573 results about "Free energies" patented technology

The invention relates to the technical field of safety and environmental protection, in particular to a negative-pressure circulating explosion-control dust removal method suitable for combustible dust places, which comprises the following steps: firstly, sealing a bin body and injecting carbon dioxide-nitrogen inert gas to generate a low-oxygen environment; then, a flow guide angle-adjustable cyclone separator, a dielectric barrier discharge plasma reactor and a waste heat thermo-acoustic transducer are driven to cooperatively capture dust, and sound node amplitude synthesis capture data are obtained through compressed sensing of a microphone; inputting the environment data and the capture data into a reinforcement learning process containing a micro-thermochemistry model, and outputting instructions for adjusting negative pressure pulse, inert flow, sound field intensity and cyclone geometry; in combination with distributed radio frequency imaging data, an adversarial digital twinborn model is generated to predict explosion free energy, and automatic rollback or shutdown is carried out when the explosion free energy exceeds a threshold, so that a safe, efficient and low-energy-consumption closed loop is formed.

The present application relates to the technical field of catalysts and relates to a non-noble metal cathode hydrogen evolution catalyst for PEM water electrolysis and a use thereof. The present application provides a method for preparing a cathode hydrogen evolution catalyst. A nickel-molybdenum heteropoly acid having a specific structure is generated in situ on the surface of a porous carbon material carrier, and is used as a hydrogen evolution catalyst active component precursor, and an in-situ generated nickel-containing molybdenum sulfide active component is subjected to sulfidation treatment to prepare a non-noble-metal-supported cathode hydrogen evolution catalyst, wherein the in-situ generated nickel-containing molybdenum sulfide active component has a hydrogen adsorption free energy similar to that of Pt and catalytic hydrogen evolution activity, the dispersity is good, and the number of active sites is large, thereby reducing the costs of the catalyst; in addition, during the sulfidation treatment of the nickel-molybdenum heteropoly acid, nitrogen or phosphorus atoms in the carrier can be doped into the lattice of molybdenum disulfide, thereby reducing the hydrogen evolution Gibbs free energy of sulfur atoms on an MoS2 crystal basal surface, and more active sites are formed on a sulfur edge and a molybdenum edge, facilitating a hydrogen evolution reaction.

The invention discloses a universal nucleic acid probe design method based on hybridization thermodynamics and kinetics, which comprises the following steps: 1) determining a target amplification region according to a fragment interval, generating a primer pair which meets the length requirement and covers a target fragment, calculating the Gibbs free energy change of nucleic acid hybridization by adopting an intelligent nucleic acid hybridization algorithm, and calculating the Gibbs free energy change of the target fragment according to the Gibbs free energy change; by combining chemical thermodynamics, kinetic parameters, sequence complementarity and enzymatic reaction characteristics, generating an optimal primer pair under a multi-dimensional constraint condition; and 2) on the basis of the generated optimal primer pair, generating a probe combination meeting a free energy difference threshold as a candidate pool, screening the probe combination with the highest objective function value from the candidate pool through an iterative process, and updating the candidate pool step by step until the primer pair and the probe combination with the highest objective function value are output. By introducing an intelligent algorithm and combining a thermodynamic model, a dynamic model and specificity evaluation, an optimal primer and probe combination is automatically generated, and the stability and reliability of design are ensured.

The invention relates to the field of additive manufacturing, and discloses a dynamic phase change control additive ultrahigh-strength steel strengthening and toughening process which comprises the following steps: S1, constructing a phase change structure design target which comprises retained austenite distribution, a structure space structure and mechanical property requirements; s2, based on the phase change structure design target, establishing a free energy model taking a phase field variable as a core for representing a phase change driving behavior between austenite and martensite; s3, on the basis of the free energy model, constructing a multi-field partial differential control model coupling phase field evolution, element diffusion and heat conduction; and S4, in combination with the multi-field partial differential control model, determining an interface adsorption parameter, a diffusion coefficient and a thermophysical parameter based on experimental characterization data. According to the method, path parameters are optimized through a multi-field partial differential control model and tissue deviation feedback, and accurate control of the phase change tissue and dynamic closed-loop adjustment of the machining process are achieved.

