study the effect of carbon chain lengths on the surface properties of salt-free cationic surfactants and conclude that the longer the hydrocarbon chain, the greater the hydrophobic effect. Díez-Pascual et al. [77] study the influence of surfactants with different chain lengths on the morphology of graphene and find that the thickness of the graphite flakes increases with the increase of the surfactant chain length, and the longer the molecular chain, the higher the surface coverage. Zhang et al. [78] use two Gemini surfactants (only the length of the hydrophobic tail is different) to study the adsorption of emerging pollutants after modified montmorillonite and find that an increase in the alkyl chain lengths can increase the mobility of molecules. Badache et al. [79] study the effects of different hydrophobic tail chain lengths on the interfacial properties of cationic surfactants and find that the surfactant with the longest chain has the lowest critical micelle concentration.

MD simulation was used for the calculation. Firstly, a binary system of surfactant-anthracite was constructed. The adsorption energy and diffusion coefficient of six LAS with different chain lengths were compared; a macroconclusion was drawn that the adsorption effect of 12C on the surface of anthracite was the best. Secondly a binary system of surfactant-graphite layer was constructed. Through coordination number and radial distribution function analysis, the microscopic reason for the highest adsorption strength of 12C was explained from the perspective of functional groups. In order to detect the tightness of the surfactant adsorption layer, a water layer was added to construct a water-surfactant-anthracite ternary system. Through the analysis of the RDF between oxygen atoms in the water and the order parameters of the hydrophobic tail chain, it was proved that the layer composed of 12C was the densest and had the strongest ability to “isolate” water molecules. This research fills the gap in the systematic study of the diffusion characteristics of LAS with different chain lengths on the surface of anthracite, expands the basic theory of wettability, and also provides ideas for the design and development of surfactants with new structures and new dust suppression agents.

2. Materials and Methods

2.1. Materials

This study selected the Jincheng anthracite that we had studied before (from the no. 3 coal seam of the Zhaozhuang coal mine in Jincheng). After hand-selecting and stripping the vitrinite, it was crushed and ground and passed through a 200-mesh sieve. The demineralized coal was obtained by pickling with HCl-HF-HCl. Carbonate, phosphate, clay, silica, and other minerals in coal were removed. The acid-treated coal sample was washed with excess distilled water until the pH of the filtrate became neutral. By measuring proximate analysis, ultimate analysis, carbon nuclear magnetic resonance (¹³C-NMR), high-resolution transmission electron microscopy (HRTEM), infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), a molecular model of the average structural unit of Jincheng anthracite was initially constructed. Table 1 shows the results of proximate and ultimate analysis of Jincheng anthracite [80].

1. Proximate and ultimate analysis of Jincheng anthracite.

Proximate analysis (%)				Ultimate analysis (%)
M_ad	A_d	V_da f	FC_d	C	H	O	N	S
0.66	23.05	12.86	67.05	91.51	3.89	2.10	1.71	0.79

M_ad: moisture content on an air-dried basis. A_d: ash content on dry basis. V_da f: volatile content (in ash-free form). FC_d: fixed carbon content.

Analyzing the carbon skeleton parameters obtained from the 13C-NMR test results of demineralized coal, it was found that the bridge carbon and the peripheral carbon were relatively high. This indicated that the degree of condensation of the benzene ring of coal molecules was relatively high. Therefore, the aromatic structure of Jincheng anthracite was dominated by fused-ring aromatic hydrocarbons formed by seven benzene rings, and the degree of substitution of aromatic rings was relatively high. Table 2 shows the structural parameters of Jincheng anthracite samples. Combining the analysis of coal samples with FTIR and XPS [80], Jincheng anthracite contained less nitrogen and exists in the form of organic nitrogen and inorganic nitrogen; the sulfur in coal samples mainly existed in the form of organic sulfur, with less inorganic sulfur. The oxygen content in coal was less in the aromatic structure, and the oxygen-containing functional groups included hydroxyl and ketone groups. Finally, two structural units S1 and S2 containing hydroxyl group and ketone group were constructed (Figure 1(a)). The surfactant molecules used in this study were six kinds of LAS with different carbon chain lengths (denoted by mC, where m represents the number of carbon atoms in the tail chain, m = 8, 10, 12, 14, 16, 18); they were sodium octaalkylbenzene sulfonate, sodium decaylbenzene sulfonate, sodium dodecylbenzene sulfonate, sodium tetradecylbenzene sulfonate, sodium cetylbenzene sulfonate, and sodium octadecylbenzene sulfonate. The reason for selecting them was that the six surfactants had the same hydrophilic head group, but the hydrophobic tail chain length was different. We used the controlled variable method to study the effect of surfactant tail chain length on adsorption characteristics.

2. Molecular structural parameter of JC anthracite coal sample.

x_b	C	σ + 1	P₀	B.L.	S.C.	MW	M_σ
0.46	27	3.0	0.40	1.2	1.8	398	20

Details are in the caption following the image — Open in figure viewer PowerPoint

x_b is the average value of the ratio of bridge carbon to peripheral carbon of aromatic compounds in coal (referred to as bridge carbon ratio); this value can reflect the degree of polycondensation of aromatic compounds, that is, the size of the aromatic nucleus. C represents the average number of carbon atoms in the aromatic layer. σ + 1 represents the average number of bridge bonds and side chains contained in each aromatic layer. P₀ represents the percentage of bridge. B.L. represents the number of bridges and rings in each aromatic layer. S.C. represents the number of side chains in each aromatic layer. MW represents the average molecular mass of the aromatic layer. Mσ represents the average molecular mass of the side chain or 1/2 bridge chain.

2.2. Computer Simulation Details

In this study, we used the molecular structure of Jincheng anthracite proposed earlier by our team to build a coal model [80], as shown in Figure 1(a). Forty optimized coal molecules (ratio 1 : 1) were randomly stacked in a cubic unit cell (35 × 35 × 35 Å3) through the Amorphous Cell module of Materials Studio 6.0. Annealing algorithm was used to relax the coal structure. The initial temperature of anthracite cell was set to 298K and then rose once every 50K until the temperature reached 1098K. Five annealing cycles were performed, and the final temperature was 298K [67, 81]. On the top of the coal seam, a 12 nm thick vacuum layer was added to prevent the influence of periodic boundary conditions. Figure 2 shows the constructed anthracite surface model.

Surfactant-anthracite, surfactant-graphite layer, and water-surfactant-anthracite system models were constructed. 10 surfactant molecules made up the surfactant layer, while 1000 water molecules made up the water layer. The length and width of the surfactant layer and the water layer are consistent with the coal model, both being 35 Å. In this way, the models can be matched when constructing surfactant-coal and water-surfactant-coal systems. The intermolecular interaction was described by the polymer uniform force field. Before the MD simulation, geometric optimization was used to minimize the energy of all models. Subsequently, NVT ensemble (the constant-temperature, constant-volume ensemble) was used for MD simulation. Nose was used for thermostat [82, 83]. The time step was set to 1 fs. 1000 ps simulations were conducted to make the systems interact with each other fully. In all MD simulations, the Ewald algorithm was selected for long-range electrostatic interaction, and the accuracy was 0.001 kcal/mol, and the atom based algorithm was selected for the van der Waals interaction, and the cut-off distance was 1.25 nm. A 12 nm vacuum layer was added to the surface of each model to eliminate the mirror effect [84]. In the display of all models, the sodium atoms were hidden to show the adsorption form of surfactants more clearly.

3. Results and Discussion

3.1. Surfactant-Anthracite System

The surfactant layer was first placed on the surface of the anthracite with the build layer tool, and the MD simulation of 1000 ps was performed. Under the action of van der Waals force and electrostatic force, the surfactant layer kept approaching the anthracite coal and finally completed the adsorption. The final adsorption configuration is shown in Figure 3. In order to highlight the adsorption form of surfactants, the coal surface model was displayed in a line model, and the surfactant was displayed in a CPK mode. The initial configuration can be viewed in the support material, as shown in Figures S1–S6.

