Method for predicting the conductivity of compositions containing organic ionic conductive agents and polymers.

Abstract

This invention provides a method for predicting the conductivity of compositions containing organic ionic conductive agents and polymers. [Solution] A method for predicting the conductivity of a composition containing an organic ionic conductive agent and a polymer, comprising the steps of: (S1) a computer creating an analytical model including the organic ionic conductive agent and the polymer; and (S2) applying a voltage to the analytical model and calculating the ion migration distance.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the conductivity of compositions containing organic ionic conductive agents and polymers. [Background technology]

[0002] With the rapid development of IT technology in recent years, simulation techniques for predicting the physical properties of various materials are being widely used. For example, molecular dynamics (MD) simulations are commonly used as a method for predicting physical properties such as ionic conductivity and diffusion coefficient in various battery materials.

[0003] For example, Non-Patent Document 1 discloses a method for creating a model that includes polyethylene oxide and inorganic ions (NaClO4), simulating the diffusivity of the ions, determining the diffusion coefficient from the results, and calculating the conductivity based on a predetermined theoretical formula. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Effect of Coordination Behavior in Polymer Electrolytes for Sodium-Ion Conduction:A Molecular Dynamics Study of Poly(ethylene oxide) and Poly(tetrahydrofuran),Francielli S. GenierIan D. Hosein,Macromolecules Vol54 / Issue18 [Overview of the Initiative] [Problems that the invention aims to solve]

[0005] However, when verifying the prediction accuracy by the above conventional method, it has been revealed by the study of the inventors that depending on the type of ion-molecule structure, the correlation coefficient may be low and the prediction accuracy may be insufficient. For example, in a system containing an organic ion conductive agent with low motility, it is difficult to accurately simulate the diffusion coefficient of ions, etc., and it has been revealed that there is room for further improvement.

[0006] The present invention has been made in view of such circumstances, and an object thereof is to newly provide a method for predicting the conductivity of a composition containing an organic ion conductive agent and a polymer.

[0007] Also, in one embodiment of the present invention, in a method for predicting the conductivity of a composition containing an organic ion conductive agent and a polymer, the object is to improve the prediction accuracy.

Means for Solving the Problems

[0008] The present invention has the following [1] to [6] as its gist. [1] A method for predicting the conductivity of a composition containing an organic ion conductive agent and a polymer, wherein a computer creates an analysis model including an organic ion conductive agent and a polymer (step S1), and applies a voltage to the analysis model to calculate the movement distance of ions (step S2), a method for predicting the conductivity of a composition containing an organic ion conductive agent and a polymer. [2] The method for predicting conductivity according to [1], wherein step (S2) is a step of applying a voltage to the analysis model and calculating the mean square displacement of ions by molecular dynamics simulation. [3] The method for predicting conductivity according to [1] or [2], wherein step (S2) is a step of applying a voltage to the analysis model and calculating the mean square displacement of cations by molecular dynamics simulation, and predicting conductivity based on the mean square displacement of the cations. [4] A method for predicting conductivity according to any one of [1] to [3], wherein the above-mentioned organic ionic conductive agent is one or more selected from the group consisting of imidazolium salts, quaternary ammonium salts, and quaternary phosphonium salts. [5] A method for predicting conductivity according to any one of [1] to [4], wherein the polymer is at least one of hydrin rubber and nitrile rubber. [6] A method for predicting conductivity as described in any of [1] to [5], wherein the above step (S1) is a step of creating an analytical model that includes an organic ionic conductive agent, a polymer, and water. [Effects of the Invention]

[0009] According to the present invention, a novel method for predicting the conductivity of a composition containing an organic ionic conductive agent and a polymer can be provided.

[0010] In one embodiment of the present invention, the accuracy of predicting conductivity can be improved in a method for predicting the conductivity of a composition containing an organic ionic conductive agent and a polymer.