The invention relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which comprises the following steps of: acquiring dynamic characteristic data in a forging process in real time by acquiring multi-dimensional material performance parameters, and constructing a multi-target collaborative optimization model; and intelligent collaborative configuration of the reduction rate parameters is realized by adopting a self-adaptive weight distribution strategy. And a thermal coupling factor normalization equation and a phase field free energy functional normalization model are established, and the technical problems that in a traditional forging process, due to empirical configuration of reduction rate parameters, the structure is uneven, and residual stress exceeds the standard are solved. A lightweight model is deployed through edge computing nodes, millisecond-level dynamic correction of an optimization scheme is realized in combination with a 5G industrial private network, and the grain size qualification rate and the size precision of the titanium alloy forgings are improved. The method improves the yield of the grain size of the forge piece, ensures that the residual stress of the forge piece is reduced, stably controls the size precision, and improves the batch stability of high-end titanium alloy parts.

The present application relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which acquires dynamic characteristic data of a forging process in real time through multi-dimensional material performance parameter collection, constructs a multi-objective collaborative optimization model, and realizes intelligent collaborative configuration of the reduction rate parameter by using an adaptive weight distribution strategy. A thermal coupling factor normalization equation and a phase field free energy functional normalization model are established to solve the technical problems of uneven structure and excessive residual stress caused by the empirical configuration of the reduction rate parameter in the traditional forging process. A lightweight model is deployed through an edge computing node, and millisecond-level dynamic correction of the optimization scheme is realized in combination with a 5G industrial private network, thereby improving the grain size qualification rate and size accuracy of titanium alloy forgings. The present application improves the grain size qualification rate of forgings, ensures stable control of the size accuracy while reducing the residual stress of forgings, and improves the batch stability of high-end titanium alloy components.

The invention discloses a method for predicting the catalytic activity of a bimetallic supported catalyst in a CO2RR reaction, and belongs to the technical field of computational material science and catalytic materials. The method comprises the following steps: constructing a spatial configuration of TMCu / W2CO2 with double metal atoms (TMCu) loaded on the surface of W2CO2; a relaxation configuration with the distance between TM atoms and Cu atoms smaller than 3.0 is screened out; calculating the adsorption energy Eads of adsorbing CO2 at different sites, and screening out a catalyst model with strong adsorption capacity on reactants; the optimal path in the reaction process is determined, the limiting potential UL of the surface of the catalyst is calculated based on the maximum value of the Gibbs free energy change delta G in the optimal path, and the UL is used for predicting the activity of different TMCu / W2CO2 surface electrocatalysis CO2RR. According to the method, screening standards taking the distance between double metal atoms, the binding energy of double metals, the adsorption energy of CO2 and the limited potential as the core are established, a low-efficiency system is rapidly eliminated, the experiment trial and error cost is reduced, and the development efficiency of the catalyst is improved.

The invention relates to a high-temperature heating furnace temperature measurement system based on a temperature emissivity inversion algorithm, and belongs to the technical field of metallurgical industry temperature measurement. The system comprises a spectrum detection system, a master control upper computer and a temperature adjusting system, the spectrum detection system is used for acquiring a plurality of wavelengths and corresponding gray values of a melt in the heating furnace; the temperature adjusting system is used for adjusting the temperature of the heating furnace; the master control upper computer is used for receiving the wavelengths and the corresponding gray values obtained by the spectrum detection system to obtain the spectral radiation brightness corresponding to the wavelengths; obtaining the temperature of the melt in the heating furnace by using a free energy minimization temperature emissivity inversion method based on each wavelength and the corresponding spectral radiance; and the temperature adjusting system is controlled to adjust the temperature of the heating furnace based on the temperature of the melt in the heating furnace. Real-time and accurate temperature measurement of melts with different emissivity is realized, the production cost is reduced, and the temperature measurement precision is high.