3.1.1. Adsorption Energy

The magnitude of the adsorption energy value can measure the adsorption strength. The more negative the adsorption energy, the more energy released, the higher the degree of aggregation, and the higher the adsorption strength [37, 85, 86]. The adsorption energy is calculated by the following equations:

(1)

where EV stands for van der Waals adsorption energy, EL stands for electrostatic adsorption energy, E stands for total adsorption energy, E_total stands for the total energy of surfactant and anthracite, E_coal is anthracite energy, and E_surf stands for surfactant energy.

We use a Perl script to extract the energy directly. The average of the last 10 conformational energies was calculated to arrive at the data in Table 3. Comparing the total adsorption energy data from Table 3, the 12C-anthracite system’s energy was the lowest, indicating that the 12C released the most energy during the adsorption process, the adsorption strength was the highest, and the degree of aggregation on the anthracite coal surface was the highest. This was due to the different molecular structure of surfactants. These six surfactants had the same polar head group and different hydrophobic tail chain length. When the hydrophobic chain length m increased from 8 to 12, the hydrophobic interaction between the hydrophobic tail chain and the anthracite was enhanced, and it was easy to self-aggregate. At this time, due to the short length of the hydrophobic tail chain, the steric hindrance effect was not obvious yet (the steric hindrance effect mainly refers to the steric hindrance caused by the proximity of certain groups in the molecule; it is a kind of intramolecular tension). At this time, the π-π stacking effect between the benzene ring and the aromatic structure of anthracite in the surfactant was relatively obvious, but as the carbon chain becomes longer, this stacking effect weakens. The total adsorption energy showed a linear decreasing trend, which was the same as other previous studies. When m = 12, the adsorption energy was the most negative, indicating that the chain length had the greatest influence on the aggregation behavior of surfactants; at this time, under the combined effect of steric hindrance, hydrophobic interaction, and π-π stacking between the benzene ring and the aromatic structure of anthracite, the surfactant had the densest aggregation and the adsorption strength was higher than other systems. As the length of the hydrophobic carbon chain continued to increase, the intermolecular steric hindrance was enhanced, and the π-π stacking effect was weakened, which was not conducive to the aggregation of surfactant molecules, resulting in a linear increase in the total adsorption energy. At the same time, the hydrophobic interaction between the tail chain and the anthracite continued to increase. When m = 18, the influence of the hydrophobic interaction was dominant, causing the adsorption energy to decrease again.

3. Adsorption energy between surfactant and anthracite.

Model	EV (kcal·mol⁻¹)	EL (kcal·mol⁻¹)	E (kcal·mol⁻¹)
8C-anthracite	−268.26	−3.34	−271.60
10C-anthracite	−290.06	−2.47	−290.53
12C-anthracite	−366.75	−5.51	−372.26
14C-anthracite	−269.37	−3.18	−272.55
16C-anthracite	−251.78	−3.84	−255.62
18C-anthracite	−266.59	−3.79	−270.38

The components of adsorption energy were further analyzed [54, 84, 87]. van der Waals interaction played a leading role in adsorption process, and electrostatic interaction was so small that it can be ignored. The range of electrostatic interaction was several nanometers [88]; although the simulation model satisfies the distance condition, due to the high degree of coalification of anthracite, low oxygen content, and low electronegativity, the charge difference between anthracite and anionic surfactant molecules was very small [89]. Therefore, the electrostatic interaction between the oppositely charged atoms was very weak.

3.1.2. Diffusion Coefficient

To describe the dynamics properties of the collector, the mean square displacement (MSD) was calculated. Based on the MSD, the diffusion coefficient (D) was calculated according to (2)–(4). A smaller diffusion coefficient value suggests higher dynamics properties [90].

(2)

(3)

(4)

where N is the total number of surfactant molecules; r(t) and r(0) are the position vectors of the surfactant molecules at time t and t = 0, respectively; K is the slope of the MSD curve; that is, the diffusion coefficient D is 1/6 of the slope of the MSD curve.

As seen from Figure 4, the change trend of the diffusion coefficient was the same as that of the adsorption energy. The diffusion coefficient of 12C was the smallest. This was because the 12C had the strongest adsorption strength on the anthracite surface model, which limited its free diffusion ability. This result was consistent with the adsorption energy and contact surface area results. The diffusion coefficient of 8C was much greater than that of other surfactants. This was due to the shortest hydrophobic carbon chain of 8C, which led to the smallest hydrophobic interaction and the smallest intermolecular steric hindrance, resulting in the strongest free diffusion ability. This was consistent with the curvature analysis below.

3.2. Graphite Systems

Studies have found that oxygen-containing functional groups greatly affect the adsorption process [91]. In order to further explore the microscopic reasons for the highest adsorption strength of 12C on the surface of anthracite, the graphite layer was modified by hydroxyl and ketone groups, respectively.

Figure 5 shows models of the graphite layer modified by hydroxyl and ketone. The molecular structure of anthracite consists of an aromatic layer, which is very similar to the graphite flakes structure. Therefore, the surface of anthracite can be replaced by the surface of the graphite layer modified with oxygen-containing functional groups, which is more convenient and intuitive for the study of oxygen-containing functional groups [90, 92–96].

3.2.1. Adsorption Configuration

According to the construction method of the surfactant-anthracite system model in Section 2.1, 5 surfactant molecules were placed on the surface of the modified graphite layer, and the equilibrium was reached after 1000 ps molecular dynamics simulation. The final equilibrium configurations are shown in Figures 6 and 7.

3.2.2. Radial Distribution Function

The radial distribution function (RDF) between atoms can determine the degree of aggregation, and then by calculating the coordination number, the degree of aggregation of surfactants on the surface of anthracite can be quantified. The height of the first peak of RDF indicates the degree of order between atoms [25, 97–99]. The high and sharp peaks of RDF indicate strong order and strong interaction between atoms [100–104]. The radial distribution function was calculated by the following formula:

(5)

Figure 8 is the RDF between the oxygen atoms in the oxygen-containing group on the coal surface and the oxygen atoms in the surfactant.

As seen from Figure 8, the RDF peak of 12C was the highest near the coal surface hydroxyl groups, indicating that the interaction between 12C and coal surface hydroxyl groups was the strongest, and 12C aggregates most densely near the hydroxyl groups. The RDF peak of 12C near the ketone group on the coal surface was much higher than that of other surfactants, indicating that 12C also had the highest degree of aggregation near the ketone group. The RDF peak of 18C was the smallest near the ketone group and the hydroxyl group. This was because the 18C carbon chain was the longest, and the hydrophobic interaction between the hydrophobic carbon chain and anthracite was the strongest. At this time, the π-π stacking effect of the benzene ring and the aromatic structure of anthracite in the surfactant was the weakest. Although the steric effect was also the strongest at this time, according to the analysis of the adsorption energy, the hydrophobic interaction in 18C was dominant; this led to better interweaving of the hydrophobic tail chain with the coal surface, which made it more difficult for the oxygen atoms in the head group to contact with the oxygen-containing functional groups.

Generally, the coordination number refers to the number of B atoms around the A atom, which is a parameter for evaluating the strength of the interaction between atoms. Here, A represents the oxygen atoms in the oxygen-containing functional group, and B represents the oxygen atoms in the surfactants. The larger the value of the coordination number, the stronger the interaction between atoms. The coordination number was calculated by the following formula;

(6)

In the formula, V is the periodic model volume and N is the total number of B atoms. Figure 9 shows the results.

The coordination number result was consistent with the RDF analysis result, and the coordination number could reflect the result more intuitively. The coordination number of 12C near the hydroxyl group was 1.36, which was much higher than that of other surfactants, and the coordination number near the ketone group was 0.93, which was much higher than other surfactants. It showed that 12C had the highest degree of aggregation near the hydroxyl group and the ketone group. The coordination number of 18C near the hydroxyl group was 0.52, and the coordination number near the ketone group was 0.36, both of which were the lowest.