[0011] For example, according to one embodiment of the present invention, simulations can be performed while simultaneously considering not only the diffusion motion of ions but also the association and dissociation of cations and anions. As a result, even when the molecular structure of the ions is changed, the simulation results and experimentally measured values ​​will correlate better, thereby improving the accuracy of conductivity prediction. [Brief explanation of the drawing]

[0012] [Figure 1] This flowchart shows an example of a processing procedure for an conductivity prediction method according to one embodiment of the present invention. [Figure 2] This figure shows the multiple regression correlations of the examples. [Figure 3] This figure shows the multiple regression correlation of the comparative example. [Modes for carrying out the invention]

[0013] Figure 1 is a flowchart showing an example of the main processing steps of a method for predicting the conductivity of a composition containing an organic ionic conductive agent and a polymer according to one embodiment of the present invention (hereinafter sometimes referred to as "this prediction method"). This prediction method includes the steps of: (S1) creating an analytical model (S2) in which a computer creates an analytical model (S1) including an organic ionic conductive agent and a polymer; and (S2) applying a voltage to the analytical model and calculating the ion migration distance. However, this prediction method is not limited to the contents of the flowchart shown.

[0014] According to one embodiment of the present invention, by improving the mobility of ions, it becomes possible to perform simulations that simultaneously consider not only the diffusion motion of ions but also the association and dissociation of cations and anions. As a result, even when the molecular structure of the ions is changed, the simulation results and experimentally measured values ​​will correlate better, and the accuracy of conductivity predictions can be improved.

[0015] Furthermore, in one embodiment of the present invention, the improved ion mobility allows for a more accurate simulation of the ion mobility in the actual material corresponding to the analytical model. Additionally, the increased total ion momentum improves the statistical accuracy regarding ion migration distance, thereby further enhancing the accuracy of conductivity prediction.

[0016] Furthermore, in one embodiment of the present invention, among the methods for predicting conductivity based on the ion migration distance as described above, it is preferable to perform a step of calculating the cation migration distance by molecular dynamics simulation and predict conductivity based on the cation migration distance, in order to further improve the accuracy of the conductivity prediction.

[0017] Furthermore, in one embodiment of the present invention, from the viewpoint of further improving the accuracy of conductivity prediction, it is preferable to perform the step of creating an analytical model that includes, for example, an organic ionic conductive agent, a polymer, and water. Embodiments of the present invention will be described in detail below.

[0018] Each step shown in Figure 1 is performed by a computer. The computer is similar to those used in conventional CAE (Computer-Aided Engineering). This prediction method typically involves software and hardware working together to execute pre-stored processing routines in an information processing device such as a personal computer, which is equipped with a processing unit (CPU), ROM, working memory, storage devices such as magnetic disks, input devices such as a keyboard and mouse, and display devices such as a display. Specifically, for example, a molecular dynamics calculation program is stored in a storage device, and the CPU executes this program while simultaneously reading the initial structure stored in a storage medium such as RAM, or input from an input device such as a keyboard, as well as various conditions required for the calculation.

[0019] <Subject of analysis> The target of analysis in this prediction method is a composition containing organic ionic conductive agents and polymers. In step S1, an analytical model including the organic ionic conductive agent and polymer is created.

[0020] (Organic ionic conductive agent) As the organic ionic conductive agent, known organic ionic conductive agents can be used as appropriate. An organic ionic conductive agent is an ionic conductive agent that contains molecular ions formed from organic matter, and specifically, although not limited to the following, examples include quaternary ammonium salts, quaternary phosphonium salts, and imidazolium salts.

[0021] As the cation of the quaternary ammonium salt, those having one or more alkyl groups or aryl groups (such as methyl group, ethyl group, propyl group, butyl group, hexyl group, octyl group, decyl group, phenyl group, xylyl group, etc.) with about 1 to 18 carbon atoms can be mentioned. The alkyl group or aryl group of the cation of the quaternary ammonium salt preferably has 1 to 10 carbon atoms. As the anion of the quaternary ammonium salt, F - , - , - , - , - , - , , - , - , , - , - , - , - , - , - , 2- , - ,

[0022] , - , - , - , - , Cl - , Br - , I - and other halogen ions, ClO4 - , BF4 - , PF6 - , SO4 2- , HSO4 - , C2H5SO4 - , CF3COO - , CF3SO3 - , (CF3SO2)2N - , (CF3CF2SO2)2N - , CF3(CF2)3SO3 - , (CF3SO2)3C - , CF3(CF2)2COO - and the like can be mentioned.

