It is very important to understand the structure and properties of aluminum electrolytic capacitor ionic liquids at the molecular level. The use of molecular dynamics simulation methods can not only analyze the arrangement of anions and cations in aluminum electrolytic capacitor ionic liquids and their microstructure, but also predict their thermodynamic properties.

Molecular dynamics simulation is an important simulation method for simulating atoms and molecules in evaluating and predicting the structure and properties of materials. In this method, the influence of volume is ignored, and the atoms or particles in the system are treated as particles, and only mass and charge are considered. By drawing a multidimensional coordinate system, the position and velocity of each atom or particle at the initial moment can be given, and then the potential energy function between particles can be given, and the force and potential energy generated by each atom under the action of surrounding atoms can be calculated. Then, the force and potential energy can be finally solved by using the classical Newtonian mechanics theory equation, and the vector velocity and multidimensional coordinates of each atom at each moment can be determined, so that the behavior trajectory of the entire system can be understood from a macroscopic perspective. Without considering the extranuclear electrons of each atom and the polarization effect they produce, all atoms or particles in the system are in an ideal state and satisfy Newton’s equation of motion. Each atom can still consider the superposition of the forces of other atoms on its body based on the current force. Therefore, the molecular dynamics method is a special form of Newton’s equation of motion. In short, molecular dynamics simulation is a modern computer simulation method based on the principles of stress field and Newtonian motion mechanics.

1 Molecular dynamics simulation of aluminum electrolytic capacitor ionic liquids

Before conducting molecular dynamics simulation, you first need to accurately understand basic concepts such as force field, Newton’s equations of motion and their numerical solutions, ensemble, periodic boundary conditions, integration, step size, etc.

1. Molecular field

The force field is an expression of the potential energy surface, which is the basis of molecular dynamics simulation and is a function of the potential energy of the molecule and the distance between atoms. There are many different forms of force fields, with different scopes of application and limitations. Choosing the right force field has a great impact on the accuracy of the calculation results.

Among various forms of force fields, Lennard-Jones (LJ) potential energy [Equation (6)] is currently the most commonly used potential energy, and its potential energy expression is:

Where: U(r) is the molecular potential energy corresponding to the r value; r is the interatomic distance; ε, σ are potential energy parameters

At present, force fields have developed from the initial unit sub-molecular system to polyatomic molecules, polymer molecules and even biomolecular systems. The complexity, accuracy and scope of application of force fields are very different from before, and have been greatly improved in all aspects. Among the many force fields, each force field has its own advantages and disadvantages under different conditions. Therefore, it is crucial to analyze and select the appropriate force field during simulation. This requires considering many factors such as the simulation conditions and the characteristics of the system, so as to select the best force field, ensure the speed and accuracy of the simulation, and minimize the error.

2. Newton’s motion equation and its numerical solution method

In molecular dynamics calculations, you first need to understand Newton’s equations of motion and their related numerical solutions.

Since there are a large number of hydrogen bonds in aluminum electrolytic capacitor ionic liquids, and they have a great influence on the performance of aluminum electrolytic capacitor ionic liquids, for commonly used imidazole ionic liquids, in order to accurately understand the physical properties and analyze the relationship between the local structure and the properties of ionic liquids, it is first necessary to establish its hydrogen bond force field, and then obtain it through molecular dynamics simulation methods; in addition, for the common aluminum electrolytic capacitor ionic liquid and water mixed system in applications, when studying the formation of cluster structures of ionic liquids in aqueous solutions, it is necessary to focus on analyzing the relationship between the formation mechanism of vesicle structure and penetration behavior; finally, a granulation force field is constructed and the parameters are optimized, and commonly used aluminum electrolytic capacitor ionic liquids can achieve large-scale calculations in this force field.

The velocity and position of the particle are calculated according to equations (7), (8) and (9), thereby determining the trajectory of the particle’s motion. This is the basic idea of ​​molecular dynamics simulation calculations.