Through the abovementioned RDF and coordination number analysis, it was found that the reason for the highest degree of aggregation of 12C on the surface of anthracite was that it had the strongest ability to cover oxygen-containing functional groups on the surface of coal.

3.3. Water-Surfactant-Anthracite System

To further explore the the compactness of the layered structure and the relationship between the diffusion characteristics of surfactants in the aqueous solution, a water layer composed of 1,000 molecules was placed on the modified coal surface. After adding the water layer, the system became thermodynamically unstable. After the 1000 ps MD simulation, the system had regained equilibrium. Figure 10 shows the final conformation of the water-surfactant-anthracite system after MD simulation. After the surfactant layer covered the coal surface, it was equivalent to adding a water barrier layer [67]. The “isolation” effect made it difficult for water molecules to contact with anthracite. From Figure 10, the layered structure formed by 12C was the densest. The “isolation” ability of the layered structure to water molecules was used to evaluate the compactness of the layered structure. Furthermore, the microscopic reasons for the different degree of compactness of the layered structure were analyzed by the coordination number and RDF, the curvature of the hydrophobic tail chain, and the order parameters of the hydrophobic tail chain.

3.3.1. Radial Distribution Function

In order to determine the effect of the surfactant on the water molecules, the radial distribution function between the oxygen atoms (O_w − O_w) in the water molecules was calculated. As seen from Figure 11, the shape of the curve was not much different. Figure 11 showed that there was a peak between O_w atoms at about 0.29 nm, which was the average distance between oxygen atoms, but the peak heights were different. The RDF peak between O_w atoms in 12C was the lowest, indicating that the order of water molecules was the lowest in the system after 12C adsorption. It showed that the probability of finding O_w atoms around O_w atoms after 12C adsorption was the smallest at the same distance, indicating that the distance between O_w atoms was the largest. This was because the hydrophobic layer formed by 12C was the densest and had the strongest ability to “separate” the coal surface from the water layer, leading to more free diffusion of water molecules.

In order to compare the hydrophobic effect of the layered structure more intuitively, the coordination number between O_w was calculated, and the result is shown in Figure 12.

It can be seen that the coordination number of O_w after 12C adsorption was 10.07, which was the lowest among the six systems, which confirmed the above RDF conclusion. The change trend of coordination number was consistent with the above adsorption energy trend.

3.3.2. Hydrophobic Tail Order Parameters

The hydrophobic tail chain order parameter (S_CD) was used to measure the order of the hydrophobic tail chain, and then the adsorption strength of the hydrophobic tail chain on the surface of anthracite can be judged by calculation [23, 25, 72, 105]. S_CD was calculated using the following formula:

(7)

θ was the angle between the hydrophobic tail chain and the Z axis. The order parameter could vary from −1/2 to 1. When the value of S_CD was 1, the hydrophobic tail chain was arranged along the vertical direction of the interface. When the value of S_CD was −1/2, the hydrophobic tail chains were arranged parallel to the interface. When S_CD = 0, the tail chain distribution was isotropic [72, 106, 107]. The order parameters of different systems are shown in Figure 13.

The S_CD value of 12C was the closest to zero, indicating that its isotropy was the strongest. This also allowed its molecules to be combined from different directions, so that the molecules could be better interwoven together to form a denser layered structure. The S_CD value of 10C was the smallest, indicating that its molecular chains tended to align along the interface; that is, the tendency to “lie flat” on the coal surface was the strongest. The S_CD value of 14C was the largest, indicating that its molecular chains tended to be arranged perpendicular to the coal surface, that is, “standing” on the coal surface.

4. Conclusions

MD simulation method was used to study the diffusion characteristics of six kinds of linear alkylbenzene sulfonates with different hydrophobic chain length on the surface of anthracite. After analysis, the following conclusions were drawn:

(1)
By analyzing the adsorption energy and diffusion coefficient of the surfactant-anthracite system, it was found that the total adsorption energy of the 12C/anthracite system was the lowest; the diffusion coefficient of 12C was the smallest. It showed that 12C had the highest adsorption strength on the surface of anthracite.
(2)
Through the analysis of coordination number and RDF in the surfactant-graphite layer system, it was found that the reason for the highest adsorption strength of 12C on the surface of anthracite was that it had the highest degree of aggregation near oxygen-containing functional groups on the surface of anthracite.
(3)
Through the analysis of the coordination number and RDF of the water oxygen atoms of the water-surfactant-anthracite system, it was found that the layered structure formed by 12C in the solution was the densest, and the ability to “isolate” water was the strongest. After further analysis of order parameters, it was found that the 12C order parameter was the closest to zero, indicating that its isotropy was the strongest, which also allows its molecules to be combined from different directions, better intertwined, forming a denser layered structure, and better covering the coal surface.
(4)
The systematic study of linear alkylbenzene sulfonates with different chain lengths on the surface of anthracite can better understand the influence of surfactant structure on the adsorption strength. This research fills the gap in the systematic study of the diffusion characteristics of LAS with different chain lengths on the surface of anthracite, expands the basic theory of wettability, and also provides ideas for the design and development of surfactants with new structures and new dust suppression agents.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (Grant No. 51974195).

Open Research

Data Availability

The data used to support the findings of this study are included within the article.