The invention discloses a driving risk cognitive map construction method and system based on free energy, and the method comprises the steps: inputting a starting position and a target position of an intelligent driving vehicle into a trained risk entropy calculation model, so as to output a risk cognitive map of the intelligent driving vehicle; the training process of the risk entropy calculation model is as follows: acquiring environment state information of a vehicle to construct a training data set; constructing a perception and prediction model based on a free energy theory by using the environmental state information, wherein the free energy is the difference between the prediction error of the vehicle to the environmental state and a real perception value; information entropy is introduced into the perception and prediction model to define driving risk entropy, the risk degree of the vehicle in the environment is represented, and therefore the free energy of the current state of the vehicle is calculated; a driving risk entropy is calculated in a continuous time sequence manner, a high-risk area in the risk cognition map is dynamically updated in combination with a historical driving track of the vehicle, and the updated risk cognition map is provided for a decision module of the vehicle; constructing a loss function to adjust trainable parameters in the risk entropy calculation model; according to the risk cognition map construction method and system, dynamic risk assessment of the intelligent driving vehicle in a complex driving environment is effectively realized.

The invention discloses a method for exploring a heterogeneous catalytic reaction network based on a deep potential energy surface model, and the method comprises the steps: carrying out the accurate prediction of the free energy change of all possible element reactions on a catalyst through the combination of transfer learning and a density functional theory, and constructing a reaction network diagram based on a graph theory method; an improved Dijkstra method is used for searching a reaction network diagram to determine a reaction mechanism, and the selectivity of the catalyst is explored based on a Markov chain method. According to the technical scheme provided by the invention, when the CO2 reduction reaction mechanism on the monatomic alloy is explored, the prediction error MAE of the electron energy of each intermediate is only 0.16 eV, the calculated amount is reduced by 90%, the dominant path of the CO2 reduction reaction is visually displayed, and a new normal form is provided for complex mechanism exploration and catalyst screening.

The invention provides a method for extracting lithium, rubidium and cesium through strengthened roasting of lepidolite and preparing lithium, rubidium and cesium salt, and belongs to the technical field of lithium, rubidium and cesium extraction.The method comprises the steps that lepidolite, a carbon / sulfur additive and chlorine salt are subjected to ball milling, roasting and grinding, and roasted sand is obtained; through thermodynamic analysis on the lepidolite, after the carbon / sulfur additive is introduced, the Gibbs free energy of the reaction of the lepidolite and the additive is reduced, the reaction trend is improved, and in the roasting process, ions such as sodium, potassium and calcium in chlorine salt and the carbon / sulfur additive are replaced in a similar isomorphic manner, so that lithium, rubidium and cesium are leached more easily; in the process of intensifying roasting by introducing the carbon / sulfur additive, the use amount of the chlorine salt additive is reduced by 10-25%, potassium-sodium mixed salt and calcium oxalate are recycled, the recycling rate is greater than 50%, and the leaching residue amount is reduced by 30-40%; and the carbon / sulfur additive can directionally promote the reaction of the calcium salt additive and the lepidolite and convert the calcium salt additive into dicalcium silicate, so that the leaching residues obtained in the subsequent leaching process are easier to recycle.

The invention belongs to the field of medical technology analysis, and discloses an analysis method and system for revealing a hidden binding pocket of a drug target, and the method comprises the steps: firstly obtaining a representative conformation metastable state of a target protein through conventional molecular dynamics simulation and clustering analysis; secondly, constructing a Markov state model to analyze a dynamic transformation rule between conformations; carrying out enhanced sampling by adopting meta-dynamics, and deeply exploring a rare conformation space; and finally, constructing a free energy landscape to quantitatively evaluate the relative stability of the conformation, and identifying a hidden binding pocket in the stable rare conformation. According to the method, the limitation of a single calculation means is overcome, a full-chain calculation system of dynamic conformation analysis-hidden cavity feature mining-novel ligand rational design is constructed, and the formation mechanism and potential druggability of the hidden pocket can be comprehensively revealed from the two dimensions of dynamics and thermodynamics; and an efficient and accurate calculation framework is provided for research and development of innovative drugs targeting difficult drug targets.

The invention discloses a natural gas hydrogen-doped or pure hydrogen verification and calibration loop. The loop comprises a gas supplementing and exhausting device, a filtering device, a circulating fan, a heat exchanger, a temperature control system, a bypass adjusting device, a working-grade standard device, a detected device, a transmission turbine device and a verification control system which are sequentially connected according to a gas flowing sequence to form the loop, the calibration method comprises the following steps: step 1, analyzing an obtained gas component xi by a mass spectrum analyzer; 2, calculating a contrast density function # imgabs0 # and a contrast temperature function # imgabs1 #; 3, assuming the molar density rho value of the gas mixture to obtain the contrast density delta and the reciprocal tau of the contrast temperature of the gas mixture; and 4, calculating to obtain comparison Helmholtz free energy # imgabs2 # binary comparison Helmholtz free energy # imgabs3 # and ideal Helmholtz free energy # imgabs4 #. The loop and the calibration method effectively solve the problems of verification and calibration of the detected flowmeter for trade measurement of natural gas doped hydrogen or pure hydrogen gas.

