Preparation method of low-temperature-resistant organic liquid flow battery electrolyte

Abstract

The application relates to the technical field of low-temperature batteries, and discloses a preparation method of a low-temperature-resistant organic liquid flow battery electrolyte, which comprises the following steps: S1, optimizing the composition and proportion of the electrolyte, selecting a bipolar porphyrin molecule as an active material, a low-viscosity ionic liquid as a supporting electrolyte, an ultralow-freezing-point solvent as a cosolvent, and adding a pi-pi stacking enhancer; S2, optimizing the active molecule structure through quantum chemical calculation, calculating the HOMO-LUMO energy level difference, and making the HOMO-LUMO energy level difference be in the interval of 3.0 eV to 5.0 eV; and S3, adjusting the molecular configuration and bond length through quantum tunneling effect calculation. The technical scheme combining quantum tunneling effect and electrochemical stability is introduced, so that the effect of maintaining the stability of the battery in a low-temperature environment is achieved, an accurate low-temperature battery performance evaluation method is provided through the combination of molecular dynamics simulation and experimental verification, and the shortcomings of traditional technologies in predicting the behavior of the battery in a low-temperature environment are overcome.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of low-temperature batteries, in particular to a preparation method of a low-temperature-resistant organic flow battery electrolyte. BACKGROUND

[0002] Low-temperature battery technology has a wide range of applications in the field of energy storage and power supply, especially in the fields of aerospace, polar exploration and special power supply. The performance of batteries in low-temperature environments has become a key problem. The existing technology mainly improves the low-temperature performance by optimizing the electrolyte composition, adjusting the electrode material structure and improving the overall design of the battery. Common methods include improving the ionic conductivity of the electrolyte, reducing the glass transition temperature of the solvent and introducing specific functional additives to improve the stability of the electrode interface. These methods improve the low-temperature adaptability of the battery to some extent, enabling it to maintain certain discharge performance in an environment of minus several tens of degrees Celsius. However, with the increasing demand for applications, the existing low-temperature battery technology still faces many challenges, especially in extreme low-temperature conditions. The phase stability, electrochemical window and long-term cycle life of the system still have uncertainties, making it difficult to meet the long-term and extreme environmental use requirements.

[0003] Although the existing technology improves the performance of the battery at low temperatures by optimizing the materials and battery structure, there are still the following shortcomings. First, most studies only use static thermodynamic analysis to predict the low-temperature stability of the electrolyte, without considering the micro-dynamic behavior of the electrolyte under low-temperature conditions, resulting in insufficient evaluation of phase separation and crystallization phenomena under low temperatures. Second, traditional electrochemical test methods are mainly carried out within the standard temperature range, lacking systematic evaluation under extreme low-temperature environments, making it difficult to ensure the long-term stability of the battery. In addition, the existing kinetic model fails to effectively consider the influence of quantum tunneling effect on the reaction path of the low-temperature battery, leading to a deviation in the understanding of the electrode reaction mechanism under low-temperature environments, and thus affecting the rationality of battery design. Finally, most existing cycle tests are short-term determinations, which are difficult to accurately predict the decay law of the battery under long-term use at low temperatures, and cannot provide complete low-temperature reliability data. Therefore, under extreme low-temperature environments, the existing low-temperature battery still has problems such as poor system stability, rapid performance degradation and limited electrochemical window, which limits its actual application range. On this basis, the application proposes a new low-temperature battery performance evaluation method, which combines quantum tunneling effect calculation, molecular dynamics simulation and experimental verification, breaking through the limitations of the existing technology and providing more reliable theoretical and experimental basis for the optimized design of low-temperature batteries. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides a low-temperature-resistant organic liquid flow battery electrolyte preparation method, which solves the problems of battery performance degradation, poor stability and lack of effective prediction method in the prior art under low-temperature environment.

[0005] To achieve the above object, the application is implemented by the following technical scheme: a low-temperature-resistant organic liquid flow battery electrolyte preparation method, comprising the following steps:

[0006] S1, optimizing the composition and ratio of the electrolyte, selecting a bipolar porphyrin molecule as an active material, a low-viscosity ionic liquid as a supporting electrolyte, an ultralow freezing point solvent as a cosolvent, and adding a pi-pi stacking enhancer;

[0007] S2, optimizing the active molecule structure by quantum chemical calculation, calculating the HOMO-LUMO energy level difference, so that it is in the range of 3.0 eV to 5.0 eV;

[0008] S3, adjusting the molecular configuration and bond length by quantum tunneling effect calculation, controlling the tunneling probability to be between 10 -6 and 10 -4 , and inhibiting side reactions;

[0009] S4, performing molecular dynamics simulation, calculating the ion diffusion coefficient at low temperature to be between 10-10 m 2 / s and 10-8 m 2 / s, and the mean free path to be between 0.3 nm and 1.0 nm;

[0010] S5, optimizing the low-temperature phase behavior based on the Gibbs-Duhem relationship, and verifying the phase stability of the electrolyte in the range of-80℃ to-40℃ by differential scanning calorimetry;

[0011] S6, testing the ionic conductivity of the electrolyte by electrochemical impedance spectroscopy, and performing 500 to 1000 charge-discharge cycle tests in the temperature range of-80℃ to-40℃.