Newton’s equations of motion are generally solved using the numerical solution developed by Verlet. The original Verlet method had a large error. Its principle was to expand the particle’s position using the Taylor formula and calculate the result. Later, Verlet proposed the leap frog method, which solved the problem of large errors. The mathematical formula for calculating speed and position using this method is as follows:

3. Ensemble

An ensemble is a collection of systems with the same conditions. For example, the canonical ensemble is a collection of systems with the same number of molecules N, the same volume V and the same temperature T, and its symbol is (N, V, T). In addition, there are many ensembles, such as the isoparticle isothermal constant pressure ensemble (N, Tp), the isoparticle isochoric constant energy ensemble (N.V.E), etc. Ensemble is a very important concept in statistical mechanics. Using the ensemble as a starting point for deduction, all statistical properties of the system can be obtained. In practical applications, it is very important to choose an appropriate ensemble. For example, when studying the phase change of a material, the commonly used ensemble is (N, T, p).

4. Periodic boundary conditions

Molecular dynamics calculations usually select a certain number of molecules and place them in a cubic box. The box is used as a simulation system, and is usually surrounded by boxes with the same particle arrangement and motion as the box. During the movement of particles, in order to maintain a constant number of particles in the simulation system, if one or more particles in the calculation system leave the box, one or more particles must run into the calculation system from other boxes, so as to ensure that the density of the simulation system is constant, which is the case in actual situations. This condition that ensures the constant density of the system is called a periodic boundary condition.

2 Application of aluminum electrolytic capacitor ionic liquids in molecular dynamics simulation research

The molecular dynamics simulation method can obtain the microscopic structural information of aluminum electrolytic capacitor ionic liquids that cannot be measured under experimental conditions, as well as the behavior of aluminum electrolytic capacitor ionic liquids under extreme conditions, and provide a basis for understanding the interaction between atoms in aluminum electrolytic capacitor ionic liquids and further obtaining the atom-atom and center-of-mass radial distribution functions of the system. This section mainly introduces some examples of the application of molecular dynamics simulation in the study of the structure and properties of aluminum electrolytic capacitor ionic liquids, and explains its specific application direction

Zhang et al. used molecular dynamics simulation to study the electric field driven ion emission from the free surface of a planar RTIL film and calculated the ion emission rate (je) as a function of the electric field perpendicular to the RTIL/vacuum surface (En). They found that the logarithm of ie to the surface charge density (a) is proportional to En, that monomers must cross two barriers before emission, that the fraction of dimers depends on the external field and ion-ion interactions, and that En and the molecular details of the ions/surface determine the ion emission rate and composition from the RTIL/vacuum surface.

In order to reduce the level of carbon dioxide in the atmosphere, Shaikh et al. used amino acid aluminum electrolytic capacitor ionic liquids to reversibly capture carbon dioxide. The effects of CO₂ chemical adsorption on glycine tetramethylphosphine [P₁₁₁₁][Gly] and tetrabutylphosphoglycine [P₄₄₄₄][Gly] ILs were calculated using DFT, and molecular dynamics simulations were performed to study the physicochemical properties of ILs in pure form and in the presence of water. The study confirmed that the studied ILs can effectively capture CO₂. Figure 14 shows the activation energy barrier for CO₂ absorption on [Pu][Gly] in the gas phase, and a continuous solvation model was used. The first step of the reaction is the formation of an intermolecular complex between glycine and CO₂. In the solvent phase, the increase in the energy barrier can be attributed to the hydrophobicity and bulkiness of the cation. The effect of carbon dioxide on glycine salts and water generally reduces the energy barrier.