The invention provides a collecting agent for micro-fine particle quartz ore and a preparation method and application of the collecting agent, and relates to the technical field of mineral flotation. Specifically, the preparation method of the collecting agent comprises the steps that a mixture containing the graphene and the sodium oleate is subjected to cold plasma treatment, and the graphene and the sodium oleate are grafted to obtain the collecting agent. The collecting agent can be used for mineral flotation, and high-purity quartz can be efficiently obtained; on the basis of the net capturing performance of graphene and functional groups corresponding to grafted sodium oleate, the flocculation effect on micro-fine quartz is achieved, so that the quartz is efficiently captured, the problem that the fine quartz is free in ore pulp due to large free energy in the flotation process and is difficult to capture and float is solved, the flotation separation efficiency is improved, the separation index is improved, and the flotation cost is reduced. Good application prospects are realized.

The invention relates to the cross technical field of industrial intelligent simulation and computational material science, in particular to a phase field simulation method and device based on physical constraint deep learning and electronic equipment, and the method comprises the steps: obtaining phase field parameters of a to-be-predicted material, including an initial phase field value, an initial material mobility tensor and an initial free energy density coefficient, the phase field parameters are input into a pre-trained prediction model, so that a phase field simulation result of the to-be-predicted material is obtained, and the pre-trained prediction model is used for predicting the phase field of the to-be-predicted material based on the improved LSTM network, a preset hybrid forward propagation strategy and a preset parameter coupling mechanism. And training a preset neural network by using a mixed loss function and a sub-path gradient back propagation strategy. Therefore, the problems of low phase field simulation calculation efficiency, physics non-conservation of a data driving model and the like in the related technology are solved, physical equation constraints are embedded into the neural network through a hybrid model architecture, and efficient and accurate prediction of material microstructure evolution is realized.

The invention discloses a porous high-entropy alloy structure layer for strengthening aluminum / steel heterogeneous interface connection and a selective laser melting forming method of the porous high-entropy alloy structure layer, and belongs to the field of high-entropy alloy materials. The porous high-entropy alloy structure layer comprises FeCoCrNiMn high-entropy alloy and is of a single cubic array stacking structure formed through selective laser melting. The selective laser melting forming method comprises the steps that a porous cubic structure model is designed through Majics three-dimensional software and guided into selective laser melting manufacturing equipment, a metal powder rapid forming system is started, FeCoCrNiMn high-entropy alloy powder is subjected to selective laser melting forming, and the high-strength and high-toughness high-entropy alloy coating is obtained. By effectively controlling the laser power and the three-dimensional model parameters, free energy of an alloy system is effectively reduced, formation of intermetallic compounds is inhibited, the mechanical property of aluminum / steel dissimilar metal connection is improved, and the method has wide application prospects in the aspect of aluminum / steel dissimilar metal connection.

The invention provides an energy management method and device for an elevator energy supply system. The method is executed by a cloud server and a local controller which work cooperatively. A cloud server obtains historical and real-time operation data of an energy supply system, predicts the available output power of a photovoltaic unit and the comprehensive power demand of an elevator load in a first time period in the future, generates an energy scheduling strategy of the first time period through optimization calculation, and issues the energy scheduling strategy to a local controller; the local controller collects the local real-time operation state of the energy supply system, selects the current execution strategy and controls the energy flow path of each unit so as to supply power to the elevator load or recover the regenerated energy of the elevator load. According to the method, the energy-saving economy can be improved, the two free energy sources including photovoltaic power generation and elevator regeneration energy are preferentially utilized to the maximum extent through cloud collaborative scheduling, the energy storage charging and discharging strategy is optimized to make full use of the commercial power peak-valley price difference, and the overall operation electric charge of an elevator system is remarkably reduced.