Supporting Information

References

1 Zhang R. Y., Sun C. B., Kou J., Zhao H., Wei D., and Xing Y., Enhancing the leaching of chalcopyrite using acidithiobacillus ferrooxidans under the induction of surfactant triton X-100, Minerals-Basel. (2019) 9, no. 1.
Web of Science® Google Scholar
2 Liu Z.-Y., Wang C., Zhou H., Wang Y., Zhang L., Zhang L., and Zhao S., Characterizing the impact of surfactant structure on interfacial tension: a molecular dynamics study, Journal of Molecular Modeling. (2017) 23, no. 4, https://doi.org/10.1007/s00894-017-3285-0, 2-s2.0-85015400218.
10.1007/s00894-017-3285-0
Web of Science® Google Scholar
3 Qiu Y. S., Yu Y. F., Zhang L. Y., Qian Y., and Ouyang Z., An investigation of reverse flotation separation of sericite from graphite by using a surfactant: MF, Minerals-Basel. (2016) 6, no. 3, https://doi.org/10.3390/min6030057, 2-s2.0-84977098177.
10.3390/min6030057
Web of Science® Google Scholar
4 Chen P., Hu Y., Gao Z., Zhai J., Fang D., Yue T., Zhang C., and Sun W., Discovery of a novel cationic surfactant: tributyltetradecyl-phosphonium chloride for iron ore flotation: from prediction to experimental verification, Minerals-Basel. (2017) 7, no. 12, https://doi.org/10.3390/min7120240, 2-s2.0-85038562531.
10.3390/min7120240
Web of Science® Google Scholar
5 Huang G., Xu J., Geng P., and Li J., Carrier flotation of low-rank coal with polystyrene [J], Minerals-Basel. (2020) 10, no. 5, https://doi.org/10.3390/min10050452.
10.3390/min10050452
Web of Science® Google Scholar
6 Winniewska M., Nowicki P., and Urban T., Influence of surfactants with different ionic character on the structure of poly(acrylic acid) adsorption layer on the activated biocarbons surface—electrokinetic and stability studies, Journal of Molecular Liquids. (2021) 332.
Web of Science® Google Scholar
7 Zhao T., Feng N., Zhao Y., and Gong C., Adsorption properties and aggregation behavior of mixed system of anionic/cation surfactants with different structures, Journal of Molecular Liquids. (2021) 337.
10.1016/j.molliq.2021.116341
PubMed Web of Science® Google Scholar
8 Pei J., Xing X., Xia B., Wang Z., and Luo Z., Study on the adsorption behavior between an imidazolium ionic liquid and na-montmorillonite, Molecules. (2019) 24, no. 7, https://doi.org/10.3390/molecules24071396, 2-s2.0-85064589792.
10.3390/molecules24071396
Web of Science® Google Scholar
9 Gifu I. C., Maxim M. E., Cinteza L. O. et al., Antimicrobial activities of hydrophobically modified poly(acrylate) films and their complexes with different chain length cationic surfactants, Coatings. (2019) 9, no. 4, https://doi.org/10.3390/coatings9040244, 2-s2.0-85072063582.
10.3390/coatings9040244
Web of Science® Google Scholar
10 Xu Y., Huang W., Shi Q., Zhang Y., Wu J., and Song L., Porous nano-structured VO2 films with different surfactants: synthesis mechanisms, characterization, and applications, Journal of Materials Science: Materials in Electronics. (2013) 24, no. 10, 3823–3829, https://doi.org/10.1007/s10854-013-1324-x, 2-s2.0-84890114394.
10.1007/s10854-013-1324-x
CAS Web of Science® Google Scholar
11 Rupp C., Steckel H., and Muller B. W., Mixed micelle formation with phosphatidylcholines: the influence of surfactants with different molecule structures, International Journal of Pharmaceutics. (2010) 387, no. 1-2, 120–128, https://doi.org/10.1016/j.ijpharm.2009.12.018, 2-s2.0-77649183728.
10.1016/j.ijpharm.2009.12.018
CAS PubMed Web of Science® Google Scholar
12 Wang W., Han Y., Zhu J., Fan Y., and Wang Y., Seeded growth of gold nanoparticles in aqueous solution of cationic gemini surfactants with different spacer length: influences of molecular and aggregate structures, Journal of Nanoparticle Research. (2019) 21, no. 2, https://doi.org/10.1007/s11051-019-4465-x, 2-s2.0-85060604927.
10.1007/s11051-019-4465-x
Web of Science® Google Scholar
13 Kim H., Goh B., Lee S., Lee K., and Choi J., Computational study on interfacial interactions between polymethyl methacrylate-based bone cement and hydroxyapatite in nanoscale [J], Applied Sciences-Basel. (2021) 11, no. 7, https://doi.org/10.3390/app11072937.
10.3390/app11072937
PubMed Web of Science® Google Scholar
14 Bai S., Kubelka J., and Piri M., Atomistic molecular dynamics simulations of surfactant-induced wettability alteration in crevices of calcite nanopores, Energy & Fuels. (2020) 34, no. 3, 3135–3143, https://doi.org/10.1021/acs.energyfuels.9b04528.
10.1021/acs.energyfuels.9b04528
CAS Web of Science® Google Scholar
15 Yue X. M., An Z. Y., Ye M., Liu Z. J., Xiao C. C., Huang Y., Han Y. J., Zhang S. Q., and Zhu J. S., Preparation of porous activated carbons for high performance supercapacitors from taixi anthracite by multi-stage activation, Molecules. (2019) 24, no. 19, https://doi.org/10.3390/molecules24193588, 2-s2.0-85072990237.
10.3390/molecules24193588
Web of Science® Google Scholar
16 Liu Y., Ma X., Li H. A., and Hou J., Competitive adsorption behavior of hydrocarbon(s)/CO₂ mixtures in a double-nanopore system using molecular simulations, Fuel. (2019) 252, 612–621, https://doi.org/10.1016/j.fuel.2019.04.123, 2-s2.0-85065093688.
10.1016/j.fuel.2019.04.123
CAS Web of Science® Google Scholar
17 Yin C., Li Y., Zhang T., Liu J., Yuan Y., and Huang M., Effects of exposure to anionic surfactants (SDBS and SDS) on nitrogen removal of aerobic denitrifier, Water Environment Research. (2020) 92, no. 12, 2129–2139, https://doi.org/10.1002/wer.1384.
10.1002/wer.1384
CAS PubMed Web of Science® Google Scholar
18 Xu C., Wang D., Wang H., Ma L., Zhu X., Zhu Y., Zhang Y., and Liu F., Experimental investigation of coal dust wetting ability of anionic surfactants with different structures, Process Safety and Environmental Protection. (2019) 121, 69–76, https://doi.org/10.1016/j.psep.2018.10.010, 2-s2.0-85055642559.
10.1016/j.psep.2018.10.010
CAS Web of Science® Google Scholar
19 Askari A., Vahabzadeh F., and Mardanpour M. M., Quantitative determination of linear alkylbenzene sulfonate (LAS) concentration and simultaneous power generation in a microbial fuel cell-based biosensor, Journal of Cleaner Production. (2021) 294.
10.1016/j.jclepro.2021.126349
PubMed Web of Science® Google Scholar
20 Zhao X., Gong L., Liao G., Luan H., Chen Q., Liu D., and Feng Y., Micellar solubilization of petroleum fractions by heavy alkylbenzene sulfonate surfactant, Journal of Molecular Liquids. (2021) 329.
10.1016/j.molliq.2021.115519
Web of Science® Google Scholar
21 Franco-Belussi L., Jones-Costa M., Salla R. F. et al., Hepatotoxicity of the anionic surfactant linear alkylbenzene sulphonate (LAS) in bullfrog tadpoles, Chemosphere. (2021) 266.
10.1016/j.chemosphere.2020.129014
PubMed Web of Science® Google Scholar
22 He X., Shinoda W., Devane R., Anderson K. L., and Klein M. L., Paramaterization of a coarse-grained model for linear alkylbenzene sulfonate surfactants and molecular dynamics studies of their self-assembly in aqueous solution, Chemical Physics Letters. (2010) 487, no. 1–3, 71–76, https://doi.org/10.1016/j.cplett.2010.01.029, 2-s2.0-76349107341.
10.1016/j.cplett.2010.01.029
