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

Abstract

The invention relates to the technical field of low-temperature batteries, and discloses a low-temperature-resistant organic flow battery electrolyte preparation method, which comprises: S1, optimizing the composition and the ratio of an electrolyte, selecting a bipolar porphyrin molecule as an active material, a low-viscosity ionic liquid as a supporting electrolyte, and an ultra-low freezing point solvent as a co-solvent, a pi-pi stacking reinforcing agent is added; s2, optimizing an active molecular structure through quantum chemistry calculation, and calculating HOMO-LUMO energy level difference, so that the HOMO-LUMO energy level difference is in an interval of 3.0 eV to 5.0 eV; and S3, calculating and adjusting molecular configuration and bond length through a quantum tunneling effect. By introducing the technical scheme of combining the quantum tunneling effect and the electrochemical stability, the effect of maintaining the stability of the battery in a low-temperature environment is realized, and the accurate low-temperature battery performance evaluation method is provided by combining molecular dynamics simulation and experimental verification. The defect of battery behavior prediction in a low-temperature environment in the traditional technology is overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of low-temperature batteries, and specifically to a preparation method for an electrolyte of a low-temperature resistant organic flow battery. Background Art

[0002] Low-temperature battery technology has wide applications in the fields of energy storage and power supply, especially in fields such as aerospace, polar exploration, and special power sources. The battery performance under low-temperature conditions becomes a key issue. Existing technologies mainly improve the low-temperature performance by optimizing the electrolyte composition, adjusting the electrode material structure, and improving the overall battery design. Common methods include increasing the ionic conductivity of the electrolyte, lowering the glass transition temperature of the solvent, and introducing specific functional additives to improve the electrode interface stability. These methods have improved the low-temperature adaptability of the battery to a certain extent, enabling it to maintain a certain discharge performance in an environment of dozens of degrees Celsius below zero. However, with the continuous increase of application requirements, the existing low-temperature battery technology still faces many challenges. Especially under extremely low-temperature conditions, the key performance such as the phase stability, electrochemical window, and long-term cycle life of the system still has uncertainties and is difficult to meet the usage requirements under long-term and extreme environments.

[0003] Although the existing technology has improved the battery performance at low temperatures by optimizing the materials and battery structure, there are still the following deficiencies. First, most studies only use static thermodynamic analysis to predict the low-temperature stability of the electrolyte, without considering the microscopic kinetic behavior of the electrolyte under low-temperature conditions, resulting in insufficient evaluation of the phase separation and crystallization phenomena that occur at low temperatures. Second, traditional electrochemical testing methods are mainly carried out within the standard temperature range and lack systematic evaluation in extremely low-temperature environments, making it difficult to ensure the long-term stability of the battery. In addition, the existing kinetic models fail to effectively consider the influence of quantum tunneling effects on the reaction path of low-temperature batteries, resulting in deviations in the understanding of the electrode reaction mechanism under low-temperature conditions, and further affecting the rationality of battery design. Finally, most of the existing cycle tests are short-term measurements, making it difficult to accurately predict the attenuation law of the battery during long-term use at low temperatures and unable to provide complete low-temperature reliability data. Therefore, under extremely low-temperature environments, the existing low-temperature batteries still have problems such as poor system stability, rapid performance degradation, and limited electrochemical window, which limit their actual application scope. On this basis, the present invention proposes a new method for evaluating the performance of low-temperature batteries, combining quantum tunneling effect calculation, molecular dynamics simulation, and experimental verification, breaking through the limitations of the existing technology, and providing a more reliable theoretical and experimental basis for the optimized design of low-temperature batteries. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for preparing an electrolyte for a low-temperature organic flow battery, which solves the problems of battery performance degradation, poor stability, and lack of effective prediction methods in the prior art under low-temperature environments.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for preparing an electrolyte for a low-temperature organic flow battery, comprising the following steps:

[0006] S1. Optimize the composition and ratio of the electrolyte, select bipolar porphyrin-like molecules as active materials, low-viscosity ionic liquids as supporting electrolytes, ultra-low freezing point solvents as co-solvents, and add π-π stacking enhancers;