The invention relates to a self-supporting electrode and a preparation method and application thereof, and belongs to the technical field of new energy and alkaline electro-catalytic water decomposition. A preparation method of a self-supporting electrode comprises the following steps: taking a nickel net, foamed nickel or a stainless steel net as a substrate, taking deionized water containing a ferric iron compound and phosphate as an etching solution, soaking the substrate in the etching solution at room temperature, taking out the substrate, and drying at room temperature to obtain a surface-modified self-supporting electrode; according to the method, PO4 < 3-> is introduced while the Fe / M self-supporting electrode is synthesized through a one-step etching method, so that the free energy of * OH and * O intermediates is adjusted, the valence state is adjusted, and the oxygen evolution reaction rate is increased; meanwhile, PO4 < 3-> and Fe sites act, so that the Fe coordination sites are stabilized, and precipitation of iron is prevented; the self-supporting electrode prepared by using a room-temperature one-step chemical etching method not only has good water electrolysis oxygen evolution reaction activity, stability and repeatability, but also is simple and rapid in preparation process and low in cost, and large-scale industrial production can be realized.

The invention discloses a bridge temperature-induced strain prediction method under the influence of non-constant noise, and the method comprises the steps: building a training set containing main beam temperature and temperature-induced strain according to historical monitoring data, and assuming a test set; under a heterovariance Gaussian process framework, constructing conditional distribution of temperature-induced strain of a main beam of the test set as an undetermined heterovariance Gaussian regression model; a variational Bayesian inference method is adopted to construct a variational free energy boundary based on variational posteriori distribution, and approximation is performed on the variational free energy boundary to obtain an edge variational boundary; assuming that the variational posteriori distribution of the noise logarithmic variance function obeys multivariate normal distribution, simplifying an edge variational boundary into a new edge variational boundary, and solving to obtain a hyper-parameter and a variational parameter; and substituting the obtained result into the to-be-determined heterovariance Gaussian regression model, obtaining new main beam temperature data, and predicting the temperature-induced strain of the main beam in the test set. The bridge temperature-induced strain prediction method under the influence of the non-constant noise is established, the prediction precision can be ensured, and the calculation efficiency can be improved.

The invention relates to the crossing field of artificial intelligence and material science, and provides an automatic generation method and system for a hydrogel formula for an ultrathin lens based on energy guidance, and the method comprises the steps: constructing a component feature space; establishing a free energy density function fusing a Flory-Hugins theory and elastic network correction, wherein the free energy density function is used for predicting a swelling ratio, a modulus and a diffusion coefficient; deploying a generative adversarial network, and introducing an energy guide item formed by a mean square error of prediction performance and target performance into generator loss; and enabling the generator to output a feasible formula meeting triple performance constraints through iterative training. The system supports Monte Carlo sampling screening, diversity enhancement, cross-scene migration adaptation, real-time error feedback correction, manufacturable hard constraint filtering and multi-target Pareto frontier exploration. An AI frame is deeply embedded through a physical model, the realizability of formula thermodynamics is ensured, the design precision, generalization ability and industrial applicability are remarkably improved, and automatic closed-loop evolution of hydrogel intelligent design is achieved.

The invention relates to a temperature field imaging method based on free energy minimization, and belongs to the technical field of temperature field temperature distribution monitoring. Comprising the following steps: acquiring a multispectral image of a to-be-measured temperature field; wherein each pixel point in the multispectral image comprises a plurality of wavelengths and corresponding gray values; the pixel points comprise background pixel points and object pixel points; based on each wavelength and the corresponding gray value of each object pixel point, using pre-calibrated gray value-spectral radiation brightness corresponding data to obtain spectral radiation brightness corresponding to each wavelength of each object pixel point in the multispectral image; based on each wavelength of each object pixel point and the corresponding spectral radiation brightness, obtaining the temperature of each object pixel point by using a spectral emissivity temperature separation inversion method based on free energy minimization; and obtaining a temperature distribution diagram of the temperature field based on the temperature of each object pixel point. The accurate and efficient inversion of the temperature distribution state of the temperature field is realized.