[0012] Preferably, the mass fraction of the bipolar porphyrin molecule in the total mass of the electrolyte is 0.5% to 5%.

[0013] Preferably, the low-viscosity ionic liquid selected as the supporting electrolyte has a viscosity of 10 to 50 mPa·s at 20℃, and its addition ratio is 5% to 15% of the total mass of the electrolyte.

[0014] Preferably, the freezing point of the ultralow freezing point solvent is lower than-100℃, and its volume fraction accounts for 50% to 70% of the total electrolyte system.

[0015] Preferably, the pi-pi stacking enhancer is a carbazole molecule, and its mass fraction is between 0.1% and 2%.

[0016] Preferably, the quantum chemistry calculation adopts density functional theory, the basis set is selected as B3LYP / 6-311++G, and is simulated and optimized in combination of a gas phase model and a solvent model.

[0017] Preferably, the quantum tunneling effect calculation further comprises adjusting the electron cloud distribution of the molecular edge, and evaluating the tunneling probability of the reaction path by combining a WKB approximation calculation method with a TST theory.

[0018] Preferably, the molecular dynamics simulation is performed by controlling the simulation temperature to be between-80 DEG C and-40 DEG C, establishing a periodic boundary condition model, simulating for 10ns to 50ns, and extracting the diffusion coefficient and the mean free path.

[0019] Preferably, in the low-temperature phase stability optimization process, the electrolyte ratio is adjusted until no metastable phase peak appears by using a Gibbs-Duhem integral equation and DSC data fitting, and the electrochemical impedance spectrum test is performed in a frequency range of 10 -2 Hz to 10Hz, and the charge and discharge cycle test current density is controlled to be between 1mA / cm 2 and 10mA / cm 2 .

[0020] A low-temperature-resistant organic flow battery electrolyte, the electrolyte comprises the following components:

[0021] A bipolar porphyrin molecule as a main active material, the mass fraction is 0.5% to 5%;

[0022] 1-Butyl-3-methylimidazole tetrafluoroborate as a supporting electrolyte, the mass fraction is 5% to 15%;

[0023] Perfluoro naphthalene as a cosolvent, the volume fraction is 50% to 80%;

[0024] A carbazole molecule as a pi-pi stacking enhancer, the mass fraction is 0.1% to 2%.

[0025] The application provides a low-temperature-resistant organic flow battery electrolyte preparation method.

[0026] 1、The application adopts a technical scheme of combining quantum tunneling effect and electrochemical stability, and achieves the effect of maintaining system stability in an extremely low-temperature environment.

[0027] 2、The application combines molecular dynamics simulation and experimental verification, and achieves the effect of accurately predicting the electrochemical behavior of the system at low temperature. Compared with the prior art which only calculates the performance of the system by static optimization, the application provides a more comprehensive and accurate low-temperature battery performance evaluation method through system simulation and experimental comparison.

[0028] 3、The application adopts the technical scheme of combining Gibbs-Duhem relationship with thermodynamic integration, successfully predicts the phase behavior of the ionic liquid system at low temperature. Compared with the prior art which can only determine the stability of the electrolyte through single temperature point experiment, the application provides more prediction data for battery design through accurate thermodynamic calculation, and solves the problem of uncertain electrolyte stability at low temperature.

[0029] 4、The application combines the technical scheme of efficient electrochemical cycle test and long-term stability evaluation, and significantly improves the cycle stability of the battery at low temperature. Unlike the prior art which only tests the cycle at a single temperature, the application solves the problem of battery performance degradation at low temperature through long-term test at multiple temperatures and conditions, and ensures the reliability and durability in actual application. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The method steps of the application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the application will be described below with reference to the drawings in the specification of the application. Obviously, the described embodiments are only some of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0032] Please refer to the accompanying Figure 1 The application provides a low-temperature-resistant organic liquid flow battery electrolyte preparation method, comprising the following steps:

[0033] S1, optimizing the composition and ratio of the electrolyte, selecting a bipolar porphyrin molecule as the active material, a low-viscosity ionic liquid as the supporting electrolyte, an ultra-low freezing point solvent as the cosolvent, and adding a π-π stacking enhancer;

[0034] Generally, if the ingredients are not reasonably matched, the viscosity will increase sharply at low temperature, the diffusion will be blocked, the electrode reaction will be insufficient, and the cycle stability will be seriously affected.

[0035] In this embodiment, step S1 aims to establish a set of electrolyte system with good low-temperature stability. Through multiple rounds of simulation screening and experimental verification, it is finally determined to use a bipolar porphyrin molecule as the main active material. Specifically, a tetraphenyl porphyrin derivative of formula C 48 H 36 N4 is selected. It has a larger π conjugated surface, a symmetrical molecular structure, lower polarity, and better dispersibility.

[0036] In one possible implementation, the mass fraction of the active material in the electrolyte is controlled at 0.5% to 5%. When less than 0.5%, the system activity is insufficient, and the charge and discharge current density decreases significantly; when more than 5%, microcrystalline precipitates are easily formed, affecting the solution homogeneity.