Abbaspour et al. simulated iron nanoclusters of different physical sizes in the aluminum electrolytic capacitor ionic liquid 1-butyl-1.1.1-trimethylammonium methane sulfonate [N₁₁₁₄][C₁SO₃] at two thermodynamic temperatures (300K and 500K) and atmospheric pressure using MD, and studied the effects of cluster size and temperature on some thermodynamic, structural and kinetic properties of the system. The results show that the absolute value of the solvation energy increases with the increase of the nanocluster size. Similarly, the absolute solvation energy increases with the increase of temperature. The effect of cluster size is much greater than that of temperature. There are at least two shells (double layers) around the nanocluster, and the anions are closer to the cluster surface than the cations. The researchers calculated the radial distribution functions (RDFs) of Fe-cations and Fe-anions at 300K and 500K, as shown in Figure 15. The Fe-cation and Fe-anion RDFs show two different peaks, which confirm the shells (double layers) around the two nanoclusters. The ions or ion clusters are attracted to the surface of the nanoparticles (positively charged) by electrostatic forces. Ions or ion clusters with a negative net charge form the first layer around the clusters, and counterions or ion clusters form the second layer on the nanoparticle surface through electrostatic attraction. The first peak of the Fe-anion RDF appears at a shorter distance than the Fe-cation RDF, indicating that the anions are closer to the cluster surface than the cations, a result consistent with the colloidal stability (DLVO) model. In the DLVO model, the first solvation shell of the anions surrounds the metal clusters, followed by a disordered layer of the cations.

Fig. 14 Gibbs free energy spectra of CO2 absorption on [P₁₁₁₁l[Gly] optimized in the gas phase (and in the solvent phase in brackets) relative to the precomplex in kcal mol^-1; the main distances are shown in A. The transition state has been localized both in the presence and absence of an explicit water molecule.

Figure  15 RDF of Fe-cations and Fe-anions at 300K and 500K

Marcinkowski et al. measured the density and sound velocity of four aluminum electrolytic capacitor ionic liquids (N-Z-based-N-methylmorpholine tetrafluoroborate [Mor_1,2l[BF₄l, N-butyl-N-methylmorpholine tetrafluoroborate [Mor_1，4][BF₄], N-octyl-N-methylmorpholine-tetrafluoroborate [Mor1,g][BF4] and N-decyl-N-methylmorpholine tetrafluoroborate [Mor.1o][BF4]) in acetonitrile (AN) at a temperature of 298.15~328.15K and atmospheric pressure. The association degree of IL in AN was calculated using MD simulation, and the limiting apparent molar volume was estimated and compared with the experimental value. The results show that the most important factor controlling the volume and acoustic properties of aluminum electrolytic capacitor ionic liquids is the size of the cations of aluminum electrolytic capacitor ionic liquids. In addition, AN interacts more strongly with N-alkyl-N-methylmorpholine tetrafluoroborate than with dimethyl sulfoxide and N,N-dimethylformamide, not only through strong ion-dipole interactions but also through stacking effects. The radial distribution function can easily predict the ionic association form of various types of ion pairs (IPs) present in solution []. Figures 16 and 17 correspond to the RDF between the cation center of mass and the anion center of mass of [Mor1, n][BF₄] and [Mor_1,n]FTFSI] ionic liquids, respectively.

Figure 16 Radial distribution functions between the cation mass center and anion mass center of [Mor_1,2][BF₄] (blue), [Mor_1,4][BF₄] (red), [Mor_1,8][BF₄] purple and [Mor_1,10][BF₄] (green) ionic liquids in acetonitrile obtained from molecular dynamics simulations

Figure 17 Radial distribution function between the mass center of the cation and the mass center of the anion of [Mor_1,2] [ITFSII] (blue), [Mor_1,4] [ITFSII](red), ([Mor_1,8] [ITFSII](purple) and [Mor_1,10] [ITFSII] (green) ionic liquids in acetonitrile obtained from molecular dynamics simulation