The invention relates to the technical field of inorganic material synthesis, in particular to a preparation method of a controlled-release magnesium hydroxide fertilizer. Comprising the steps of primary magnesium precipitation, hydrothermal treatment, secondary magnesium precipitation, anaerobic heat treatment and pulping. According to the method, firstly, crystal defects are eliminated through hydro-thermal treatment, crystal seeds with complete crystallinity are obtained, then the crystal seeds are added into a magnesium precipitation reaction system, the principle of Gibbs free energy minimization is followed, newly generated magnesium hydroxide tends to precipitate on the surfaces of the crystal seeds, under the interference effect of a crystal form regulator, the newly generated magnesium hydroxide crystal has many defects, and the magnesium precipitation effect is good. And then oxygen-free heat treatment is carried out, so that the defects of the magnesium hydroxide on the surface layer are further increased, and the initial dissolution and release capacity is improved. The controlled-release magnesium fertilizer which is high in early-stage release speed and long-acting in later-stage slow release is synthesized by utilizing a crystal defect engineering tool, and is different from a traditional coating scheme, and a brand new thought is provided for the design of the controlled-release fertilizer.

The invention discloses a method for predicting molecular solvation free energy based on machine learning, relates to the technical field of computational chemistry, and solves the technical problem that a molecular dynamics method consumes a large amount of computing resources and time. The method comprises the following steps: using SMILES formulas of solute molecules and solvent molecules as input data; converting the SMILES formula into a one-dimensional descriptor, a two-dimensional descriptor and a three-dimensional descriptor of a molecule based on an RDKit tool; based on an open source experiment data set, training a machine learning model through an association relationship between a one-dimensional descriptor, a two-dimensional descriptor and a three-dimensional descriptor of a molecule and experiment solvation free energy to obtain a prediction model; and inputting molecular descriptors of solute molecules and solvent molecules into the prediction model to obtain a result of predicting solvation free energy of the solute molecules. Existing molecular dynamics simulation is replaced by the machine learning model, and meanwhile, model training is performed by adopting a machine learning algorithm and experimental data, so that the accuracy and the stability of a prediction result are improved.

The invention relates to the technical field of metal smelting, in particular to a metal smelting temperature control method and system. The system comprises a temperature monitoring module, a gas analysis module, a thermodynamic simulation module and a dynamic regulation and control module, the thermodynamic simulation module sets a fitness function according to the Gibbs free energy value, and obtains the optimal temperature and oxygen potential through a particle swarm optimization algorithm; the dynamic regulation and control module is used for converting the theoretical optimal temperature of the thermodynamic model into precise operation, and efficient and stable control over the smelting temperature is achieved. According to the method, the reaction process in the furnace and the current optimal temperature and oxygen potential are accurately controlled on the basis of the Gibbs free energy value and the particle swarm optimization algorithm, the theoretical optimal temperature of a thermodynamic model is converted into precise operation through multi-PID cooperative control, and efficient and stable control over the antimony smelting temperature is achieved; and therefore, the problem that the oxygen-enriched smelting temperature is not accurately controlled is fundamentally solved, the extraction rate of metal is increased, and energy consumption is reduced.

There is disclosed a hybrid system for generating free energy from dam-water by integrating giant turbines with piezoelectric nanogenerators and solar cells. This three-in-one generator harnesses electrical energy from solar and mechanical energy. Strategically positioned large-scale turbines convert the kinetic energy of water flow into electrical energy. Concurrently, integrated piezoelectric nanogenerators transform mechanical stress from turbine operations into electrical energy via the piezoelectric effect. These nanogenerators function as self-charging hybrid supercapacitors (SCHSCs), featuring activated carbon (AC) electrodes and PVA-KOH-BTO electrolyte. Solar panels utilizing dye-sensitized solar cells (DSSCs) with TiO2 photoanodes, molybdenum disulfide (MoS2) counter electrodes, an iodine-based electrolyte, and dyes enhance energy conversion efficiency under varying solar conditions. The system efficiently generates electricity even under low-flow conditions, minimal sunlight, or when turbines are not operating at full capacity.