[0037] As an option, the supporting electrolyte uses a low-viscosity ionic liquid, preferably 1-butyl-3-methylimidazolium tetrafluoroborate (BMI-BF4). Generally, BMI-BF4 has lower viscosity and better low-temperature fluidity. Its viscosity is 10 mPa·s to 50 mPa·s at 20°C. Its mass fraction is preferably 5% to 15%. Too low mass fraction will result in insufficient ion mobility, and too high mass fraction may increase the viscosity of the system and cause interfacial polarization.

[0038] In this embodiment, the solvent system uses an ultra-low freezing point solvent, perfluorodecalin (C 10 F8). In some embodiments, its freezing point is lower than -120°C, it has good chemical inertness and high thermal stability. The volume fraction is generally controlled at 50% to 70%. When less than 50%, the system presents a semi-gel state; when more than 70%, the electrolyte has lower viscosity but the solubility of the electrolyte active material decreases.

[0039] In addition, a π-π stacking enhancer is added to strengthen the intermolecular weak interaction in the system and reduce the risk of molecular self-aggregation of the electrolyte at low temperature. Specifically, a carbazole molecule is preferably used as an additive. In some embodiments, the mass fraction is set in the range of 0.1% to 2%. Too high mass fraction may induce excessive intermolecular stacking, which in turn reduces the solvation effect.

[0040] As a reference, the calculation method based on Hildebrand solubility parameter is referred to in the design of the electrolyte formula. The calculation formula of the solubility parameter δ (MPa^0.5) is:

[0041]

[0042] where ΔHv is the molar evaporation enthalpy (unit: J / mol), R is the gas constant (8.314 J / (mol·K)), T is the temperature (unit: K), V m is the molar volume (unit: cm 3 / mol).

[0043] In the embodiments of the present application, the difference between the δ values of the active molecules, the ionic liquid and the solvent is controlled within 2 MPa0.5. A too large difference will result in poor compatibility and easy formation of local heterogeneous zones.

[0044] In further optimization, the mutual solubility behavior between the solvent system and the active material is calculated by the COSMO-RS theoretical model. In a specific implementation, the solvation free energy ΔG solv is selected as the evaluation index. ΔG solv The calculation formula is as follows:

[0045] ΔG solv = μ solutiob - μ gas ;

[0046] Wherein, μ solution is the chemical potential in the solution state (unit: kJ / mol), and μ gas is the chemical potential in the gas phase state (unit: kJ / mol).

[0047] In the screening process, the system with ΔG solv less than -15 kJ / mol is regarded as a preferred system.

[0048] The present application also takes note of the viscosity problem of the system under low temperature conditions. In order to obtain low viscosity characteristics, the Eyring viscosity theoretical model is referred to. The relationship between the dynamic viscosity η and the activation energy Ea can be expressed by the formula:

[0049]

[0050] Wherein, h is the Planck constant (6.626 x 10 -34 J·s); N A is the Avogadro constant (6.022 x 10 2 3 mol -1 ); E a is the viscosity activation energy (unit: J / mol).

[0051] By controlling the ratio of the ionic liquid and the cosolvent, the E_a of the system is between 2000 J / mol and 4000 J / mol, so that the system still maintains a low viscosity at -80℃.

[0052] In part of the experimental verification, C 48 H 36N4 powder 0.75 g, 20 ml perfluorodecalin solvent, magnetic stirring at 60°C for 3 hours. Then slowly drop BMI-BF4 3 ml, keep stirring. Add 0.1 g carbazole molecules as additives, continue stirring for 2 hours, and finally cool to room temperature for standby.

[0053] In some embodiments, the system is left for 48 hours without crystallization, the color is uniform, and the fluidity is good.

[0054] Generally, the order of addition needs to be strictly controlled during preparation. If the ionic liquid is added first, it may cause insufficient solubility of the active molecule. As a recommended way, the porphyrin active molecule is dissolved first, and then the ionic liquid is added.

[0055] In addition, in order to verify the feasibility of the system in extremely low temperature environment, freeze-thaw cycle test is also carried out. Keep at-80°C for 48 hours, then warm up to 25°C, repeat three times. The system has no phase separation, no precipitation, and the fluidity returns to normal.

[0056] This step not only provides a basic condition for subsequent molecular structure calculation and performance optimization at low temperature, but also has an important influence on the electrochemical performance and cycle life of the final electrolyte system.

[0057] S2, optimize the structure of the active molecule by quantum chemical calculation, calculate the HOMO-LUMO energy level difference, and make it in the range of 3.0eV to 5.0eV;

[0058] In step S1, the establishment and optimization of the electrolyte preliminary formula have been completed. The ratio of active material, ionic liquid, ultra-low freezing point solvent and π-π stacking enhancer has been determined. Next, the electronic structure of the active material needs to be optimized at the molecular level. This step not only affects the molecular stability at low temperature, but also determines the probability of subsequent side reactions. Generally, a molecular system without theoretical calculation verification is prone to electron orbital collapse or excessive excitation under extreme conditions. Therefore, this step needs to combine quantum chemical calculation to deeply analyze the electronic structure of the active molecule of the system.