CAS Web of Science® Google Scholar
23 Takeda K., Andoh Y., Shinoda W., and Okazaki S., Molecular behavior of linear alkylbenzene sulfonate in hydrated crystal, tilted gel, and liquid crystal phases studied by molecular dynamics simulation, Langmuir. (2019) 35, no. 33, 10877–10884, https://doi.org/10.1021/acs.langmuir.9b01607, 2-s2.0-85070971156.
10.1021/acs.langmuir.9b01607
CAS PubMed Web of Science® Google Scholar
24 Chai L., Yang L., Zhang Y., Zhou Y., Wang F., and Wu Z., Antagonism or synergism? responses of hydrocharis dubia (Bl.) Backer to linear alkylbenzene sulfonate, naphthalene and their joint exposure, Ecotoxicology and Environmental Safety. (2020) 200.
10.1016/j.ecoenv.2020.110747
PubMed Web of Science® Google Scholar
25 He X., Guvench O., MackerellA. D.JR., and Klein M. L., Atomistic simulation study of linear alkylbenzene sulfonates at the water/air interface, The Journal of Physical Chemistry B. (2010) 114, no. 30, 9787–9794, https://doi.org/10.1021/jp101860v, 2-s2.0-77955440094.
10.1021/jp101860v
CAS PubMed Web of Science® Google Scholar
26 Smith G. A., Huggett A., Jones C., and Ortego G., Surface activity and performance properties of gemini salts of linear alkylbenzene sulfonate in aqueous solution, Journal of Surfactants and Detergents. (2021) 24, no. 4, 563–574, https://doi.org/10.1002/jsde.12496.
10.1002/jsde.12496
CAS Web of Science® Google Scholar
27 Lee T., Park U., Puligundla P., and Mok C., Corona discharge plasma-based degradation of simulated residual linear alkylbenzene sulphonate and dodecyl benzene sulfonate surfactants, International Journal of Environmental Science and Technology. (2020) 17, no. 2, 1171–1178, https://doi.org/10.1007/s13762-019-02534-w, 2-s2.0-85073985102.
10.1007/s13762-019-02534-w
CAS Web of Science® Google Scholar
28 Kishimoto N. and Hamamoto S., Removal of linear alkylbenzene sulfonate (LAS) by a cetyltrimethylammonium bromide (CTAB)-aided coagulation-filtration process, Environmental Technology. (2020) 43, no. 6, 815–823.
10.1080/09593330.2020.1807607
PubMed Web of Science® Google Scholar
29 Wang X., Liu X., Huo Y., and Niu J., Properties of binary mixture of cetyl diphenyl ether disulfonate and linear alkylbenzene sulfonate, Tenside Surfactants Detergents. (2020) 57, no. 3, 259–264, https://doi.org/10.3139/113.110677.
10.3139/113.110677
CAS Web of Science® Google Scholar
30 Koohsaryan E., Anbia M., Sepehrian M., and Maghsoodlu M., Facile hydrothermal synthesis of hierarchical sodium P zeolite as a nonphosphate detergent builder, Journal of Surfactants and Detergents. (2021) 24, no. 1, 85–97, https://doi.org/10.1002/jsde.12452.
10.1002/jsde.12452
CAS Web of Science® Google Scholar
31 Gang H. Z., Bian P. C., He X., He X., Bao X., Mu B. Z., Li Y., and Yang S. Z., Mixing of surfactin, an anionic biosurfactant, with alkylbenzene sulfonate, a chemically synthesized anionic surfactant, at the n-decane/water interface, Journal of Surfactants and Detergents. (2021) 24, no. 3, 445–457, https://doi.org/10.1002/jsde.12495.
10.1002/jsde.12495
CAS Web of Science® Google Scholar
32 Cai H.-Y., Zhang Y., Liu Z.-Y., Li J.-G., Gong Q.-T., Liao Q., Zhang L., and Zhao S., Molecular dynamics simulation of binary betaine and anionic surfactant mixtures at decane—water interface, Journal of Molecular Liquids. (2018) 266, 82–89, https://doi.org/10.1016/j.molliq.2018.06.047, 2-s2.0-85048899307.
10.1016/j.molliq.2018.06.047
CAS Web of Science® Google Scholar
33 DU X., Zhao L., He X., Chen H., Fang W., and Li W., Ultra-stable aqueous foams with multilayer films stabilized by 1-dodecanol, sodium dodecyl sulfonate and polyvinyl alcohol, Chemical Engineering Science. (2017) 160, 72–79, https://doi.org/10.1016/j.ces.2016.11.024, 2-s2.0-84998610661.
10.1016/j.ces.2016.11.024
CAS Web of Science® Google Scholar
34 Liu Z., Zhou G., Li S., Wang C., Liu R., and Jiang W., Molecular dynamics simulation and experimental characterization of anionic surfactant: influence on wettability of low-rank coal, Fuel. (2020) 279.
10.1016/j.fuel.2020.118323
PubMed Web of Science® Google Scholar
35 Han W., Zhou G., Xing M., Yang Y., Zhang X., Miao Y., and Wang Y., Experimental investigation on physicochemical characteristics of coal treated with synthetic sodium salicylate-imidazole ionic liquids, Journal of Molecular Liquids. (2021) 327.
10.1016/j.molliq.2020.114822
PubMed Web of Science® Google Scholar
36 Li B., Liu S., Guo J., Zhang L., and Sun X., Increase in wettability difference between organic and mineral matter to promote low-rank coal flotation by using ultrasonic treatment, Applied Surface Science. (2019) 481, 454–459, https://doi.org/10.1016/j.apsusc.2019.03.142, 2-s2.0-85063007431.
10.1016/j.apsusc.2019.03.142
CAS Web of Science® Google Scholar
37 Li L., He M., Li Z., Ma C., Yu H., and You X., Wettability effect of ethoxylated nonyl phenol with different ethylene oxide chain length on shendong long-flame coal surface, Materials Today Communications. (2021) 26.
10.1016/j.mtcomm.2020.101697
PubMed Web of Science® Google Scholar
38 Meng J., Yin F., Li S., Zhong R., Sheng Z., and Nie B., Effect of different concentrations of surfactant on the wettability of coal by molecular dynamics simulation, International Journal of Mining Science and Technology. (2019) 29, no. 4, 577–584, https://doi.org/10.1016/j.ijmst.2019.06.010, 2-s2.0-85067691335.
10.1016/j.ijmst.2019.06.010
CAS Web of Science® Google Scholar
39 Liu B., Zhang Y., Xu K., Zhang Y., Hao Z., and Ma N., Study on a new type of composite powder explosion inhibitor used to suppress underground coal dust explosion, Applied Sciences-Basel. (2021) 11, no. 18, https://doi.org/10.3390/app11188512.
10.3390/app11188512
Web of Science® Google Scholar
40 Oproiu C. L., Voicu G., Bădănoiu A., and Nicoară A. I., The solidification/stabilization of wastewater (from a landfill leachate) in specially designed binders based on coal ash, Materials. (2021) 14, no. 19, https://doi.org/10.3390/ma14195610.
10.3390/ma14195610
PubMed Web of Science® Google Scholar
41 Jin H., Zhang Y., Chen K., Niu K., Wu G., Wei X., and Wang H., Preparation and characterization of a composite dust suppressant for coal mines, Polymers-Basel. (2020) 12, no. 12, https://doi.org/10.3390/polym12122942.
10.3390/polym12122942
Web of Science® Google Scholar
42 Yuan M., Nie W., Zhou W., Yan J., Bao Q., Guo C., Tong P., Zhang H., and Guo L., Determining the effect of the non-ionic surfactant AEO(9) on lignite adsorption and wetting via molecular dynamics (MD) simulation and experiment comparisons, Fuel. (2020) 278.
10.1016/j.fuel.2020.118339
Web of Science® Google Scholar
43 Li B., Liu S., Fan M., and Zhang L., The effect of ethylene oxide groups in dodecyl ethoxyl ethers on low rank coal flotation: an experimental study and simulation, Powder Technology. (2019) 344, 684–692, https://doi.org/10.1016/j.powtec.2018.12.063, 2-s2.0-85058678336.
10.1016/j.powtec.2018.12.063
CAS Web of Science® Google Scholar
44 Zhang H., XI P., Zhuo Q., and Liu W., Construction of molecular model and adsorption of collectors on bulianta coal, Molecules. (2020) 25, no. 17, https://doi.org/10.3390/molecules25174030.