Sedghamiz et al. used classical molecular dynamics simulation to study the interfacial complexation of non-halogenated chiral ionic liquid (CIL) 1-butyl-3-methylimidazolium (T-4)-bis[(aS)-a-(hydroxy-0)phenylacetyl-xO]borate with propranolol enantiomers. The experimental results showed that the synthesized halogen-free chiral ionic liquid is a suitable candidate for the chiral discrimination of propranolol enantiomers. In their study, MD simulation was used to study the performance of [BMIm⁺[BSMB^–] as a chiral selector for the identification of propranolol enantiomers, and the density distribution, microstructure, orientation or cyclization, diffusion and hydrogen bond lifetime of propranolol enantiomers at the CIL/vapor interface were studied. The number and interaction strength of hydrogen bonds in the S-propranolol/CIL system were greater than those in the R-propranolol/CIL system, which increased the thermodynamic stability of the S-propranolol/CIL system by 9.93×10⁴ kJ·mol^-1. Figure 4-18 shows the simulation snapshots of the R- and S-propranolol/CIL systems, which show that these compounds are surface active and all located on the CIL surface. The density distribution of R- and S-propranolol enantiomers is shown in Figure 19.

Figure 18: Snapshot of the system after 5 ns simulation

(a) R-propranolol/CIL solution; (b) S-propranolol/CIL solution. Propranolol enantiomers are presented by ball and stick model, and CIL molecules are brown lines.
The color coding is: oxygen-red, nitrogen-blue, carbon-grey, hydrogen-white

Figure 19 Atomic density distribution of (a) R- and (b) S-propranolol/CIL solutions

Xiao et al. modified montmorillonite with ionic liquid 1-hexadecyl-3-methylimidazole chloride (C₁₆mimCl ) and studied the loading characteristics and mechanism of ionic liquid on montmorillonite for aluminum electrolytic capacitors. Molecular dynamics simulation (MDS) was used to construct a modified montmorillonite model with C₁₆mimCl  loading of 0.5CEC, 1.0CEC and 3.0CEC. The simulation results are shown in Figure 20. C₁₆mimCl  intercalation mainly affects the height and type of montmorillonite interlayers. Previous studies have proposed a variety of different hypotheses for the arrangement of cationic surfactants in montmorillonite interlayers, such as lateral monolayer, lateral bilayer, pseudo-trilayer, inclined monolayer and oblique bilayer. As can be seen from Figure 20, at low doses (e.g., 0.5CEC), C₁₆mimCl  is mainly arranged in the form of lateral bilayers in the montmorillonite interlayers, and the organic chains are relatively straight [Figure 4-20(b)]. As the dosage increases (e.g., 1.0CEC), C₁₆mimCl is arranged in a pseudo-three-layer style in montmorillonite [Figure 20(c)], which is consistent with the findings of Liu et al. If the dosage continues to increase (e.g., 3.0CEC), C₁₆mimCl will be embedded in the montmorillonite interlayer through hydrophobic interactions between alkyl chains, thereby destroying the arrangement of C₁₆mimCl  molecules and rearranging them. Most of them are concentrated in the middle of the montmorillonite interlayer and arranged into a tilted double layer [Figure 4-20(d)]. When the loading amount is close to saturation, the arrangement of C₁₆mimCl  remains unchanged. According to the simulation results, this study reveals the arrangement and modification mechanism of C₁₆mimCl  in montmorillonite, which can provide new ideas for the synthesis of organic montmorillonite and its application in pollutant removal.

Figure 20 Snapshots of C₁₆mimCl-Mt structures at different modifier loadings

a)Mt; (b)0.5 C₁₆mimCl-Mt; (c)1.0 C₁₆mimCl-Mt; (d)3.0 C₁₆mimCl-Mt
Green balls: Ca ions; Gray balls: C atoms; White balls: H atoms; Blue balls: N atoms; Light green balls: C1 atoms; Sticks: Water molecules