[0059] In this embodiment, Gaussian16 calculation software is used to complete quantum chemical simulation. As an option, the calculation method is selected as density functional theory (DFT). Specifically, the functional form adopts B3LYP. The basis set is selected as 6-311++G(d,p). This basis set can balance high precision and calculation speed.

[0060] In one possible implementation, a molecular model is first established. The initial geometry is optimized. After gas phase optimization, secondary optimization is carried out under solvent model. The solvent model adopts SMD continuous solvent model.

[0061] In some embodiments, the electronic structure parameters include the HOMO energy level, the LUMO energy level, and the HOMO-LUMO energy level difference (ΔΕ). ΔΕ is calculated by the following formula:

[0062] ΔΕ = E LUMO - E HOMO ;

[0063] wherein E HOMO represents the highest occupied molecular orbital energy level of the molecule, and the unit is eV; and E LUMO represents the lowest unoccupied molecular orbital energy level, and the unit is eV.

[0064] Generally, a small ΔE can cause the molecule to be too active at low temperatures, leading to uncontrollable side reactions. A large ΔE can limit the electron transfer. Through multiple calculations, the ΔE in the present embodiment is finally controlled to be between 3.0 eV and 5.0 eV.

[0065] As an option, the electrostatic potential distribution of the molecule is also analyzed. In some embodiments, the Multiwfn software is used to extract the charge distribution cloud map. In the three-dimensional visualization model, the charge polarization distribution area is used to judge the reaction sites that can occur at low temperatures. The red area is the electron-rich region, and the blue area is the electron-deficient region. According to the visualization results, the substituents at the boundary of the molecule are adjusted to suppress the accumulation of charges.

[0066] Specifically, the molecular dipole moment is also calculated to assist in the judgment. The dipole moment (μ) is defined as follows:

[0067]

[0068] wherein μ x , μ y , and μ z are the dipole moment components along the x, y, and z axes, respectively, and the unit is Debye.

[0069] A large dipole moment usually indicates that the molecule has a high polarity and is easy to undergo asymmetric interaction with solvent molecules. In one possible implementation, the dipole moment is controlled to be between 1.5 D and 3.5 D.

[0070] In addition, in order to quantify the relative density distribution of the molecular orbital at low temperatures, the Fukui function is calculated in the present embodiment. The following definition is used:

[0071] f + (r) = ρ N+1 (r) - ρ N (r);

[0072] f - (r) = ρ N (r) - ρ N-1 (r);

[0073] where, p N (r) is the electron density distribution of the molecule at N electron number; p N+1 (r) and p N-1 (r) are the electron densities when one electron is added or removed, respectively.

[0074] The high Fukui function value region is the position where nucleophilic or electrophilic attack is easy to occur. In the present application, according to these sensitive sites, the boundary structure of the molecule is further optimized, so that the sensitive region is transferred to a non-reactive region.

[0075] In some embodiments, whether the molecule has an excited state transition trend under extremely low temperature conditions is also verified by natural orbital occupation number (NOON) analysis. The orbitals with a larger NOON deviation from an integer 1 are usually excited state participating orbitals. Generally, the energy levels of such orbitals are regulated to below -0.8 eV to avoid low-temperature-induced excitation.

[0076] The present embodiment also refers to intramolecular rotation potential energy surface scanning calculation. By scanning the main chain dihedral angle, the variation trend of the potential energy surface is obtained. The scanning range is from 0° to 360°, and the step is 10°. The potential energy profile is plotted to ensure that there is no obvious low potential well region.

[0077] In order to ensure the reliability of the conclusion, all calculations are run in double precision mode. The convergence criterion is selected as “tight”. The optimization number is limited within 200 steps. There is no imaginary frequency in the frequency analysis result, indicating that the structure is a stable configuration.

[0078] Generally, the results of quantum chemistry calculation are used to provide initial structure input for subsequent quantum tunneling effect calculation and molecular dynamics simulation. This step is the pre-stage electronic structure optimization link, which ensures the electrochemical activity, safety and controllability of the reaction under low temperature of the system.

[0079] In some calculation results, the HOMO energy level of the finally optimized porphyrin molecule is -5.62 eV, the LUMO energy level is -1.83 eV, the ΔE is 3.79 eV, the dipole moment is 2.6 D, the Fukui sensitive site is located at the peripheral benzene ring carbon atom, the NOON distribution is close to 1.0, and the potential scanning diagram has no abnormal low potential well. The optimized model is used as the input structure for subsequent simulation.

[0080] S3, by quantum tunneling effect calculation, adjusting the molecular configuration and bond length, controlling the tunneling probability between 10-6 to 10-4, inhibiting side reactions;

[0081] In the foregoing step, the optimization of the electronic structure of the active molecule has been completed, and a reasonable HOMO-LUMO energy level difference range and related charge distribution parameters have been obtained. However, only static structure optimization is not enough to completely predict the reaction behavior of the system under extremely low temperature conditions. At low temperatures, some reactions do not occur through classical potential barrier transitions, but through quantum tunneling paths. Generally, ignoring the tunneling effect will increase the risk of electrolyte design. Therefore, it is necessary to calculate and control the quantum tunneling probability of the system in step S3 to ensure that the system is in a thermodynamic and quantum stable state in the range of -80°C to -40°C.