The invention relates to an alkali metal gas chamber anti-relaxation self-assembly molecular coating adsorption energy quantification method, which finally realizes the physical quantification of the anti-relaxation coating adsorption energy by establishing a contact angle and surface topography collaborative characterization model and combining an adsorption-desorption dynamic formula, and is characterized by comprising the following steps: 1) establishing a contact angle and surface topography collaborative characterization model; depositing organosilane on a gas chamber glass substrate by adopting a liquid phase self-assembly method to form an anti-relaxation coating sample; step 2, selecting a plurality of measuring points on the anti-relaxation coating sample, dropping a 1 [mu] L ultrapure water liquid drop on each measuring point, determining a polarization retention angle of each measuring point according to the liquid drop height and the liquid drop substrate radius obtained by a contact angle measuring instrument, and scanning a local area of the corresponding measuring point by using an atomic force microscope (AFM), extracting an arithmetic mean value Ra of the surface roughness parameter and a root-mean-square value Rq of the surface roughness parameter as quantitative indexes of the polarization maintaining cross section; and step 3, taking free energy formed by the molecular layer of the coating as coating adsorption energy delta G.

The invention discloses a non-aqueous electrolyte and a sodium ion battery thereof. The non-aqueous electrolyte comprises a sodium salt, a non-aqueous organic solvent and an additive, the additive comprises a 1, 3-dicarbonyl-4-trifluoromethyl compound as shown in a formula 1, and R is selected from aryl or heterocyclic radical. The additive of the non-aqueous electrolyte contains the 1, 3-dicarbonyl-4-trifluoromethyl compound, so that electropolymerization can be carried out on a positive electrode to form an SEI film to wrap a positive electrode material, metal ions can be effectively prevented from being dissolved out, and the cycle performance of the battery is further effectively improved. Meanwhile, the chaos degree of an electrolyte system can be increased to achieve the purpose of increasing the entropy value of the electrolyte, so that the Gibbs free energy in the salt dissolution process is reduced, low-temperature sodium salt precipitation is improved, the ionic conductivity of the non-aqueous electrolyte can be effectively improved, and the liquid temperature range of the non-aqueous electrolyte can be widened. # imgabs0 # formula 1

The utility model relates to the technical field of battery production, and provides a buffer mechanism for rolling a solid non-cell energy storage power supply, which comprises a rack, a plurality of feeding extrusion rollers are rotatably arranged on the rack, an extrusion gap is formed between two feeding extrusion rollers in one group, the number of the feeding extrusion rollers is at least two, and the number of the feeding extrusion rollers is at least two. The pressing rollers are rotationally arranged on the rack, every two pressing rollers form a group, a pressing gap is formed between every two pressing rollers in one group, the pressing rollers are arranged below the feeding extrusion roller, the buffering piece is arranged on the rack in a swinging mode and located between the feeding extrusion roller and the pressing rollers, and after materials are extruded by the feeding extrusion roller through the extrusion gap, the materials are conveyed to the feeding extrusion roller through the buffering piece. And the buffer piece enters the pressing gap and is pressed by the pressing roller. According to the technical scheme, the problem that the product consistency and the quality stability are affected due to the fact that the material thickness is changed due to the fact that the tension is unstable or uneven during extrusion processing and die assembly of the positive and negative pole diaphragms in the prior art is solved.

A method includes calculating, by a node within a machine learning model, free energy measures corresponding to all nodal actions in a predetermined plurality of nodal actions that are performable by the node within the machine learning model during an upcoming timestep. A nodal action is selected to be performed by the node during the upcoming timestep based on comparisons among the free energy measures. The node performs the selected nodal action during the upcoming timestep.

The invention discloses a method and a device for simulating the mechanical property of a zirconium-tin-niobium alloy under an irradiation condition, and mainly aims to solve the problems of lack of microstructure simulation of the irradiated zirconium-tin-niobium alloy and high cost, long period and low safety coefficient of mechanical property inspection of the irradiated zirconium-tin-niobium alloy. Comprising the following steps: establishing a microstructure model of the zirconium-tin-niobium alloy by adopting a phase field method, and introducing a rate theoretical model of irradiation defect evolution into the model to obtain a microstructure evolution model of the zirconium-tin-niobium alloy under the irradiation condition; the defect concentration under the irradiation condition is determined based on the model; the zirconium element concentration and the niobium element concentration under the irradiation condition are obtained, and the nonlinear elastic energy density of the zirconium-tin-niobium alloy is determined based on the obtained concentration values; determining a total free energy density function of the zirconium-tin-niobium alloy based on the nonlinear elastic energy density, the defect concentration, the zirconium element concentration and the niobium element concentration of the zirconium-tin-niobium alloy under the irradiation condition; and performing derivation processing on the total free energy density function to obtain a target stress tensor component.