[0022] As the cation of the quaternary phosphonium salt, those having one or more alkyl groups or aryl groups (such as methyl group, ethyl group, propyl group, butyl group, hexyl group, octyl group, decyl group, phenyl group, xylyl group, etc.) with about 1 to 18 carbon atoms can be mentioned. The alkyl group or aryl group of the cation of the quaternary phosphonium salt preferably has 1 to 10 carbon atoms. As the anion of the quaternary phosphonium salt, F - , Cl - , Br - , I - and other halogen ions, ClO4 - , BF4 - , PF6 - , SO4 2- , HSO4 - , C2H5SO4 - , CF3COO - , CF3SO3 - , (CF3SO2)2N- , (CF3CF2SO2)2N - CF3(CF2)3SO3 - (CF3SO2)3C - CF3(CF2)2COO - These are some examples.

[0023] Examples of imidazolium salts include unsubstituted imidazolium salts, 1-alkylimidazolium salts, 3-alkylimidazolium salts, 1,3-dialkylimidazolium salts, and 1,2,3-trialkylimidazolium salts. More specifically, examples include 1-methylimidazolium salt, 1,3-dimethylimidazolium salt, 1,3-diethylimidazolium salt, 1,3-dipropylimidazolium salt, 1,3-dibutylimidazolium salt, 1,3-dicyclohexylimidazolium salt, 1-ethyl-3-methylimidazolium salt, 1-propyl-3-methylimidazolium salt, 1-butyl-3-methylimidazolium salt, 1-hexyl-3-methylimidazolium salt, 1-ethyl-2,3-dimethylimidazolium salt, 1-propyl-2,3-dimethylimidazolium salt, and 1-butyl-2,3-dimethylimidazolium salt. As for the anion of the imidazolium salt, F - ,Cl - ,Br - ,I - Halogen ions such as ClO4 - BF4 - PF6 - SO4 2- HSO4 - C2H5SO4 - CF3COO - CF3SO3 - , (CF3SO2)2N - , (CF3CF2SO2)2N - CF3(CF2)3SO3 - (CF3SO2)3C - CF3(CF2)2COO - These are some examples.

[0024] (polymer) Examples of polymers include rubber, resins, and elastomers. Specifically, although not limited to the following, examples include polymers that have polarity as a whole molecule due to having polar groups in their molecular structure. Examples of polar polymers, although not limited to the following, include polymers with an intrinsic volume resistivity of 1 × 10⁻⁶. 14 Examples include polar polymers with a value of Ω·cm or less. The intrinsic volume resistivity is 1 × 10⁻⁶. 13 Less than or equal to Ω·cm, or 1 × 10⁻⁶ 12 The polarity may be less than or equal to Ω·cm. Examples of the above polar groups include chloro groups, nitrile groups, carboxyl groups, and epoxy groups.

[0025] Examples of polar rubbers analyzed in this prediction method include hydrin rubber, nitrile rubber (NBR), urethane rubber (U), acrylic rubber (a copolymer of acrylic acid ester and 2-chloroethyl vinyl ether, ACM), chloroprene rubber (CR), and epoxidized natural rubber (ENR).

[0026] Examples of hydrin rubber include epichlorohydrin homopolymer (CO), epichlorohydrin-ethylene oxide binary copolymer (ECO), epichlorohydrin-allyl glycidyl ether binary copolymer (GCO), and epichlorohydrin-ethylene oxide-allyl glycidyl ether terpolymer (GECO).

[0027] Furthermore, the polymer may include non-polar rubbers, such as isoprene rubber (IR), natural rubber (NR), styrene-butadiene rubber (SBR), butadiene rubber (BR), and silicone rubber (Q).

[0028] A specific example of a material to be analyzed using this prediction method is conductive rubber material.

[0029] <Molecular Dynamics Simulation> This prediction method involves creating an analytical model that includes organic ionic conductive agents and polymers, and using this analytical model to calculate the displacement distances, such as the mean squared displacement (MSD) of ions, through molecular dynamics simulations. The molecular dynamics simulation itself and the method for calculating the displacement distances, such as the mean squared displacement (MSD), follow known techniques. For example, a potential function is set between the atoms constituting the organic ionic conductive agent and polymer, the atomic positions are gradually displaced according to Newton's equations of motion, and equilibration is performed so that the atomic positions, which change over time, reach a statistical equilibrium state, and the displacement distances, such as the mean squared displacement (MSD), are calculated.

[0030] A key feature of this prediction method, which differs from conventional techniques, is that in the step of calculating the displacement distance in molecular dynamics simulations, voltage application conditions are set and the displacement distance of the ions is calculated. This prediction method is excellent because it can improve the accuracy of conductivity prediction by focusing on the relationship between ion mobility and conductivity and calculating the displacement distance of ions under voltage application conditions. The prediction method of the present invention will be described in more detail below.