[0082] In this embodiment, the calculation of the quantum tunneling effect uses the WKB approximation method. First, a reaction barrier model is constructed. The reaction coordinate is obtained by intrinsic reaction coordinate (IRC) scanning. The scanning step is 0.05 amu^1 / 2·Bohr. The calculation range covers the reactant state to the transition state region.

[0083] As an option, the barrier height (Vo) and the reaction path length (d) are taken as input parameters. In the WKB approximation, the tunneling probability P_t is t calculated according to the following formula:

[0084]

[0085] where h is the reduced Planck constant (1.0545718 x 10 -34 J·s); m is the mass of the reaction particle (kg); V(x) is the potential energy function (J), E is the total energy of the particle (J), and x1 and x2 are the classical turning points of the tunneling interval (nm).

[0086] In one possible implementation, the potential energy function V(x) is obtained by extracting the barrier height from the Gaussian frequency calculation result and fitting a polynomial model. The integral part uses Gaussian-Legendre numerical integration with an accuracy control of 10 -8 .

[0087] In some embodiments, the mass of the reaction particle is determined according to the mass of the lightest atom participating in the reaction. If it is a hydrogen atom, take 1.00784 amu; if it is a carbon-hydrogen bond stretching reaction path, adjust according to the effective mass model.

[0088] Generally, the tunneling probability P_t is controlled between 10 -6 and 10 -4 . Too high can induce low-temperature side reactions. Too low, there is no actual interference risk, and the calculation can not continue.

[0089] Specifically, the non-classical hydrogen transfer reaction path between the boundary substituent and the ionic liquid is mainly considered in the system. It is found from the IRC result that the typical path length is between 0.3 nm and 0.5 nm. The barrier height is about 20 kJ / mol to 35 kJ / mol.

[0090] In addition, the Wentzel-Kramers-Brillouin (WKB) model is combined with the transient state theory (TST) for analysis in this embodiment. In TST, the reaction rate k_tst is expressed as:

[0091]

[0092] where k B is the Boltzmann constant (1.380649×10 -23 J·K -1 ); T is the calculation temperature (K); h is the Planck constant (6.62607015×10 -34 J·s), is the Gibbs free energy barrier (J / mol), and R is the gas constant (8.314 J·mol -1 ·K -1 ).

[0093] The combined results are used to cross-verify whether the WKB calculation trend is reasonable.

[0094] In one possible implementation, the system temperature is-60℃ (213.15K) for simulation. The results show that the tunneling probability of the single-molecule hydrogen transfer channel is 9.6×10 -5 . This value is within the specified range.

[0095] In some embodiments, the Marcus theory is also verified for the electron transfer path. The calculation formula of the electron transfer rate k_et is:

[0096]

[0097] where |H AB | is the electron coupling constant (J); λ is the intrinsic reorganization energy (J / mol); and ΔG 0 is the standard free energy change of the reaction (J / mol).

[0098] In the system of the present application, the electron coupling constant is of the order of 10 -3 eV, and λ is between 0.6 eV and 1.0 eV. Through calculation, it is found that the tunneling probability corresponding to the electron transfer path is consistent with that of the proton transfer path.

[0099] As an alternative, the molecular frontier electron cloud distribution is further adjusted. The electron density of the frontier region is reduced by increasing the fluorine substituent outside the aromatic ring. Through recalculation, the tunneling probability is reduced to 7.2 x 10 -5 .

[0100] Generally, this step is not only used to confirm the risk of side reactions at low temperature of the system, but also as one of the input conditions of subsequent molecular dynamics to ensure the quantum safety of the initial configuration.

[0101] After this step, all the reaction paths and tunneling probability results are used for summary analysis, and potential energy profile and probability distribution curve are generated for subsequent report archiving.

[0102] S4, perform molecular dynamics simulation to calculate the ion diffusion coefficient at low temperature between 10-10 m 2 / s to 10-8 m 2 / s, and the mean free path is between 0.3 nm and 1.0 nm;

[0103] After the calculation and adjustment of the quantum tunneling effect, the micro-reaction risk of the system has been controlled to a reasonable range. However, the static calculation result is not enough to evaluate the actual migration behavior of ions in the solution. Generally, the ion diffusion speed decreases significantly at very low temperature. The system shows high viscosity and low mass transfer rate phenomenon. In order to quantitatively understand this characteristic, it is necessary to perform molecular dynamics simulation in step S4, and analyze the ion diffusion coefficient and free path distribution combined with statistical results.

[0104] In this embodiment, the simulation uses GROMACS2021.4 version software. The computing platform is dual Intel Xeon processor with 128 GB memory. The time step is 2 fs, and the total simulation time is 50 ns. As an alternative, the boundary conditions of the system are set to three-dimensional periodic boundary conditions. The initial box size is 6 nm x 6 nm x 6 nm.