10.3390/molecules25174030
Web of Science® Google Scholar
45 Chang H., Zhang H., Jia Z., Li X., Gao W., and Wei W., Wettability of coal pitch surface by aqueous solutions of cationic gemini surfactants, Colloids and Surfaces A: Physicochemical and Engineering Aspects. (2016) 494, 59–64, https://doi.org/10.1016/j.colsurfa.2016.01.022, 2-s2.0-84955133638.
10.1016/j.colsurfa.2016.01.022
CAS Web of Science® Google Scholar
46 Liu X., Liu S., Cheng Y., and Xu G., Decrease in hydrophilicity and moisture readsorption of lignite: effects of surfactant structure, Fuel. (2020) 273.
10.1016/j.fuel.2020.117812
Web of Science® Google Scholar
47 Ni G., Qian S., Meng X., Hui W., Yuhang X., Weimin C., and Gang W., Effect of NaCl-SDS compound solution on the wettability and functional groups of coal, Fuel. (2019) 257.
Web of Science® Google Scholar
48 Yao Q., Xu C., Zhang Y., Zhou G., Zhang S., and Wang D., Micromechanism of coal dust wettability and its effect on the selection and development of dust suppressants, Process Safety and Environmental Protection. (2017) 111, 726–732, https://doi.org/10.1016/j.psep.2017.08.037, 2-s2.0-85029367110.
10.1016/j.psep.2017.08.037
CAS Web of Science® Google Scholar
49 Chang Z., Chen X., and Peng Y., The interaction between diesel and surfactant triton X-100 and their adsorption on coal surfaces with different degrees of oxidation, Powder Technology. (2019) 342, 840–847, https://doi.org/10.1016/j.powtec.2018.10.047, 2-s2.0-85055907504.
10.1016/j.powtec.2018.10.047
CAS Web of Science® Google Scholar
50 Liu X., Liu S., Fan M., and Zhang L., Decrease of hydrophilicity of lignite using CTAB: effects of adsorption differences of surfactant onto mineral composition and functional groups, Fuel. (2017) 197, 474–481, https://doi.org/10.1016/j.fuel.2017.02.065, 2-s2.0-85013872325.
10.1016/j.fuel.2017.02.065
CAS Web of Science® Google Scholar
51 Chen H., Gizzatov A., and Abdel-Fattah A. I., Molecular assembly of surfactant mixtures in oil-swollen micelles: implications for high salinity colloidal stability, The Journal of Physical Chemistry B. (2020) 124, no. 3, 568–576, https://doi.org/10.1021/acs.jpcb.9b09929.
10.1021/acs.jpcb.9b09929
CAS PubMed Web of Science® Google Scholar
52 Zhou J. and Ranjith P. G., Self-assembly and viscosity changes of binary surfactant solutions: a molecular dynamics study, Journal of Colloid and Interface Science. (2021) 585, 250–257, https://doi.org/10.1016/j.jcis.2020.11.022.
10.1016/j.jcis.2020.11.022
CAS PubMed Web of Science® Google Scholar
53 Müller P., Bonthuis D. J., Miller R., and Schneck E., Ionic surfactants at air/water and oil/water interfaces: a comparison based on molecular dynamics simulations, The Journal of Physical Chemistry B. (2021) 125, no. 1, 406–415, https://doi.org/10.1021/acs.jpcb.0c08615.
10.1021/acs.jpcb.0c08615
CAS PubMed Web of Science® Google Scholar
54 Kubelka J., Bai S., and Piri M., Effects of surfactant charge and molecular structure on wettability alteration of calcite: insights from molecular dynamics simulations, The Journal of Physical Chemistry B. (2021) 125, no. 4, 1293–1305, https://doi.org/10.1021/acs.jpcb.0c10361.
10.1021/acs.jpcb.0c10361
CAS PubMed Web of Science® Google Scholar
55 Zhang L., Liu Z., Ren T., Wu P., Shen J.-W., Zhang W., and Wang X., Understanding the structure of hydrophobic surfactants at the air/water interface from molecular level, Langmuir. (2014) 30, no. 46, 13815–13822, https://doi.org/10.1021/la5030586, 2-s2.0-84912554811.
10.1021/la5030586
CAS PubMed Web of Science® Google Scholar
56 Saha B., Sankar Patra A., Mukherjee A. K., and Paul I., Interaction and thermal stability of carboxymethyl cellulose on alpha-Fe₂O₃(001) surface: ReaxFF molecular dynamics simulations study, Journal of Molecular Graphics and Modelling. (2021) 102.
10.1016/j.jmgm.2020.107787
PubMed Web of Science® Google Scholar
57 Li B., Liu S., Guo J., and Zhang L., Interaction between low rank coal and kaolinite particles: a DFT simulation, Applied Surface Science. (2018) 456, 215–220, https://doi.org/10.1016/j.apsusc.2018.06.121, 2-s2.0-85048726565.
10.1016/j.apsusc.2018.06.121
CAS Web of Science® Google Scholar
58 Domínguez H., Computer studies on the effects of long chain alcohols on sodium dodecyl sulfate (SDS) molecules in SDS/dodecanol and SDS/hexadecanol monolayers at the air/water interface, The Journal of Physical Chemistry B. (2006) 110, no. 26, 13151–13157, https://doi.org/10.1021/jp062018n, 2-s2.0-33746373489.
10.1021/jp062018n
CAS PubMed Web of Science® Google Scholar
59 Wang L., Sun N., Wang Z., Han H., Yang Y., Liu R., Hu Y., Tang H., and Sun W., Self-assembly of mixed dodecylamine-dodecanol molecules at the air/water interface based on large-scale molecular dynamics, Journal of Molecular Liquids. (2019) 276, 867–874, https://doi.org/10.1016/j.molliq.2018.12.076, 2-s2.0-85058542602.
10.1016/j.molliq.2018.12.076
CAS Web of Science® Google Scholar
60 Wang M., Gao J., Xu J., Du Q., and Zhang Y., Effect of H₂O on the transformation of sulfur during demineralized coal pyrolysis: molecular dynamics simulation using ReaxFF, Energy & Fuels. (2021) 35, no. 3, 2379–2390, https://doi.org/10.1021/acs.energyfuels.0c02916.
10.1021/acs.energyfuels.0c02916
CAS Web of Science® Google Scholar
61 Hao M., Qiao Z., Zhang H., Wang Y., and Li Y., Thermodynamic analysis of CH₄/CO₂/N₂ adsorption on anthracite coal: investigated by molecular simulation, Energy & Fuels. (2021) 35, no. 5, 4246–4257, https://doi.org/10.1021/acs.energyfuels.0c04337.
10.1021/acs.energyfuels.0c04337
CAS Web of Science® Google Scholar
62 Dong K., Zeng F., Jia J., Chen C., and Gong Z., Molecular simulation of the preferential adsorption of CH₄ and CO₂ in middle-rank coal, Molecular Simulation. (2019) 45, no. 1, 15–25, https://doi.org/10.1080/08927022.2018.1521968, 2-s2.0-85055121386.
10.1080/08927022.2018.1521968
CAS Web of Science® Google Scholar
63 Yu S., Bo J., and Meijun Q., Molecular dynamic simulation of self- and transport diffusion for CO₂/CH₄/N₂ in low-rank coal vitrinite, Energy & Fuels. (2018) 32, no. 3, 3085–3096, https://doi.org/10.1021/acs.energyfuels.7b03676, 2-s2.0-85044120467.
10.1021/acs.energyfuels.7b03676
CAS Web of Science® Google Scholar
64 Li Y., Yang Z., and Li X., Molecular simulation study on the effect of coal rank and moisture on CO₂/CH₄ competitive adsorption, Energy & Fuels. (2019) 33, no. 9, 9087–9098, https://doi.org/10.1021/acs.energyfuels.9b01805, 2-s2.0-85072656472.
10.1021/acs.energyfuels.9b01805
CAS Web of Science® Google Scholar
65 Chen R., Dong X., Fan Y., Ma X., Dong Y., and Chang M., Interaction between STAC and coal/kaolinite in tailing dewatering: an experimental and molecular-simulation study, Fuel. (2020) 279.
10.1016/j.fuel.2020.118224
PubMed Web of Science® Google Scholar
66 Ma X. M., Fan Y. P., and Dong X. S., Impact of clay minerals on the dewatering of coal slurry: an experimental and molecular-simulation study, Minerals-Basel. (2018) 8, no. 9, https://doi.org/10.3390/min8090400, 2-s2.0-85054835739.