Sedghamiz et al. used molecular dynamics simulation to study the effect of single-walled carbon nanotubes (SWCNTs) on the enantiomeric separation ability of chiral ionic liquids. The non-halogenated chiral ionic liquid (CIL) 1-butyl-3-methylimidazole (T-4)-bis[(aS)-a-(hydroxy-0)phenylacetal-xO1 borate [BMIm⁺][BSMB^–] was used to separate propranolol enantiomers, and MD simulation was used to study the molecular level map of the behavior of CIL and propranolol enantiomers on the surface of single-walled carbon nanotubes. The density distribution, microstructure, radial distribution function, dimer presence autocorrelation function (DAF) and kinetic properties of propranolol enantiomers were also studied in CIL/CNT solutions. The study showed that carbon nanotubes (CNTs) improve the enantioselectivity of chiral ionic liquids by enhancing the interaction interface between the chiral selector and the compound enantiomers. The R-propranolol enantiomer has a stronger interaction with the CIL and CNT surfaces. In addition, stronger interactions were found between R-propranolol molecules, which resulted in different replacement modes of propranolol enantiomers on the CNT surface, causing R-propranolol molecules to diffuse more along the CNT and have the greatest interaction with CIL molecules. The diffusion coefficients between S-propranolol and R-propranolol molecules showed a higher difference compared to the system without CNT. It can be seen that the interaction, diffusion and mobility of propranolol enantiomers are strongly affected by CNT. Figure 21 shows the number radial (x density) distribution of S- and R-propranolol center of mass (CM) perpendicular to the CNT wall. The notable feature of the two enantiomers is that the oscillation of the CM number density extends along the x-axis, which indicates that the molecules are layered near the solid surface. The layered structure of the density distribution diagram and the peaks of the S-enantiomer are sharper, which represents the further diffusion of the molecules around the CNT in a radial distribution. On the other hand, the wider peak of the R-enantiomer indicates that the molecules are dispersed along the CNT surface and the layered structure along the x-axis is relatively low.

Figure 21. Mass density center distribution of R- and S-propranolol in CIL/CNT solution

Saha et al. combined docking experiments and molecular dynamics simulations to explore the interaction between KMP-11 protein and aluminum electrolytic capacitor ionic liquids choline dihydrogen phosphate (CDHP) solvent from multispectral studies. KMP-11 protein is a candidate vaccine for “Leishmaniasis” and does not contain any tryptophan (Trp) in its WT sequence. In this study, two single-point Trp of KMP-11 were generated with the help of intrinsic fluorescence, and the mutant proteins Y5W and Y89W were used as biomarkers of Trp. In the former mutant (Y5W), Trp was inserted near the N-terminus, while in the latter mutant (Y89W), the same residue was located near the C-terminus of the protein. MD simulations were used to predict and prove that the secondary structure of the WT and mutant proteins was almost not unfolded after the addition of CDHP molecules. Figure 22 shows discrete snapshots of the binding conformations captured at 0, 50ns, and 100ns from the MD trajectories of WTKMP-11-DCHP, Y5W-CDHP, and Y89W-CDHP.

Figure 22. Discrete snapshots showing the binding conformations of WTKMP-11-CDHP, Y5W-CDHP, and Y89W-CDHP captured at different time ranges.

Vázquez-Montelongo et al. used classical MD calculations to study the separation of aromatic compounds from aromatic/aliphatic mixtures using RTILs as solvents. Due to the highly charged nature of the solvent, an accurate force field is required to accurately describe the intermolecular interactions between the different mixture components. The multipolar/polarizable force field AMOEBA was used to study the ability of 1.3-dimethylimidazolium tetrafluorobromide [Dmim][BF4] and ethyl-methylimidazolium tetrafluorobromide [Emim][BF41 to extract benzene from a mixture of benzene and dodecane. The entire system consists of a gasoline model, a 1:1 mixture of n-dodecane (NC12) and benzene (PhH), and a combination of [Dmim][BF41 and [Emim][BF41 as extractants. The results show that [Dmim][BF4] has a better ability to extract benzene than [Emim][BF4]. Figures 23 and 24 are density distribution diagrams of the mixtures [Dmim][BF4]+PhH-NC12 and [Emim][BF4]+PhH-NC12, respectively. As can be seen from Table 3, the density of [Dmim][BF4]

Figure 23 Density distribution of the mixture Dmim][BF₄]+PhH-NC12 along the z-direction. The black, red, green, and blue lines represent the densities of PhH, NC12, Dmim, and BF₄ along the z-axis, respectively. This profile is obtained in the last 50 ns of the 150 ns full MD simulation.