[0007] S2. Through quantum chemical calculations, optimize the structure of the active molecules, calculate the HOMO-LUMO energy level difference, and make it fall within the range of 3.0 eV to 5.0 eV;

[0008] S3. Through quantum tunneling effect calculations, adjust the molecular configuration and bond length, and control the tunneling probability between 10 -6 and 10 -4 to suppress side reactions;

[0009] S4. Conduct molecular dynamics simulations, calculate the ion diffusion coefficient at low temperatures between 10-10 m 2 / s and 10-8 m 2 / s, and the mean free path between 0.3 nm and 1.0 nm;

[0010] S5. Optimize the low-temperature phase behavior based on the Gibbs-Duhem relationship, and use differential scanning calorimetry to verify the phase stability of the electrolyte in the range of -80°C to -40°C;

[0011] S6. Test the ionic conductivity of the electrolyte through electrochemical impedance spectroscopy, and conduct 500 to 1000 charge-discharge cycle tests in the temperature range of -80°C to -40°C.

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

[0013] Preferably, the viscosity of the low-viscosity ionic liquid selected 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.

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

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

[0016] Preferably, the quantum chemical calculation adopts the density functional theory, the basis set is selected as B3LYP / 6-311++G, and the simulation optimization is carried out by combining the gas phase model and the solvent model.

[0017] Preferably, the calculation of the quantum tunneling effect further includes adjusting the electron cloud distribution at the molecular edge, and jointly evaluating the tunneling probability of the reaction path by using the WKB approximation calculation method and the TST theory.

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

[0019] Preferably, during the optimization of the low-temperature phase stability, the Gibbs-Duhem integral equation is used to fit with the DSC data, and the electrolyte ratio is adjusted until no metastable phase peak appears. The electrochemical impedance spectroscopy test is carried out in the frequency range of 10 -2 Hz to 10 Hz, and the charge-discharge cycle test current density is controlled between 1 mA / cm 2 and 10 mA / cm 2 .

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

[0021] Bipolar porphyrin molecules as the main active material, with a mass fraction of 0.5% to 5%;

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

[0023] Perfluoronaphthalene as the co-solvent, with a volume fraction of 50% to 80%;

[0024] Carbazole molecules as the π-π stacking enhancer, with a mass fraction of 0.1% to 2%.

[0025] The present invention provides a preparation method for a low-temperature resistant organic liquid flow battery electrolyte. It has the following beneficial effects:

[0026] 1. The present invention adopts a technical solution that combines the quantum tunneling effect and electrochemical stability, achieving the effect of maintaining the system stability in an extremely low-temperature environment. Compared with the prior art that only relies on the prediction of traditional thermodynamic models, the present invention avoids the problem of system performance degradation caused by ignoring the low-temperature effect by introducing the precise calculation of the quantum tunneling probability, and greatly improves the reliability under low-temperature conditions.

[0027] 2. The present invention combines the technical solutions of molecular dynamics simulation and experimental verification, achieving the effect of accurately predicting the electrochemical behavior of the system at low temperatures. Compared with the existing method of only judging the system performance through static optimization calculation, the present invention provides a more comprehensive and accurate method for evaluating the performance of low-temperature batteries through systematic simulation and experimental comparison.

[0028] 3. The present invention adopts the technical solution of combining the Gibbs–Duhem relation with thermodynamic integration, successfully predicting the phase behavior of ionic liquid systems in low-temperature environments. Compared with the existing solution that can only determine the electrolyte stability through experiments at a single temperature point, the present invention provides more prediction data for battery design through precise thermodynamic calculations, solving the problem of uncertain electrolyte stability under low-temperature conditions.

[0029] 4. The present invention combines the technical solutions of efficient electrochemical cycling tests and long-term stability evaluations, significantly improving the cycling stability of batteries under low-temperature conditions. Different from the existing solution of cycling tests at a single temperature, the present invention solves the problem of battery performance degradation under low temperatures through long-term tests at multiple temperatures and conditions, ensuring reliability and durability in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of the method steps of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Please refer to the attached Figure 1 , an embodiment of the present invention provides a method for preparing an electrolyte for a low-temperature resistant organic flow battery, including the following steps:

[0033] S1. Optimize the composition and ratio of the electrolyte, select bipolar porphyrin molecules as active materials, low-viscosity ionic liquids as supporting electrolytes, ultra-low freezing point solvents as co-solvents, and add π-π stacking enhancers;

[0034] Generally, if the component combination is unreasonable, it will lead to a sharp increase in viscosity at low temperatures, hindered diffusion, insufficient electrode reactions, and seriously affect the cycling stability.