10.3390/min8090400
Web of Science® Google Scholar
67 Zhang L., Li B., Xia Y., and Liu S., Wettability modification of wender lignite by adsorption of dodecyl poly ethoxylated surfactants with different degree of ethoxylation: a molecular dynamics simulation study, Journal of Molecular Graphics and Modelling. (2017) 76, 106–117, https://doi.org/10.1016/j.jmgm.2017.06.028, 2-s2.0-85023640275.
10.1016/j.jmgm.2017.06.028
CAS PubMed Web of Science® Google Scholar
68 Li B., Guo J., Liu S., Albijanic B., Zhang L., and Sun X., Molecular insight into the mechanism of benzene ring in nonionic surfactants on low-rank coal floatability, Journal of Molecular Liquids. (2020) 302.
10.1016/j.molliq.2020.112563
Web of Science® Google Scholar
69 Guo J., Xia Y., Liu Y., Liu S., Zhang L., and Li B., Microscopic adsorption behaviors of ionic surfactants on lignite surface and its effect on the wettability of lignite: a simulation and experimental study, Journal of Molecular Liquids. (2022) 345.
10.1016/j.molliq.2021.117851
Web of Science® Google Scholar
70 Xuanlai C., Yan G., Yang X., and Xu G., Study on adsorption characteristics of sulfonate gemini surfactant on lignite surface, Minerals-Basel. (2021) 11, no. 12, https://doi.org/10.3390/min11121401.
10.3390/min11121401
Web of Science® Google Scholar
71 Chen X. L., Yan G. C., and Yang X. L., Microscopic diffusion characteristics of linear alkylbenzene sulfonates on the surface of anthracite: the influence of different attachment sites of benzene ring in the backbone [J], Minerals-Basel. (2021) 11, no. 10, https://doi.org/10.3390/min11101045.
10.3390/min11101045
Web of Science® Google Scholar
72 Vermeer L. S., DE Groot B. L., Réat V., Milon A., and Czaplicki J., Acyl chain order parameter profiles in phospholipid bilayers: computation from molecular dynamics simulations and comparison with 2H NMR experiments, European Biophysics Journal. (2007) 36, no. 8, 919–931, https://doi.org/10.1007/s00249-007-0192-9, 2-s2.0-35748967575.
10.1007/s00249-007-0192-9
CAS PubMed Web of Science® Google Scholar
73 Acisli O., Karaca S., and Gurses A., Investigation of the alkyl chain lengths of surfactants on their adsorption by montmorillonite (Mt) from aqueous solutions, Applied Clay Science. (2017) 142, 90–99.
10.1016/j.clay.2016.12.009
CAS Web of Science® Google Scholar
74 Ren H.-P., Tian S.-P., Zhu M., Zhao Y.-Z., Li K.-X., Ma Q., Ding S.-Y., Gao J., and Miao Z., Modification of montmorillonite by gemini surfactants with different chain lengths and its adsorption behavior for methyl orange, Applied Clay Science. (2018) 151, 29–36, https://doi.org/10.1016/j.clay.2017.10.024, 2-s2.0-85033556768.
10.1016/j.clay.2017.10.024
CAS Web of Science® Google Scholar
75 Kun R., Kis L., and Dékány I., Hydrophobization of bovine serum albumin with cationic surfactants with different hydrophobic chain length, Colloids and Surfaces B: Biointerfaces. (2010) 79, no. 1, 61–68, https://doi.org/10.1016/j.colsurfb.2010.03.028, 2-s2.0-77952951123.
10.1016/j.colsurfb.2010.03.028
CAS PubMed Web of Science® Google Scholar
76 Jiang Y., Geng T., Li Q., Li G., and Ju H., Equilibrium and dynamic surface tension properties of salt-free catanionic surfactants with different hydrocarbon chain lengths, Journal of Molecular Liquids. (2015) 204, 126–131, https://doi.org/10.1016/j.molliq.2015.01.026, 2-s2.0-84922229450.
10.1016/j.molliq.2015.01.026
CAS Web of Science® Google Scholar
77 Díez-Pascual A. M., Vallés C., Mateos R., Vera-López S., Kinloch I. A., and Andrés M. P. S., Influence of surfactants of different nature and chain length on the morphology, thermal stability and sheet resistance of graphene, Soft Matter. (2018) 14, no. 29, 6013–6023, https://doi.org/10.1039/c8sm01017j, 2-s2.0-85050943283.
10.1039/C8SM01017J
CAS PubMed Web of Science® Google Scholar
78 Zhang H., Xia M., Wang F., Li P., and Shi M., Adsorption properties and mechanism of montmorillonite modified by two gemini surfactants with different chain lengths for three benzotriazole emerging contaminants: experimental and theoretical study, Applied Clay Science. (2021) 207.
10.1016/j.clay.2021.106086
Web of Science® Google Scholar
79 Badache L., Lehanine Z., and Naït Abderrahmane W., Synthesis and surface properties study of a series of cationic surfactants with different hydrophobic chain lengths, Journal of Surfactants and Detergents. (2012) 15, no. 6, 715–720, https://doi.org/10.1007/s11743-012-1351-2, 2-s2.0-84870313932.
10.1007/s11743-012-1351-2
CAS Web of Science® Google Scholar
80 Wei S., Yan G. C., Zhang Z. Q., Liu M. S., and Zhang Y. F., Molecular structure analysis of jincheng anthracite coal, Journal of China Coal Society. (2018) 43, no. 2, 555–562.
Google Scholar
81 Zhang Z., Wang C., and Yan K., Adsorption of collectors on model surface of wiser bituminous coal: a molecular dynamics simulation study, Minerals Engineering. (2015) 79, 31–39, https://doi.org/10.1016/j.mineng.2015.05.009, 2-s2.0-84930198384.
10.1016/j.mineng.2015.05.009
CAS Web of Science® Google Scholar
82 You X., He M., Zhang W., Wei H., Lyu X., He Q., and Li L., Molecular dynamics simulations of nonylphenol ethoxylate on the hatcher model of subbituminous coal surface, Powder Technology. (2018) 332, 323–330, https://doi.org/10.1016/j.powtec.2018.04.004, 2-s2.0-85045061149.
10.1016/j.powtec.2018.04.004
CAS Web of Science® Google Scholar
83 Zhang R., Xing Y., Xia Y. et al., New insight into surface wetting of coal with varying coalification degree: an experimental and molecular dynamics simulation study, Applied Surface Science. (2020) 511.
10.1016/j.apsusc.2020.145610
Web of Science® Google Scholar
84 Xia Y., Yang Z., Zhang R., Xing Y., and Gui X., Enhancement of the surface hydrophobicity of low-rank coal by adsorbing DTAB: an experimental and molecular dynamics simulation study, Fuel. (2019) 239, 145–152, https://doi.org/10.1016/j.fuel.2018.10.156, 2-s2.0-85056279626.
10.1016/j.fuel.2018.10.156
CAS Web of Science® Google Scholar
85 Li J., Han Y., Qu G., Cheng J., Xue C., Gao X., Sun T., and Ding W., Molecular dynamics simulation of the aggregation behavior of N-dodecyl-N, N-dimethyl-3-ammonio-1-propanesulfonate/sodium dodecyl benzene sulfonate surfactant mixed system at oil/water interface, Colloids and Surfaces A: Physicochemical and Engineering Aspects. (2017) 531, 73–80, https://doi.org/10.1016/j.colsurfa.2017.07.088, 2-s2.0-85026900153.
10.1016/j.colsurfa.2017.07.088
CAS Web of Science® Google Scholar
86 Anvari M. H., Liu Q., Xu Z., and Choi P., Molecular dynamics study of hydrophilic sphalerite (110) surface as modified by normal and branched butylthiols, Langmuir. (2018) 34, no. 10, 3363–3373, https://doi.org/10.1021/acs.langmuir.7b04303, 2-s2.0-85043681339.
10.1021/acs.langmuir.7b04303
CAS PubMed Web of Science® Google Scholar
87 Guo J., Zhang L., Liu S., and Li B., Effects of hydrophilic groups of nonionic surfactants on the wettability of lignite surface: molecular dynamics simulation and experimental study, Fuel. (2018) 231, 449–457, https://doi.org/10.1016/j.fuel.2018.05.106, 2-s2.0-85047548570.