Density distribution along z direction (nm)

The black, red, green, and blue lines represent the density of PhH, NC12, Emim, and BF₄ along the z-axis, respectively. This curve is available for the last 50 ns of a full 300 ns MD simulation.

PhH and NC12 molar concentration values ​​were higher than those of [Emim][BF₄] [Dmim][BF₄] resulted in a better distribution of PhH and NC12 in the ternary mixture. On the other hand, [Dmim][BF₄] exhibited higher b and S values ​​relative to [Emim][BF₄], which means that [Dmim][BF₄] was more efficient in extracting PhH.

Table 3 NC12 and PhH mole fractions (x), solute distribution ratios (β) and selectivities (S) of PhH-NC12+[Dmim][BF₄] and PhH-NC12+[Emim][BF₄] in hydrocarbon and IL-rich regions

The accuracy of MD results depends mainly on the accuracy of the interaction potential model used. Zhang et al. used MD to explore the effects of different interaction potential models on the electrolytic capacitor ionic liquid 1-ethyl-3-methylimidazolium tetrafluoroborate ([Emim][BF₄]) electrospray propulsion. The reduced charge all-atom model, the full-charge all-atom model, the effective coarse-grained model, and the Merlet coarse-grained model were compared and combined with experimental data to clarify which interaction potential models may be the most realistic. Calculations show that the reduced charge all-atom model is the most accurate model in electrospray molecular dynamics simulations. In contrast, the coarse-grained model cannot reveal the energy characteristics, the full-charge all-atom model cannot characterize the velocity characteristics, and the full-charge all-atom model and the coarse-grained model underestimate and overestimate the propulsion performance, respectively. At the same time, the study also found that the energy characteristics and velocity distribution of particles in the Taylor cone are almost unchanged under different working conditions. Figure 25 is the full-atom model and the corresponding coarse-grained model of [Emim][BF₄] used in the experiment.

Figure 25 All-atom model of [Emim][BF₄] and corresponding coarse-grained model
(a) EFCG model of [Emim][BF₄]; (b) Merlet coarse-grained model of [Emim][BF₄]

Chen et al. used molecular dynamics simulation to study the extraction mechanism of electrolytic capacitor ionic liquid/n-hexane/ethyl acetate from a microscopic perspective. The four electrolytic capacitor ionic liquids studied included [Hmim][PF₆], [Hmim][OTF], [Bmim][PF₆] and [Hmim][NTf₂]. The radial distribution function, spatial distribution function and self-diffusion coefficient between the substances in the system were calculated by molecular dynamics method to select the best electrolytic capacitor ionic liquid extractant. In order to verify the good consistency between the simulation value and the experimental value, the deviation between the simulation value and the experimental value of the system material density was compared. The deviation was less than 2%. The results show that the order of extraction ability of electrolytic capacitor ionic liquid for n-hexane-ethyl acetate mixture is [Hmim][PF₆]>[Hmim][NTf₂]>[Hmim][OTF]>[Omim][PF₆]. However, [PF₆] is easily hydrolyzed to produce HF, which causes great damage to the environment and equipment. Therefore, [Hmim][NTf₂] is more suitable for the separation of n-hexane-ethyl acetate mixed system. As shown in Figure26, the spatial distribution function (SDF) reflects the effect of two different ILs systems on ethyl acetate (EA). Among them, the extraction ability of different electrolytic capacitor ionic liquids for EA is related to the surface area of ​​different electrolytic capacitor ionic liquids centered on the ion. The larger the surface area, the stronger the extraction ability. Figure 26 (a) shows a three-dimensional diagram of anions surrounded by EA. The equivalent particle distance of EA in PF₆– is 1.5nm^-3. Therefore, [Hmim][PF₆] has a stronger extraction ability for EA than [Omim][PF₆]. As shown in Figure 26 (b), when the equivalent particle distance of EA near [Hmim] is 3.3nm^-3, the surface area of ​​EA extracted by [Hmim]⁺[PF6] with [Hmim] as the central atom is greater than that of [Hmim][OTF]. Therefore, the EA extraction ability of [Hmim][PF₆] is greater than that of [Hmim][OTF].