[0035] In this embodiment, step S1 aims to establish an electrolyte system with good low temperature stability. After multiple rounds of simulation screening and experimental verification, it is finally determined that bipolar porphyrin molecules are used as the main active material. Specifically, the molecular formula C 48 H 36 N4 tetraphenylporphyrin derivatives. It has a large π conjugated surface, symmetrical molecular structure, low polarity and good dispersibility.

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

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

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

[0039] In addition, a π-π stacking enhancer is added to strengthen the weak interaction between molecules in the system and reduce the risk of molecular self-aggregation of the electrolyte at low temperatures. Specifically, carbazole molecules are preferred as additives. In some embodiments, the mass fraction is set in the range of 0.1% to 2%. Too high a mass fraction is likely to induce excessive stacking between molecules, which in turn reduces the solvation effect.

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

[0041]

[0042] Where ΔHv is the molar enthalpy of vaporization (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 invention, the difference in δ values of the active molecule, ionic liquid, and solvent is controlled within 2 MPa^0.5. A too large difference will lead to poor compatibility and easily generate local heterogeneous regions.

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

[0045] ΔG solv = μ solutiob - μ gas ;

[0046] where 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] During the screening process, a system with ΔG solv less than -15 kJ / mol was regarded as a preferred system.

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

[0049]

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

[0051] By controlling the ratio of the ionic liquid to the co-solvent, the E_a of the system is between 2000 J / mol and 4000 J / mol, ensuring that the system still maintains a low viscosity at -80°C.

[0052] In some experimental verifications, first take C 48 H 36Add 0.75 g of N4 powder to 20 ml of perfluoronaphthalene solvent and stir magnetically at 60 °C for 3 hours. Then slowly add 3 ml of BMI-BF4 dropwise while maintaining stirring. Add 0.1 g of carbazole-based molecules as an additive and continue stirring for 2 hours. Finally, cool down to room temperature for standby.

[0053] In some embodiments, there is no crystallization after the system stands for 48 hours, the color is uniform, and the fluidity is good.

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

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

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

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

[0058] In step S1, the establishment and optimization of the preliminary electrolyte formula have been completed. The ratios of the active material, ionic liquid, ultra-low freezing point solvent, and π-π stacking enhancer have been determined. Next, it is necessary to optimize the electronic structure of the active material 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, for a molecular system that has not been verified by theoretical calculations, electronic orbital collapse or overexcitation is likely to occur under extreme conditions. Therefore, this step requires in-depth analysis of the electronic structure of the active molecules in the system by combining quantum chemical calculations.

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

[0060] In a possible implementation, first establish a molecular model. Optimize its initial geometric configuration. After optimization in the gas phase, perform secondary optimization under the solvent model. The solvent model uses the SMD continuous solvent model.

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

[0062] ΔE = E LUMO - E HOMO ;

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

[0064] Generally, a smaller ΔE will cause the molecule to be too active at low temperatures, leading to uncontrollable side reactions. If ΔE is too large, electron migration will be restricted. Through multiple calculations, in this embodiment, ΔE is finally controlled between 3.0 eV and 5.0 eV.

[0065] As an option, the molecular electrostatic potential distribution 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 region is used to judge the possible reaction sites at low temperatures. The red region is the electron-rich area, and the blue is the electron-deficient area. According to the visualization results, the substituents at the molecular boundary are adjusted to inhibit charge accumulation.

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

[0067]

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

[0069] A larger dipole moment usually indicates a higher molecular polarity and an easy asymmetric interaction with solvent molecules. In one possible implementation, the dipole moment is controlled between 1.5 D and 3.5 D.

[0070] In addition, to quantify the relative density distribution of the molecular orbitals at low temperatures, the Fukui function is calculated in this embodiment. It is defined as follows:

[0071] f + (r) = ρ N+1 (r) - ρ N (r);

[0072] f - (r) = ρ N (r) - ρ N-1 (r);

[0073] where ρ N (r) is the electron density distribution of the molecule with N electrons; ρ N+1 (r) and ρ N-1 (r) are the electron densities when adding or removing an electron, respectively.