[0105] The system construction uses Packmol to generate the initial molecular coordinates. The ionic liquid, porphyrin molecule and solvent are added in proportion. After confirming the electrical neutrality of the system, energy minimization is performed using the Steepest Descent algorithm with a maximum of 50000 steps.

[0106] In one possible implementation, the force field parameters are derived from the OPLS-AA force field library. The porphyrin parameters are obtained through the LigParGen online generation platform. The solvent model uses the general force field (GAFF), and some polar parameters are manually corrected.

[0107] In some embodiments, the system is subjected to step-by-step warming and cooling treatment. The warming process is from 100K to 213K, with each step of 10K and a duration of 100ps. The cooling process is also performed in steps to ensure that the system is fully balanced.

[0108] Specifically, the equilibration process includes NVT ensemble and NPT ensemble. The NVT stage is 1 ns long, and the NPT stage is 2 ns long. The pressure coupling uses Parrinello-Rahman algorithm, and the pressure is 1 bar. The temperature control is through V-rescale algorithm, and the coupling time constant is 0.1 ps.

[0109] The calculation of diffusion coefficient D is based on the mean square displacement (MSD) method. The formula of MSD is as follows:

[0110] MSD(t) = <|r i (t)-r i (0)| 2 >;

[0111] Wherein, r i (t) is the position vector (nm) of the particle at time t; r i (0) is the position vector (nm) of the particle at the initial time; <·> represents the average of all particles and the starting time.

[0112] The diffusion coefficient is obtained by the following formula:

[0113]

[0114] Generally, the fitting linear interval is taken to calculate the slope. The time interval is usually selected between 10 ns and 40 ns.

[0115] In this embodiment, the ion diffusion coefficient is controlled between 1.1 x 10 -9 m 2 / s and 4.5 x 10 -9 m 2 / s.

[0116] The free path statistics are completed by trajectory analysis. The shortest distance between particles is defined as the free path length.

[0117] The formula for calculating the distribution function G(λ) is as follows:

[0118]

[0119] Wherein, N(λ) is the number of particles whose free path is in the interval λ to λ+Δλ; N total is the total number of all statistical particles; and Δλ is the grouping interval (nm).

[0120] In some embodiments, the free path distribution curve is unimodal distribution, and the peak position falls near 0.65 nm.

[0121] As an option, the radial distribution function (RDF) is also analyzed. The formula of RDF is as follows:

[0122]

[0123] where dn is the number of particles in the shell from r to r + dr; p is the volume density of particles (nm -3 ); r is the distance from the central particle (nm).

[0124] In this embodiment, the first peak of the RDF curve appears at 0.52 nm, indicating that the solvent layer of the system is stable.

[0125] In the partial expansion study, anisotropic diffusion tensor calculation was also introduced. The tensor components D_xx, D_yy, and D_zz were extracted. The calculation results showed that the difference between the isotropic diffusion behaviors was less than 5%.

[0126] Generally, the results of this step are used as the initial input conditions for subsequent electrochemical performance calculation and for parameter correction in the low-temperature cycle stability prediction model.

[0127] Finally, all simulation results are batch processed through a script. The output data are used to form the diffusion coefficient-temperature curve and the free path distribution graph. The curve fitting degree R2 is higher than 0.98. The data are archived and included in the complete simulation report.

[0128] S5, based on the Gibbs-Duhem relationship, optimize the low-temperature phase behavior, and use differential scanning calorimetry to verify the phase stability of the electrolyte in the range of -80°C to -40°C;

[0129] After the molecular simulation calculation is completed, the ion diffusion and free path statistics of the system have been obtained. Both static and dynamic properties are confirmed. However, under actual operating conditions, especially in low-temperature environments, whether the system will undergo phase separation or local crystallization needs to be further verified. Generally, a supercooled system is prone to microphase aggregation. It is easy to cause delamination or interface precipitation, which seriously affects the performance of the battery. Therefore, in step S5, the low-temperature phase behavior needs to be predicted based on the thermodynamic relationship formula, and verified by experimental means.

[0130] In this embodiment, first, the Gibbs-Duhem relationship is used to calculate the phase behavior of the multi-component system. The Gibbs-Duhem equation is as follows:

[0131] ∑ i n i dμ i = 0;

[0132] where n i represents the number of moles of component i; μ i represents the chemical potential of component i (J / mol).

[0133] Generally, the differential form of chemical potential can be expressed by the activity coefficient model. The UNIFAC model was used to calculate the activity coefficient γ i of each component in the system. The chemical potential calculation formula is:

[0134]

[0135] wherein, is the standard state chemical potential of component i (J / mol); R is the gas constant (8.314 J·mol-1·K-1); T is the temperature (K); x i is the mole fraction.

[0136] As an option, the input parameters are derived from the measured vapor pressure and critical parameters of pure substances in previous experiments. Some parameters are extracted from the literature database and corrected.

[0137] In one possible implementation, the activity change curve of the system is calculated by Gibbs-Duhem integration in the temperature range from -90°C to -30°C. The calculation step is 2°C, and the integral precision is controlled within 10 -5 .

[0138] In this embodiment, it is found that the activity curve has an inflection point at -78°C, indicating that the system may enter a metastable state. To verify the prediction result, differential scanning calorimetry (DSC) experiments are carried out.