[0031] <Step S1> This prediction method includes step S1, in which a computer creates an analytical model that includes organic ionic conductive agents and polymers.

[0032] In step S1, the initial structure is constructed by placing at least an organic ionic conductive agent model (cation-anion pair) that models an organic ionic conductive agent, and a polymer model that models a polymer, within the cell. Each model is constructed, for example, as an all-atom model. The potential constants used to define bond lengths, bond angles, dihedral angles, etc., for each model are defined according to common methods, for example, based on known papers. The cell, which is a virtual space, is, for example, a rectangular prism or a cube, and periodic boundary conditions are defined.

[0033] In step S1, from the viewpoint of further improving the accuracy of conductivity prediction, a water model that simulates water may be optionally placed in the cell to construct the initial structure. Preferably, the water model is placed based on the amount of moisture absorbed by the object to be analyzed (actual measured value) which has been measured in advance.

[0034] Step S1 may include a relaxation step from the perspective of equilibration. Specifically, based on molecular dynamics calculations, the motion of the organic ionic conductive agent model and polymer model in their initial structures is advanced over a predetermined time at predetermined time intervals, according to conventional methods. For example, an ensemble in which pressure and temperature are kept constant, or volume and temperature are kept constant, is applied. The relaxation step is calculated repeatedly until the initial structures of the organic ionic conductive agent model and polymer model are sufficiently relaxed, and an analysis model for the equilibrium state is created.

[0035] Furthermore, from the viewpoint of accelerating relaxation, the relaxation step may be carried out under high-temperature conditions, for example, 150°C or higher, preferably 150 to 1100°C, and more preferably 150 to 200°C. In addition, the relaxation step may be carried out in stages.

[0036] Furthermore, step S1 may include a compression step, from the viewpoint of bulking up depending on the object of analysis. Specifically, based on molecular dynamics calculations, an external pressure may be set on the cell containing the organic ionic conductive agent model and polymer model, according to a conventional method, for a predetermined time and in predetermined time increments, thereby compressing the cell. The compression step is repeated until the density of the polymer model in the cell reaches a density corresponding to, for example, the density of the object of analysis (measured value of the actual substance). It is preferable to perform the compression step before the relaxation step described above.

[0037] <Step S2> This prediction method includes step S2, in which a computer applies a voltage to an analytical model and calculates the ion migration distance. By performing step S2, the accuracy of the conductivity prediction can be improved. Step S2 involves, for example, applying a voltage to the analytical model when the system in the analytical model is in equilibrium, and calculating the ion migration distance by molecular dynamics simulation. More specifically, it is preferable to apply a voltage to the analytical model when the system in the analytical model is in equilibrium, and calculate the mean squared displacement (MSD) of the ions by molecular dynamics simulation. Mean squared displacement (MSD) is a statistical index that represents the magnitude of motion, and it means the sum of the squares of the distances between the start and end points of a motion over a predetermined time interval. The dimensions of mean squared displacement (MSD) are not particularly limited and it is a concept that encompasses one-dimensional, two-dimensional, and three-dimensional mean squared displacement.

[0038] In this prediction method, from the viewpoint of further improving prediction accuracy, step S2 is preferably a step of calculating the cation migration distance.

[0039] The direction of voltage application, voltage intensity, application time, and temperature conditions in step S2 are not particularly limited and can be set as appropriate depending on the object of analysis. The voltage application conditions in step S2 are not particularly limited, but for example, from the viewpoint of ion mobility, a value of 0.04 to 0.1 V / Å is possible. The temperature conditions in step S2 are not particularly limited, but for example, from the viewpoint of ion mobility and glass transition temperature, a value of 27 to 100°C is possible. The time conditions in step S2 are 40 to 300 ns.

[0040] The voltage intensity should be set appropriately considering the coordination distance between the cation and anion, and the electric field strength due to the cation. For example, if the strength of the externally applied electric field is about 0.1 times that of the electric field created by the cation at the cation-anion coordination distance, the mobility is considered to remain unchanged. Therefore, if the interatomic distance between the ions is 5-6 Å and the electric field created by the cation is 4.0-5.8 × 10⁻¹⁰ 9 If we consider the voltage as V / m, the externally applied voltage should be 0.1V / Å or less.