10.1016/j.fuel.2018.05.106
CAS Web of Science® Google Scholar
88 Liu Q., Yuan S., Yan H., and Zhao X., Mechanism of oil detachment from a silica surface in aqueous surfactant solutions: molecular dynamics simulations, The Journal of Physical Chemistry B. (2012) 116, no. 9, 2867–2875, https://doi.org/10.1021/jp2118482, 2-s2.0-84863293128.
10.1021/jp2118482
CAS PubMed Web of Science® Google Scholar
89 He X., Liu X., Song D., and Nie B., Effect of microstructure on electrical property of coal surface, Applied Surface Science. (2019) 483, 713–720, https://doi.org/10.1016/j.apsusc.2019.03.342, 2-s2.0-85063966964.
10.1016/j.apsusc.2019.03.342
CAS Web of Science® Google Scholar
90 Zhang L., Sun X. L., Li B., Xie Z., Guo J., and Liu S., Experimental and molecular dynamics simulation study on the enhancement of low rank coal flotation by mixed collector, Fuel. (2020) 266.
10.1016/j.fuel.2020.117046
Web of Science® Google Scholar
91 Wang W., Liang L., Peng Y., and Holuszko M., Surface chemical heterogeneity of low rank coal characterized by micro-FTIR and its correlation with hydrophobicity, Minerals-Basel. (2021) 11, no. 3, https://doi.org/10.3390/min11030239.
10.3390/min11030239
Web of Science® Google Scholar
92 Zhang L., Guo J, Hao M., Li B., and Liu S., Microscopic spreading characteristics of non-polar oil droplet on low rank coal surface: effects of surfactant pretreatment and oxygen-containing groups, Journal of Molecular Liquids. (2021) 325.
10.1016/j.molliq.2020.115232
Web of Science® Google Scholar
93 Zhang S., Song B., Cao C., Zhang H., Liu Q., Li K., and Teppen B. J., Structural evolution of high-rank coals during coalification and graphitization: X-ray diffraction, Raman spectroscopy, high-resolution transmission electron microscopy, and reactive force field molecular dynamics simulation study, Energy & Fuels. (2021) 35, no. 3, 2087–2097, https://doi.org/10.1021/acs.energyfuels.0c03649.
10.1021/acs.energyfuels.0c03649
CAS Web of Science® Google Scholar
94 Ma X., Dong X., and Fan Y., Prediction and characterization of the microcrystal structures of coal with molecular simulation, Energy & Fuels. (2018) 32, no. 3, 3097–3107, https://doi.org/10.1021/acs.energyfuels.7b03698, 2-s2.0-85044168917.
10.1021/acs.energyfuels.7b03698
CAS Web of Science® Google Scholar
95 Gao M., Li X., Ren C., Wang Z., Pan Y., and Guo L., Construction of a multicomponent molecular model of fugu coal for ReaxFF-MD pyrolysis simulation, Energy & Fuels. (2019) 33, no. 4, 2848–2858, https://doi.org/10.1021/acs.energyfuels.8b04434, 2-s2.0-85064840266.
10.1021/acs.energyfuels.8b04434
CAS Web of Science® Google Scholar
96 Bian J., Li X., Zeng F., and Wang X., Construction and evaluation of a medium-rank coal molecular model using a hybrid experimental-simulation-theoretical method, Energy & Fuels. (2019) 33, no. 12, 12905–12915, https://doi.org/10.1021/acs.energyfuels.9b02052.
10.1021/acs.energyfuels.9b02052
CAS Web of Science® Google Scholar
97 Yan H., Guo X.-L., Yuan S.-L., and Liu C.-B., Molecular dynamics study of the effect of calcium ions on the monolayer of SDC and SDSn surfactants at the vapor/liquid interface, Langmuir. (2011) 27, no. 10, 5762–5771, https://doi.org/10.1021/la1049869, 2-s2.0-79958090343.
10.1021/la1049869
CAS PubMed Web of Science® Google Scholar
98 Yang J.-S., Yang C.-L., Wang M.-S., Chen B.-D., and Ma X.-G., Crystallization of alkane melts induced by carbon nanotubes and graphene nanosheets: a molecular dynamics simulation study, Physical Chemistry Chemical Physics. (2011) 13, no. 34, 15476–15482, https://doi.org/10.1039/c1cp20695h, 2-s2.0-80052131491.
10.1039/c1cp20695h
CAS PubMed Web of Science® Google Scholar
99 Zhao T., Xu G., Yuan S., Chen Y., and Yan H., Molecular dynamics study of alkyl benzene sulfonate at air/water interface: effect of inorganic salts, The Journal of Physical Chemistry B. (2010) 114, no. 15, 5025–5033, https://doi.org/10.1021/jp907438x, 2-s2.0-77951133307.
10.1021/jp907438x
CAS PubMed Web of Science® Google Scholar
100 Guruge A. G., Warren D. B., Benameur H., Pouton C. W., and Chalmers D. K., Aqueous phase behavior of the PEO-containing non-ionic surfactant C12E6: a molecular dynamics simulation study, Journal of Colloid and Interface Science. (2021) 588, 257–268, https://doi.org/10.1016/j.jcis.2020.12.032.
10.1016/j.jcis.2020.12.032
CAS PubMed Web of Science® Google Scholar
101 Xu J., Zhang Y., Chen H., Wang P., Xie Z., Yao Y., Yan Y., and Zhang J., Effect of surfactant headgroups on the oil/water interface: an interfacial tension measurement and simulation study, Journal of Molecular Structure. (2013) 1052, 50–56, https://doi.org/10.1016/j.molstruc.2013.07.049, 2-s2.0-84883850192.
10.1016/j.molstruc.2013.07.049
CAS Web of Science® Google Scholar
102 Parra J. G., Domínguez H., Aray Y., Iza P., Zarate X., and Schott E., Behavior of the SDS/1-butanol and SDS/2-butanol mixtures at the water/n-octane interface through molecular dynamics simulations, Chemical Physics. (2019) 523, 138–149, https://doi.org/10.1016/j.chemphys.2019.04.021, 2-s2.0-85064758441.
10.1016/j.chemphys.2019.04.021
CAS Web of Science® Google Scholar
103 Parra J. G., Dominguez H., and Aray Y., Structural and interfacial properties of the CO₂-in-water foams prepared with sodium dodecyl sulfate (SDS): a molecular dynamics simulation study, Colloid Surface A. (2019) 578, 123, 615, https://doi.org/10.1016/j.colsurfa.2019.123615, 2-s2.0-85068512984.
10.1016/j.colsurfa.2019.123615
Google Scholar
104 Wang L., Hu Y., Liu R., Liu J., and Sun W., Synergistic adsorption of DDA/alcohol mixtures at the air/water interface: a molecular dynamics simulation, Journal of Molecular Liquids. (2017) 243, 1–8, https://doi.org/10.1016/j.molliq.2017.08.029, 2-s2.0-85026910631.
10.1016/j.molliq.2017.08.029
CAS Web of Science® Google Scholar
105 Stanishneva-Konovalova T. B. and Sokolova O. S., Molecular dynamics simulations of negatively charged DPPC/DPPI lipid bilayers at two levels of resolution, Computational and Theoretical Chemistry. (2015) 1058, 61–66, https://doi.org/10.1016/j.comptc.2014.04.036, 2-s2.0-84900759175.
10.1016/j.comptc.2014.04.036
CAS Web of Science® Google Scholar
106 Rodríguez-Ropero F. and Fioroni M., Effect of Na⁺, Mg²⁺, and Zn²⁺ chlorides on the structural and thermodynamic properties of water/n-heptane interfaces, Journal of Computational Chemistry. (2011) 32, no. 9, 1876–1886, https://doi.org/10.1002/jcc.21770, 2-s2.0-79955416215.
10.1002/jcc.21770
CAS PubMed Web of Science® Google Scholar
107 Sun H., Xiao H., and Liu X., Structural properties of hydroxyl-substituted alkyl benzenesulfonates at the water/vapor and water/decane interfaces, Science China Chemistry. (2011) 54, no. 7, 1078–1085, https://doi.org/10.1007/s11426-011-4305-z, 2-s2.0-79960356306.
10.1007/s11426-011-4305-z
CAS Web of Science® Google Scholar

Citing Literature

All articles

Computational Study on the Microscopic Adsorption Characteristics of Linear Alkylbenzene Sulfonates with Different Chain Lengths on Anthracite Surface

Abstract

1. Introduction