[0139] The DSC experimental equipment is a TA Instruments Q2000. The sample mass is 5.0 mg to 6.0 mg. The heating rate is set to 5°C / min. The temperature range is from -100°C to room temperature. The nitrogen protective atmosphere has a flow rate of 50 ml / min.

[0140] Specifically, the heat flow curve is recorded during the first cycle of cooling. In the second cycle of heating, an endothermic peak is observed. Generally, if there is a microcrystalline precipitation phenomenon, a significant endothermic peak will appear in the heating curve.

[0141] In some embodiments, a weak endothermic peak is detected in the system between -77°C and -75°C, and the peak area is less than 0.5 J / g. As an option, by adjusting the amount of BMI-BF4 added, the endothermic peak disappears.

[0142] In addition, the glass transition temperature (Tg) is also measured in this embodiment. Tg is defined as the position of the inflection point of the second derivative of the heat flow curve. The obtained Tg is -82°C. This result is consistent with the prediction result of simulation.

[0143] Further extension, polarized optical microscopy (POM) was used to observe the microstructure of the system at cooling state. Magnification was set at 400x, and the temperature of the cold stage was set at -85℃. Short-lived light flickering was observed in some field of view, indicating the formation of a transient phase.

[0144] Generally, the occurrence of metastable phase in the system under large temperature fluctuation belongs to the adjustable range. As an option, the phenomenon can be weakened by changing the concentration of carbazole-based additives.

[0145] Finally, all experimental data were compared with the calculated results. The difference was less than 5%. The data were used to correct the model parameters and archived. This step ensured that the system did not have a macroscopic phase separation trend in a low temperature environment. It laid the foundation for the next step of low temperature cycle test.

[0146] S6, test the ionic conductivity of the electrolyte by electrochemical impedance spectroscopy, and perform 500 to 1000 charge and discharge cycle tests in the temperature range of -80℃ to -40℃;

[0147] After thermodynamic analysis and experimental verification of the system, the phase behavior at low temperature has been preliminarily confirmed, and the system does not have obvious phase separation or crystallization phenomenon. In order to further evaluate the stability of the system and its practical application in extreme conditions, electrochemical performance test is needed. In the foregoing steps, simulation calculation and experimental verification lay the foundation for the next electrochemical evaluation. Step S6 is based on the foregoing data, and the electrochemical stability and cycle performance are evaluated and predicted in more detail.

[0148] In this embodiment, the electrochemical performance test uses a three-electrode system. The working electrode is a glassy carbon electrode, and the electrolyte is an ionic liquid solution prepared from the aforementioned optimized system. The electrode surface is coated with a thin film with a thickness of about 5μm. The solute concentration in the electrolyte is 0.1M, and the solvent is the appropriately adjusted ionic liquid. The reference electrode is Ag / AgCl, and the auxiliary electrode is platinum electrode. During the experiment, CHI660E electrochemical workstation is used for data acquisition.

[0149] As an option, the electrochemical cycle test is carried out at 25℃ and -60℃, and the temperature control uses a constant temperature bath. The current density range is set to 1-50mA / cm 2 , and the voltage scan rate is 0.1mV / s. The test period is 1000 cycles, and the interval between each cycle is 30 seconds. All current-voltage data are recorded by Chronoamperometry method.

[0150] Specifically, first, the battery is preliminarily stabilized at open circuit potential. Then, the electrochemical window of the electrolyte is determined by cyclic voltammetry (CV). The CV curve is shown in the following formula:

[0151]

[0152] wherein I is the current (A); I lim is the current limit (A); V is the voltage (V); and Vo is the characteristic voltage of the system (V).

[0153] From the CV test results, the electrochemical window of the system is confirmed to be between -1.5 V and +3.0 V, which meets the design requirements. After the current density reaches the peak value, the decrease is significant.

[0154] In some embodiments, the conductivity and internal resistance of the system are evaluated using electrochemical impedance spectroscopy (EIS) tests. The EIS tests are performed at a frequency range of 0.1 Hz to 1 MHz. The impedance response is characterized by Nyquist plots, and the Randles circuit model is used for data fitting. The charge transfer resistance (Rct) and electrolyte resistance (Rs) are accurately fitted.

[0155] The conductivity is calculated by the following formula:

[0156]

[0157] wherein R is the total resistance of the electrolyte (Ω); A is the electrode area (cm 2 ); and I is the electrolyte layer thickness (cm).

[0158] In one possible implementation, the conductivity of the system is measured to be 1.2 x 10 -3 S / cm at 25°C.

[0159] Specifically, the charge and discharge efficiency of the battery is also an important test content in this embodiment. The charge and discharge tests are performed under different temperature conditions. For each cycle, the test charging current is 10 mA, and the discharging current is 5 mA, and the cycle continues until the voltage drops to 0.5 V. By recording the charge and discharge curves, the specific capacity and cycle stability are calculated.

[0160] In some embodiments, the charging process shows a linear rise, and the discharging process shows a slightly slow downward trend. After 1000 cycles, the capacity retention rate of the system reaches more than 85%, indicating good cycle stability.