[0074] Regions with high Fukui function values are positions prone to nucleophilic or electrophilic attacks. In the present invention, according to these sensitive sites, the molecular boundary structure is further optimized to transfer the sensitive region to a non-reactive region.

[0075] In some embodiments, natural orbital occupancy number (NOON) analysis is also performed to verify whether there is an excited state transition tendency of the molecule under extremely low temperature conditions. Orbits with a large deviation of NOON from the integer 1 are usually excited state participating orbits. Generally, the energy levels of such orbits are adjusted to below -0.8 eV to avoid excitation induced by low temperature.

[0076] This embodiment also refers to the intramolecular rotation potential energy surface scanning calculation. By scanning the backbone dihedral angle, the change trend of the potential energy surface is obtained. The scanning range is from 0° to 360°, with a step size of 10°. A potential energy profile is plotted to ensure that there is no obvious low potential well region.

[0077] To ensure the reliability of the conclusion, all calculations are run in double-precision mode. The convergence criterion is selected as "tight". The number of optimization steps is limited to within 200 steps. The absence of imaginary frequencies in the frequency analysis results indicates that the structure is a stable configuration.

[0078] Generally, the results of quantum chemical calculations are used to provide the initial structure input for subsequent quantum tunneling effect calculations and molecular dynamics simulations. This step, as the pre-electronic structure optimization link, ensures the electrochemical activity, safety, and reaction controllability at low temperature of the system.

[0079] In some of the calculation results, the HOMO energy level of the optimized porphyrin molecule is -5.62 eV, the LUMO energy level is -1.83 eV, ΔE is 3.79 eV, the dipole moment is 2.6 D, the Fukui sensitive sites are located at the carbon atoms of the peripheral benzene ring, the NOON distribution is close to 1.0, and there is no abnormally low potential well in the potential energy scan diagram. This optimized model serves as the input structure for subsequent simulations.

[0080] S3. By calculating the quantum tunneling effect, adjust the molecular configuration and bond length to control the tunneling probability between 10-6 and 10-4, and suppress side reactions;

[0081] In the foregoing steps, the electronic structure of the active molecule has been optimized, and a reasonable range of HOMO-LUMO energy level differences and related charge distribution parameters have been obtained. However, only static structure optimization is not sufficient to fully predict the reaction behavior of the system under extremely low temperature conditions. At low temperatures, some reactions occur not through classical 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 regulate the quantum tunneling probability of the system in step S3 to ensure that the system is in a thermodynamically and quantum mechanically stable state within the range of -80°C to -40°C.

[0082] In this embodiment, the WKB approximation method is used to calculate the quantum tunneling effect. First, a reaction barrier model is constructed. The reaction coordinate is obtained by intrinsic reaction coordinate (IRC) scanning. The scanning step size is taken as 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 used as input parameters. In the WKB approximation, the tunneling probability P t is calculated according to the following formula:

[0084]

[0085] where, is the reduced Planck constant (1.0545718×10 -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 a possible implementation, the potential energy function V(x) is obtained by extracting the barrier height from the Gaussian frequency calculation results and fitting a polynomial model. The integral part uses Gaussian-Legendre numerical integration, and the accuracy is controlled to 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, 1.00784 amu is taken; if it is the stretching reaction path of a carbon-hydrogen bond, it is adjusted according to the effective mass model.

[0088] Generally, the tunneling probability P_t is controlled between 10 -6 and 10 -4 . If it is too high, it is easy to induce low-temperature side reactions. If it is too low, there is no actual interference risk, and the calculation can be stopped.

[0089] Specifically, the non-classical hydrogen migration reaction path that may occur between the boundary substituents and the ionic liquid is mainly considered in the system. Through the IRC results, it is found 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, in this embodiment, the Wentzel–Kramers–Brillouin (WKB) model and the transient state theory (TST) are jointly analyzed. In TST, the expression of the reaction rate k_tst is:

[0091]

[0092] where k B is the Boltzmann constant (1.380649×10 -23 J·K -1 ); T is the calculated 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 a possible implementation, the system temperature is taken as -60 °C (213.15 K) for simulation. The results show that the tunneling probability of the single-molecule hydrogen migration channel is 9.6×10 -5 . This value is within the set range.