Vucemilovic-Alagic et al. obtained structural characterization data of [C₂mim][NTf₂] in bulk and nanoconfined environments through molecular dynamics simulation, confirming the insights of molecular dynamics simulation on the structural organization and diffusion dynamics of electrolytic capacitor ionic liquids at the interface between solid and vacuum. Three different charge methods and three charge proportional factors were used to analyze the ionic liquid film of the overall electrolytic capacitor ionic liquid in contact with the hydroxylated aluminum oxide surface at the vacuum interface. Figure 27 (a) shows the structure of the ionic liquid composed of imidazolyl cation [C₂mim]⁺ and prototype anion [NTF₂]^–obtained in molecular dynamics simulation.

Figure 26 SDF curves: (a) EA around [PF₆]^–, with an isovalue of 1.5 particles·nm^-3 (green surface represents [Hmim]⁺, blue-green surface represents [Omim]⁺); (b) EA around the isovalue line [Hmim]⁺ is 3.3 particles·nm^-3 (green surface represents [PF₆]^–, purple surface represents [OTF]^–)

Figure 27 (a) Structures of cations and anions that make up the ionic liquids of electrolytic capacitors; (b) Ionic liquids consisting of 1000 ion pairs (L-type system), with cations and anions shown in red and blue, respectively; (c) ILs consisting of 1400 ion pairs between two vacuum plates (V-L-V model system); (d) ILs consisting of 1800 ion pairs in the S-L-V model system, where the solid substrate is a fully hydroxylated (0001) sapphire plate with dimensions of 7.57 nm × 6.29 nm × 2.12 nm.

Rivera-Pousa et al. studied the interaction between ammonia and the protic ionic liquid ethylammonium nitrate (EAN) by molecular dynamics simulations. In this work, they analyzed for the first time a mixture of ammonia and ILs, where the ILs included cations with similar molecular head groups NHt. At the same time, two different molecular models of NH3 were compared, one was the traditional 4-position model and the other was a novel 6-position model. The model accurately describes the electrostatic interaction by including the dipole and quadrupole electron distribution around the nitrogen atoms and the point charges on the atomic positions. In addition, the structural picture was improved by calculating the spatial distribution function (SDF) of the different species in the mixture. Since ammonia is the central point of this work, the three hydrogens of each ammonia molecule are used to define the local reference system, thereby obtaining the isosurfaces of the numerical density of each ILs species. In Figure 28, the preferential positions occupied by each particle around the ammonia molecule and the differences between the two ammonia models can be observed.

Figure 28 Spatial distribution functions of cations (c) and anions (d) around NH3 molecules in the 4-position (a) and 6-position (b) models with a molar concentration of 50%