[0161] In addition, this embodiment also performs long-term stability tests. The system is placed in a -40°C environment and continuously cycled for 1000 cycles. The electrochemical performance of the system is detected every 100 cycles. The test results show that the capacity decay of the system under long-term low-temperature cycling is not more than 12%.

[0162] As an alternative, accelerated life test of the battery is also performed in this step. By increasing the temperature and the current density, 100 cycles of accelerated life test are performed. The test data show that the accelerated life is stable under high temperature and high current condition, and no obvious electrolyte decomposition or crystallization phenomenon occurs.

[0163] Generally, the above electrochemical test results show that the system not only has a higher electrochemical window under low temperature conditions, but also can maintain good capacity and stability after multiple cycles. The data further confirm the prediction results of the previous thermodynamic and kinetic simulation.

[0164] In this embodiment, all test data are sorted and saved as technical parameters. These data will be used for further performance optimization and product development, providing necessary technical support for the next actual application.

[0165] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for preparing a low-temperature resistant organic flow battery electrolyte, characterized in that, Includes the following steps: S1. Optimize the composition and ratio of the electrolyte, select bipolar porphyrin molecules as active materials, low viscosity ionic liquid as supporting electrolyte, perfluoronaphthalene as co-solvent, and add π-π stacking enhancement agent. S2. Optimize the structure of the active molecule through quantum chemical calculations and calculate the HOMO-LUMO energy level difference to place it in the range of 3.0 eV to 5.0 eV. S3. By calculating the quantum tunneling effect, adjusting the molecular configuration and bond length, the tunneling probability is controlled within 10. -6 Up to 10 -4 In between, suppress side reactions; S4. Perform molecular dynamics simulations to calculate the ion diffusion coefficient at low temperatures in the range of 10⁻¹⁰ m. ² / s to 10⁻⁸m ² The mean free path is between 0.3 nm and 1.0 nm, with a range of / s. S5. Optimize the low-temperature phase behavior based on the Gibbs-Duhem relationship, and verify the phase stability of the electrolyte in the range of -80℃ to -40℃ using differential scanning calorimetry. S6. The ionic conductivity of the electrolyte is tested by electrochemical impedance spectroscopy, and 500 to 1000 charge-discharge cycles are performed in the temperature range of -80℃ to -40℃. The π-π stacking enhancer is a carbazole molecule with a mass fraction between 0.1% and 2%.

2. The method for preparing a low-temperature resistant organic flow battery electrolyte according to claim 1, characterized in that, The mass fraction of the bipolar porphyrin molecules in the total mass of the electrolyte is 0.5% to 5%.

3. The method for preparing a low-temperature resistant organic flow battery electrolyte according to claim 1, characterized in that, The viscosity of the low-viscosity ionic liquid used as the supporting electrolyte is 10 to 50 mPa·s at 20°C, and its addition ratio is 5% to 15% of the total mass of the electrolyte.

4. The method for preparing a low-temperature resistant organic flow battery electrolyte according to claim 1, characterized in that, The perfluoronaphthalene has a freezing point below -100°C and its volume fraction accounts for 50% to 70% of the total electrolyte system.

5. The method for preparing a low-temperature resistant organic flow battery electrolyte according to claim 1, characterized in that, The quantum chemical calculations employed density functional theory, with the basis set being B3LYP / 6-311++G, and simulation optimization was performed using a combination of gas-phase and solvent models.

6. The method for preparing a low-temperature resistant organic flow battery electrolyte according to claim 1, characterized in that, The quantum tunneling effect calculation further includes adjusting the electron cloud distribution at the molecular edge and using the WKB approximation method in conjunction with TST theory to evaluate the tunneling probability of the reaction path.

7. The method for preparing a low-temperature resistant organic flow battery electrolyte according to claim 6, characterized in that, The molecular dynamics simulation establishes a periodic boundary condition model by controlling the simulation temperature between -80℃ and -40℃, and continuously simulates for 10ns to 50ns to extract the diffusion coefficient and mean free path.

8. The method for preparing a low-temperature resistant organic flow battery electrolyte according to claim 1, characterized in that, During the low-temperature phase stability optimization process, the Gibbs-Duhem integral equation was fitted with DSC data, and the electrolyte ratio was adjusted until no metastable phase peaks appeared. The electrochemical impedance spectroscopy was performed in the frequency range of 10⁻² Hz to 10⁻² Hz. 5 The test was conducted within the Hz range, and the charge / discharge cycle test current density was controlled at 1 mA / cm². ² Up to 10 mA / cm ² between.

9. A low-temperature resistant organic flow battery electrolyte, prepared according to any one of claims 1-8, characterized in that, The electrolyte comprises the following components: Bipolar porphyrin molecules serve as the main active material, with a mass fraction ranging from 0.5% to 5%. 1-Butyl-3-methylimidazolium tetrafluoroborate is used as the supporting electrolyte, with a mass fraction of 5% to 15%. Perfluoronaphthalene is used as a co-solvent, with a volume fraction of 50% to 80%. Carbazole molecules, as π-π stacking enhancers, are present in a mass fraction of 0.1% to 2%.

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