[0095] In some embodiments, the Marcus theory is also used to verify the electron transfer path. The formula for calculating 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); ΔG 0 is the standard free energy change of the reaction (J / mol).

[0098] In the system of the present invention, 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 migration path.

[0099] As an option, further adjust the molecular boundary electron cloud distribution. Increase the fluorine substituents on the periphery of the aromatic ring to reduce the electron density in the boundary region. Through recalculation, the tunneling probability drops to 7.2×10 -5 .

[0100] Generally, this step is not only used to confirm the side reaction risk at low temperatures in the system, but also serves as one of the input conditions for subsequent molecular dynamics to ensure the quantum security of the initial configuration.

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

[0102] S4. Conduct molecular dynamics simulations to calculate that the ion diffusion coefficient at low temperatures is between 10-10 m 2 / s and 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 microscopic reaction risk of the system has been controlled within a reasonable range. However, the static calculation results are not sufficient to evaluate the actual migration behavior of ions in solution. Generally, at extremely low temperatures, the ion diffusion rate decreases significantly. The system exhibits high viscosity and low mass transfer rate phenomena. To quantitatively understand this characteristic, it is necessary to conduct molecular dynamics simulations in step S4 and analyze the ion diffusion coefficient and free path distribution in combination with statistical results.

[0104] In this embodiment, the simulation uses the GROMACS 2021.4 version software. The computing platform is a dual-way Intel Xeon processor with 128 GB of memory. The time step is taken as 2 fs, and the total simulation duration is 50 ns. As an option, the system boundary conditions are set as three-dimensional periodic boundaries. The initial box size is 6 nm × 6 nm × 6 nm.

[0105] The system is constructed using Packmol to generate the initial molecular coordinates. Ionic liquids, porphyrin molecules, and solvents 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 50,000 steps.

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

[0107] In some embodiments, the system undergoes stepwise heating and cooling processes. The heating process is from 100 K to 213 K, with a 10 K increase per step and a duration of 100 ps. The cooling process is also performed step by step to ensure full equilibrium of the system.

[0108] Specifically, the equilibration process includes the NVT ensemble and the NPT ensemble. The NVT stage lasts for 1 ns, and the NPT stage lasts for 2 ns. The pressure coupler uses the Parrinello-Rahman algorithm with a pressure of 1 bar. Temperature control is achieved through the V-rescale algorithm with a coupling time constant of 0.1 ps.

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

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

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

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

[0113]

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

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

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

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

[0118]

[0119] where N(λ) is the number of particles with mean free path in the interval from λ to λ + Δλ; N total is the total number of all statistical particles; Δλ is the bin interval (nm).

[0120] In some embodiments, the mean free path distribution curve shows a unimodal distribution, and the peak is located near 0.65 nm.

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

[0122]

[0123] Where dn is the number of particles in the shell from r to r+dr; ρ is the particle volume density (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 solvation layer of the system is stable.

[0125] In some extended studies, anisotropic diffusion tensor calculations were also introduced. The tensor components D_xx, D_yy, and D_zz were extracted separately. The calculation results showed that the difference in the isotropic three-dimensional diffusion behavior was less than 5%.

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

[0127] Finally, all simulation results are batch processed by scripts. The output data is used to form the diffusion coefficient versus temperature curve and free path distribution diagram. The curve fit R2 is higher than 0.98. The data is archived and included in the complete simulation report.

[0128] 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°C to -40°C using differential scanning calorimetry;

[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 have been confirmed. However, under actual operating conditions, especially in low-temperature environments, whether the system undergoes phase separation or local crystallization requires further inspection. In general, supercooled systems are prone to microscopic phase aggregation. It is easy to cause stratification or interface precipitation, which seriously affects battery performance. Therefore, in step S5, it is necessary to predict the low-temperature phase behavior based on the thermodynamic relationship formula and verify it through experimental means.