[0041] <Advantages of this prediction method> Conventional molecular dynamics simulations, which do not apply voltage, tend to underestimate the momentum of organic ionic conductive agents, making it difficult to accurately simulate ion diffusion coefficients and other parameters. Furthermore, the theoretical formulas for calculating conductivity from diffusion coefficients in conventional techniques are based on a dilute state (a state where ions do not interact much), but in reality, many materials have a large amount of ions added, and these ions are constantly interacting, so the assumptions of the theoretical formulas may not always hold true. Changing the ion molecular structure also changes the degree of deviation from the assumptions, which is thought to decrease the correlation coefficient. Moreover, because organic ionic conductive agents have large molecular structures and are not easily mobile, even after long-term calculations using conventional molecular dynamics simulations, the total momentum is small, and even with the same weight, the number of added molecules is small, resulting in very low statistical accuracy. Furthermore, the behavior of ions is affected by factors such as hydration due to absorbed water, which increases the ion's motion. However, this aspect is not taken into account in the conventional molecular dynamics simulations mentioned above, and it is presumed that the total momentum of the ions is excessively reduced.

[0042] In contrast, this prediction method, characterized by the step of applying the voltage described above, can significantly increase the mobility of ions constituting organic ionic conductive agents, enabling simulations that consider not only the diffusion motion of ions but also the association and dissociation of cations and anions simultaneously. As a result, for example, it becomes unnecessary to use the theoretical formulas that assume a dilute state as shown in the aforementioned conventional techniques, and the simulation results and experimentally measured values ​​correlate better even when the molecular structure of the ions is changed. In particular, by calculating the migration distance of cations formed from organic materials under voltage application conditions using molecular dynamics simulations, particularly excellent prediction accuracy can be achieved.

[0043] Furthermore, one embodiment of the present invention is excellent in that it can further improve the accuracy of conductivity prediction by performing the step of creating an analytical model that includes an organic ionic conductive agent, a polymer, and water.

[0044] As described above, the excellent present invention can contribute to elucidating combinations of polymers and ions that have excellent electrical conductivity durability in the design of conductive materials, and can also contribute to establishing suitable material design guidelines.

[0045] <Prediction device> Furthermore, one embodiment of the present invention is a prediction device that predicts the conductivity of a composition using software that performs each of the above steps. Specifically, for example, this is a prediction device that predicts the conductivity of a composition using software that performs the steps of creating an analytical model (S1) including an organic ionic conductive agent and a polymer, and applying a voltage to the analytical model and calculating the ion migration distance (S2). [Examples]

[0046] The following describes embodiments of the present invention. However, the present invention is not limited to these embodiments.

[0047] [Example 1] The subject of analysis in this embodiment is a conductive composition containing an organic ionic conductive agent and a polymer. The organic ionic conductive agent is an organic ionic conductive agent (1) consisting of a combination of the cation shown in [Chemical Formula 1] and the anion shown in [Chemical Formula 2] below, and the polymer is a hydrin polymer. Furthermore, considering the hygroscopicity of the above composition, the composition also contains water.

[0048] [ka] [ka]

[0049] Using molecular dynamics simulation software (LAMMPS), 50 modeled hydrin polymers, 7 modeled organic ionic conductive agents (1), and 82 modeled water molecules were placed in a cubic cell to construct the initial structure (density 0.10 g / cm³). 3 ), and molecular dynamics simulations showed that the cell was compressed under the conditions of 27°C × 1 ns and 0.1 MPa (compressed density 0.99 g / cm³). 3 Compression and equilibration were performed. Post-compression equilibration involved relaxation at 177°C for 50 ns, followed by further relaxation at 77°C for 1 ns. The amount of water, 82 units, is based on the actual amount of moisture absorbed, determined from actual moisture absorption experiments.

[0050] Next, a voltage of 0.1 V / Å was applied to one axis (Z-axis direction) of the cubic cell, and the mean square displacement (MSD) of the cation was calculated by molecular dynamics simulation under the conditions of 77°C × 50 ns.

[0051] [Examples 2-6] As alternative organic ionic conductive agents to those modeled in Example 1, five types of organic ionic conductive agents (2) to (6) were prepared and each was modeled. Except for changing the type of organic ionic conductive agent to one of the above (2) to (6), the mean square displacement (MSD) of the cation was calculated by molecular dynamics simulation using the same procedure as in Example 1.