Rezaeian et al. conducted molecular dynamics simulations and quantum mechanical calculations on 1-ethyl-3-methylimidazole salt ionic liquid ([C₂mim]) containing amino acid anions, and used a mixture of water, methanol and chloroform to study the interaction effect between cations and anions in amino acid ionic liquids (AAILs). The molecular dynamics simulation results show that the molar ratio of chloroform, methanol and water affects the interaction of electrolytic capacitor ionic liquids. The polarity and molar ratio of the solvent play a major role in ion aggregation and the formation of cross-complexes in electrolytic capacitor ionic liquids. DFT calculations were used to study the interaction of AAIL at the M06-2X/6-311++G (d, p) theoretical level. The results show that the maximum interaction energy between cations and anions of electrolytic capacitor ionic liquids is higher than that in the gas phase. In addition, the interaction energy of methanol and water is lower than that of chloroform, which confirms that the structure of electrolytic capacitor ionic liquids is affected in the presence of polar solvents. Natural bond orbital (NBO) and non-covalent interaction (NCI) analysis confirmed that the formation of hydrogen bonds is attributed to the important interaction between ion pairs in electrolytic capacitor ionic liquids. The final configurations of [Emim][Gly] and [Emim][Arg] obtained during the 20ns MD simulation are shown in Figure 29. As can be seen from the figure, isolated clusters are formed at a low molar ratio of methanol and water. However, when the molar ratio of these solvents is increased to 0.7, the ability to form hydrogen bonds with AAIL is improved, which is due to the strong interaction between the oxygen atoms of methanol and water and the AAIL anions. Therefore, in the presence of these solvents, the possibility of forming cross-complexes increases. On the other hand, due to the steric effect of chloroform on H-bond formation, the ions of AAIL aggregate when the molar ratio of chloroform is high. In order to understand the interaction strength between cations and anions in the presence of solvent molecules from a molecular perspective, the structure of AAIL was optimized in the gas phase and different solvents at the M06-2X/6-311++G (d, p) theoretical level, and the results are shown in Figure 30.

Figure 29 Structures obtained during 20 ns MD simulation (a1, b1) The molar ratios of chloroform in [Emim][Gly] and [Emim][Arg] are 0.3, 0.5, and 0.7, respectively (from top to bottom); (a2, b2) The molar ratios of methanol in [Emim][Gly] and [Emim][Arg] are 0.3, 0.5, and 0.7, respectively (from top to bottom); (a3, b3;) The molar ratios of water in [Emim][Gly] and [Emim][Arg] are 0.3, 0.5, and 0.7, respectively (from top to bottom). The imidazolium cation, glycine anion, arginine anion, and solvent are represented by green, blue, purple, and red, respectively.

Figure 30 Optimized structures of different AAILs in the gas phase at the M06-2X/6-311++G (d, p) theoretical level

Zhou et al. studied the structure and properties of 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF₄])-water system at the vapor-liquid interface by molecular dynamics simulation in order to investigate the water absorption and desorption behavior of ILs. The results showed that there was a good linear relationship between the surface tension in the interfacial layer and the water content. By calculating the center of mass RDF (COMRDF), the detailed structural information of the interface and the bulk phase can be understood, the differences can be found, and the law of its change with water content can be obtained. Figure 31 shows the RDF of the center of mass of five systems with different water contents (400ILA400IL+100W, A400IL+1600W, M400IL+10kW and M100IL+10kW). The cation is represented by the center of mass of the imidazole with the most concentrated positive charge. The structure of the second solvation layer of ILÉ in the bulk phase is first destroyed by higher water content, and can only be destroyed when the water content is extremely high. The RDF of the center of mass of the imidazole ring at the interface shows similar regularity.

Figure 31 RDF of the cation and anion mass centers in the body and at the interface

(a), (c) and (e) are the RDFs of imidazolium-anion, imidazolium-imidazolium and anion-anion in the bulk, respectively; (b), (d) and (f) are the RDFs of imidazolium ring-anion, imidazolium ring-imidazolium ring and anion-anion at the interface, respectively.

The COMRDF of anion-anion is different from the previous two. The first peak in the bulk phase starts to split from the A400IL+1600W system, and the first peak of the M400IL-10kW system and the M100IL-10kW system is completely divided into two. The position of the first peak is 0.6~0.7nm higher than the peak of the A400+800W system. The anion peak is formed by the attraction of multiple anions around a cation or water molecule. According to the spatial distribution function (SDF) (Figure 32), the B atoms in the anion are mainly distributed on both sides of the HCR of the imidazole ring and outside the HCW, while the B atoms in the cation are mainly distributed along O-H in the water molecule.

Figure 32 Spatial distribution function of B atoms in (a) imidazole ring and (b) water surrounding anions in A400IL+100W

a) The red and yellow boundary contour surfaces are drawn at 30 and 15 times the average density, respectively; (b) The red and yellow boundary contour surfaces are drawn at 14 and 7 times the average density, respectively

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