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

[0131] ∑ i n i dμ i =0;

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

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

[0134]

[0135] where 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 vapor pressures and critical parameters of pure substances measured 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 in the temperature range from -90 °C to -30 °C through Gibbs–Duhem integration. The calculation step size is taken as 2 °C, and the integration accuracy is controlled at 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 results, a differential scanning calorimetry (DSC) experiment is carried out.

[0139] The model of the DSC experimental equipment is TA Instruments Q2000. The sample mass is taken as 5.0 mg to 6.0 mg. The heating rate is set at 5 °C / min. The temperature range is from -100 °C to room temperature. Nitrogen protection atmosphere, with a flow rate of 50 ml / min.

[0140] Specifically, the heat flow curve is recorded during the first cycle of cooling. An endothermic peak is observed in the second cycle of heating curve. Generally, if there is a phenomenon of microcrystal precipitation, 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 addition amount of BMI-BF4, the endothermic peak disappears.

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

[0143] Furthermore, a polarized light microscope (POM) was used to observe the microscopic morphology of the system under the cooling state. The magnification was 400×, and the cold stage temperature was lowered to -85°C. A transient light flash phenomenon was observed in some fields of view, indicating the formation of a transition phase.

[0144] Generally, the appearance of metastable phases in the system under large temperature fluctuations is within the adjustable range. As an option, the concentration of the carbazole additive was changed to weaken this phenomenon.

[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 there was no macroscopic phase separation tendency in the system under low-temperature environments, laying the foundation for the next low-temperature cycle test.

[0146] S6. The ionic conductivity of the electrolyte was tested by electrochemical impedance spectroscopy, and 500 to 1000 charge-discharge cycle tests were carried out in the temperature range of -80°C to -40°C;

[0147] After the thermodynamic analysis and experimental verification of the system, the phase behavior at low temperatures has been preliminarily confirmed, and there is no obvious phase separation or crystallization phenomenon in the system. To further evaluate the stability of the system and its practical application under extreme conditions, electrochemical performance tests are required. In the previous steps, the simulation calculation and experimental verification laid the foundation for the subsequent electrochemical evaluation. Based on this, Step S6 combined the foregoing data to conduct a more detailed evaluation and prediction of the electrochemical stability and cycle performance.

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

[0149] As an option, the electrochemical cycle test was carried out at two temperatures of 25°C and -60°C, and a thermostatic bath was used for temperature control. The current density range was set to 1–50 mA / cm 2 , and the voltage scan rate was 0.1 mV / s. The test cycle was 1000 cycles, ensuring a 30-second interval between each cycle. All current-voltage data were recorded by the Chronoamperometry method.

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

[0151]

[0152] Wherein, I is the current (A); I lim is the current limit value (A); V is the voltage (V); V0 is the characteristic voltage (V) of the system.

[0153] Through the CV test results, the electrochemical window of the system was confirmed to be between -1.5 V and +3.0 V, meeting the design requirements. After the current density reached the peak, the decline was significant.

[0154] In some embodiments, electrochemical impedance spectroscopy (EIS) tests were used to evaluate the conductivity and internal resistance of the system. The EIS tests were carried out in the frequency range of 0.1 Hz to 1 MHz. The Nyquist diagram was used to characterize the impedance response, and the Randles circuit model was used for data fitting. The charge transfer resistance (Rct) and the electrolyte resistance (Rs) were accurately fitted.

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

[0156]

[0157] Wherein, R is the total resistance (Ω) of the electrolyte; A is the electrode area (cm 2 ); l is the thickness (cm) of the electrolyte layer.

[0158] In a possible implementation, the conductivity of the system was measured to be 1.2×10 -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 were carried out under different temperature conditions. For each cycle, the charging current was 10 mA and the discharging current was 5 mA, and the cycle continued until the voltage dropped to 0.5 V. By recording the charge and discharge curves, the specific capacity and cycle stability were calculated.

[0160] In some embodiments, the charging process showed a linear increase, while the discharging process showed a slightly slower decreasing trend. After 1000 cycles, the capacity retention rate of the system reached more than 85%, indicating its good cycle stability.

[0161] In addition, long-term stability tests were also carried out in this embodiment. The system was placed in an environment of -40 °C and cycled continuously for 1000 cycles. The electrochemical performance of the system was detected every 100 cycles. The test results showed that the capacity attenuation of the system did not exceed 12% under long-term low-temperature cycling.