[0052] [Comparative Example 1] In Example 1, the mean square displacement (MSD) of the cation was calculated by molecular dynamics simulation using the same procedure as in Example 1, except that no voltage was applied.

[0053] [Comparative Examples 2-6] In Examples 2 to 6 described above, the mean squared displacement (MSD) of the cations was calculated by molecular dynamics simulation using the same procedure, except that no voltage was applied.

[0054] (Superiority in prediction accuracy under voltage application conditions) The multiple regression correlation between the values ​​calculated from the mean squared displacements obtained from the above simulations for each embodiment (each SIM value) and the measured values ​​(conductivity coefficient σ) using the actual physical objects was evaluated. The results are shown in Figure 2. In addition, the multiple regression correlation between the values ​​calculated from the mean squared displacements obtained from the simulations for each comparative example (each SIM value) and the measured values ​​(conductivity coefficient σ) using the actual physical objects was evaluated. The results are shown in Figure 3. In Figures 2 and 3, the vertical axis represents the measured values ​​(conductivity coefficient σ), and the horizontal axis represents the SIM values ​​(Å / ns).

[0055] As shown in Figures 2 and 3, molecular dynamics simulations that calculate the mean square displacement of cations under conditions where no voltage is applied show insufficient prediction accuracy, whereas molecular dynamics simulations that calculate the mean square displacement of cations under conditions where voltage is applied show a significant improvement in prediction accuracy.

[0056] From these results, it was confirmed that the accuracy of predictions can be improved by performing molecular dynamics simulations to calculate the ion migration distance under conditions where a voltage is applied.

[0057] (Advantages of calculating cation migration distance) Furthermore, when molecular dynamics simulations were performed in the same manner as in each example, except that the mean squares displacement of ions (both anions and cations) was calculated instead of cations, it was confirmed that the prediction accuracy was significantly improved compared to each comparative example. However, it was confirmed that the prediction accuracy in the molecular dynamics simulation, which calculates only the mean squares displacement of cations as described above, was superior.

[0058] (Advantages of analytical models that include water) Furthermore, when molecular dynamics simulations were performed in each example in the same manner except that the water model was not placed in the cell, i.e., when molecular dynamics simulations were performed using an analysis model without water, it was confirmed that the prediction accuracy was significantly improved compared to each comparative example. However, it was confirmed that the prediction accuracy in the molecular dynamics simulation in which the water model was placed in the cell and the mean squared displacement was calculated was even better. [Industrial applicability]

[0059] The present invention includes a step S1 in which a computer creates an analytical model containing an organic ionic conductive agent and a polymer, and a step S2 in which a voltage is applied to the analytical model and the ion migration distance is calculated by molecular dynamics simulation, thereby improving the accuracy of conductivity prediction. Furthermore, according to the present invention, for example, in the design of conductive materials, it can contribute to elucidating polymer and ion combinations that have excellent current-carrying durability, and can also contribute to establishing suitable material design guidelines.

Claims

1. A method for predicting the conductivity of a composition containing an organic ionic conductive agent and a polymer, A method for predicting the conductivity of a composition containing an organic ionic conductive agent and a polymer, comprising the steps of: (S1) creating an analytical model containing an organic ionic conductive agent and a polymer using a computer; and (S2) applying a voltage to the analytical model and calculating the ion migration distance.

2. The method for predicting conductivity according to claim 1, wherein step (S2) is a step of applying a voltage to the analytical model and calculating the mean square displacement of ions by molecular dynamics simulation.

3. The method for predicting conductivity according to claim 1 or 2, wherein step (S2) is a step of applying a voltage to the analytical model and calculating the mean square displacement of the cation by molecular dynamics simulation, and the conductivity is predicted based on the mean square displacement of the cation.

4. The method for predicting conductivity according to claim 1 or 2, wherein the above-mentioned organic ionic conductive agent is one or more selected from the group consisting of imidazolium salts, quaternary ammonium salts, and quaternary phosphonium salts.

5. The method for predicting conductivity according to claim 1 or 2, wherein the polymer is at least one of hydrin rubber and nitrile rubber.

6. The method for predicting conductivity according to claim 1 or 2, wherein step (S1) is the step of creating an analytical model comprising an organic ionic conductive agent, a polymer, and water.

Images