[0162] As an option, the accelerated life test of the battery is also carried out in this step. By increasing the temperature and the current density, 100 accelerated cycle tests are conducted. The test data shows that the accelerated life is relatively stable under high temperature and high current conditions, and no obvious electrolyte decomposition or crystallization phenomenon occurs.

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

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

[0165] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for preparing an electrolyte of a low-temperature resistant organic liquid flow battery, characterized in that, It includes the following steps: S1. Optimize the composition and ratio of the electrolyte. Select bipolar porphyrin-like molecules as the active material, low-viscosity ionic liquids as the supporting electrolyte, ultra-low freezing point solvents as the co-solvent, and add π-π stacking enhancers; S2. Optimize the structure of the active molecule through quantum chemical calculations, calculate the HOMO-LUMO energy level difference, and make it fall within the range of 3.0 eV to 5.0 eV; S3, through the calculation of quantum tunneling effect, adjust the molecular configuration and bond length, and control the tunneling probability within 10 -6 Up to 10 -4 between, inhibiting side effects; S4. Conduct molecular dynamics simulations to calculate that the ion diffusion coefficient at low temperatures ranges from 10-10 m 2 / s to 10-8 m 2 / s, and the mean free path ranges from 0.3 nm to 1.0 nm; S5. Optimize the low-temperature phase behavior based on the Gibbs-Duhem relationship, and use differential scanning calorimetry to verify the phase stability of the electrolyte in the range of -80°C to -40°C; S6. Test the ionic conductivity of the electrolyte through electrochemical impedance spectroscopy, and conduct 500 to 1000 charge-discharge cycle tests in the temperature range of -80°C to -40°C.

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

3. A method for preparing an electrolyte of a low-temperature resistant organic liquid flow battery according to claim 1, characterized in that, The viscosity of the low-viscosity ionic liquid selected 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. A method for preparing an electrolyte for a low-temperature resistant organic liquid flow battery according to claim 1, characterized in that, The freezing point of the ultra-low freezing point solvent is lower than -100°C, and its volume fraction accounts for 50% to 70% of the total electrolyte system.

5. A method for preparing an electrolyte of a low-temperature resistant organic liquid flow battery according to claim 1, characterized in that, The π-π stacking enhancer is a carbazole-like molecule, and its mass fraction is between 0.1% and 2%.

6. The preparation method of an organic liquid flow battery electrolyte resistant to low temperature according to claim 1, characterized in that, The quantum chemical calculation uses density functional theory, the basis set is selected as B3LYP / 6-311++G, and the simulation optimization is carried out by combining the gas phase model and the solvent model.

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

8. A method for preparing an electrolyte for a low-temperature resistant organic flow battery according to claim 7, characterized in that, The molecular dynamics simulation controls the simulation temperature between -80°C and -40°C, establishes a periodic boundary condition model, continuously simulates for 10 ns to 50 ns, and extracts the diffusion coefficient and the mean free path.

9. The preparation method of an organic liquid flow battery electrolyte with low temperature resistance according to claim 1, characterized in that During the optimization process of the low-temperature phase stability, the Gibbs-Duhem integral equation is used to fit with the DSC data, and the electrolyte ratio is adjusted until no metastable phase peak appears. The electrochemical impedance spectroscopy test is carried out in the frequency range from 10⁻² Hz to 10 Hz, and the charge-discharge cycle test current density is controlled between 1 mA / cm 2 and 10 mA / cm 2 .

10. A low-temperature resistant organic liquid flow battery electrolyte, which is used for a preparation method of a low-temperature resistant organic liquid flow battery electrolyte according to any one of claims 1-9, and is characterized in that, The electrolyte includes the following components: Bipolar porphyrin-like molecules as the main active material, with a mass fraction of 0.5% to 5%; 1-butyl-3-methylimidazolium tetrafluoroborate as the supporting electrolyte, with a mass fraction of 5% to 15%; Perfluoronaphthalene as the co-solvent, with a volume fraction of 50% to 80%; Carbazole-like molecules as the π-π stacking enhancer, with a mass fraction of 0.1% to 2%.

Images