Large-scale production of holey graphite as high-rate anode for lithium ion batteries

Lithium ion batteries(LIBs)have been widely used in portable and smart devices because of their high energy densities,long cycle life and environmental friendliness.In order to meet the evergrowing demand for human-beings utilizing electronic devices,electric vehicles and energy storage grids.

Targeted regeneration and upcycling of spent graphite by defect‐driven tin nucleation

The recycling of spent batteries has become increasingly important owing to their wide applications,abundant raw material supply,and sustainable development.Compared with the degraded cathode,spent anode graphite often has a relatively intact structure with few defects after long cycling.Yet,most spent graphite is simply burned or discarded due to its limited value and inferior performance on using conventional recycling methods that are complex,have low efficiency,and fail in performance restoration.Herein,we propose a fast,efficient,and“intelligent”strategy to regenerate and upcycle spent graphite based on defect‐driven targeted remediation.Using Sn as a nanoscale healant,we used rapid heating(~50 ms)to enable dynamic Sn droplets to automatically nucleate around the surface defects on the graphite upon cooling owing to strong binding to the defects(~5.84 eV/atom),thus simultaneously achieving Sn dispersion and graphite remediation.As a result,the regenerated graphite showed enhanced capacity and cycle stability(458.9 mAh g^(−1) at 0.2 A g^(−1) after 100 cycles),superior to those of commercial graphite.Benefiting from the self‐adaption of Sn dispersion,spent graphite with different degrees of defects can be regenerated to similar structures and performance.EverBatt analysis indicates that targeted regeneration and upcycling have significantly lower energy consumption(~99%reduction)and near‐zero CO_(2) emission,and yield much higher profit than hydrometallurgy,which opens a new avenue for direct upcycling of spend graphite in an efficient,green,and profitable manner for sustainable battery manufacture.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Recycled graphite for more sustainable lithium-ion batteries

The demand for lithium-ion batteries(LIBs)is driven largely by their use in electric vehicles,which is projected to increase dramatically in the future.This great success,however,urgently calls for the efficient recycling of LIBs at the end of their life.Herein,we describe a froth flotation-based process to recycle graphite—the predominant active material for the negative electrode—from spent LIBs and investigate its reuse in newly assembled LIBs.It has been found that the structure and morphology of the recycled graphite are essentially unchanged compared to pristine commercial anode-grade graphite,and despite some minor impurities from the recycling process,the recycled graphite provides a remarkable reversible specific capacity of more than 350 mAh g^(−1).Even more importantly,newly assembled graphite‖NMC532 cells show excellent cycling stability with a capacity retention of 80%after 1000 cycles,that is,comparable to the performance of reference full cells comprising pristine commercial graphite.

Spent graphite regeneration:Exploring diverse repairing manners with impurities-catalyzing effect towards high performance and low energy consumption

Spent battery recycling has received considerable attention because of its economic and environmental potential.A large amount of retired graphite has been produced as the main electrode material,accompanied by a detailed exploration of the repair mechanism.However,they still suffer from unclear repair mechanisms and physicochemical evolution.In this study,spent graphite was repaired employing three methodologies:pickling-sintering,pyrogenic-recovery,and high-temperature sintering.Owing to the catalytic effect of the metal-based impurities and temperature control,the as-obtained samples displayed an ordered transformation,including the interlayer distance,crystalline degree,and grain size.As anodes of lithium ions batteries,the capacity of repaired samples reached up to 310 mA h g^(-1)above after 300loops at 1.0 C,similar to that of commercial graphite.Meanwhile,benefitting from the effective assembly of carbon atoms in internal structure of graphite at>1400℃,their initial coulombic efficiency were>87%.Even at 2.0 C,the capacity of samples remained approximately 244 mA h g^(-1)after 500 cycles.Detailed electrochemical and kinetic analyses revealed that a low temperature enhanced the isotropy,thereby enhancing the rate properties.Further,economic and environmental analyses revealed that the revenue obtained through suitable pyrogenic-recovering manners was approximately the largest value(5500$t^(-1)).Thus,this study is expected to clarify the in-depth effect of different repair methods on the traits of graphite,while offering all-round evaluations of repaired graphite.

Growth kinetics of titanium carbide coating by molten salt synthesis process on graphite sheet surface

The synthesis of carbide coatings on graphite substrates using molten salt synthesis(MSS),has garnered significant interest due to its cost-effective nature.This study investigates the reaction process and growth kinetics involved in MSS,shedding light on key aspects of the process.The involvement of Ti powder through liquid-phase mass transfer is revealed,where the diffusion distance and quantity of Ti powder play a crucial role in determining the reaction rate by influencing the C content gradient on both sides of the carbide.Furthermore,the growth kinetics of the carbide coating are predominantly governed by the diffusion behavior of C within the carbide layer,rather than the chemical reaction rate.To analyze the kinetics,the thickness of the carbide layer is measured with respect to heat treatment time and temperature,unveiling a parabolic relationship within the temperature range of 700-1300℃.The estimated activation energy for the reaction is determined to be 179283 J·mol^(-1).These findings offer valuable insights into the synthesis of carbide coatings via MSS,facilitating their optimization and enhancing our understanding of their growth mechanisms and properties for various applications.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

An efficient recycling strategy to eliminate the residual“impurities”while heal the damaged structure of spent graphite anodes

The recycling of graphite from spent lithium-ion batteries(LIBs)is overlooked due to its relatively low added value and the lack of efficient recovering methods.To reuse the spent graphite anodes,we need to eliminate their useless components(mainly the degraded solid electrolyte interphase,SEI)and reconstruct their damaged structure.Herein,a facile and efficient strategy is proposed to recycle the spent graphite on the basis of the careful investigation of the composition of the cycled graphite anodes and the rational design of the regeneration processes.The regenerated graphite,which is revitalized by calcination treatment and acid leaching,delivers superb rate performance and a high specific capacity of 370 mAh g^(-1)(~99% of its theoretical capacity)after 100 cycles at 0.1 C,superior to the commercial graphite anodes.The improved electrochemical performance could be attributed to unchoked Li^(+) transport channels and enhanced charge transfer reaction due to the effective destruction of the degraded SEI and the full recovery of the damaged structure of the spent graphite.This work clarifies that the electrochemical performance of the regenerated graphite could be deteriorated by even a trace amount of the residual“impurity”and provides a facile method for the efficient regeneration of graphite anodes.

Stable and reversible zinc metal anode with fluorinated graphite nanosheets surface coating

A highly stable zinc metal anode modified with a fluorinated graphite nanosheets(FGNSs)coating was designed.The porous structure of the coating layer effectively hinders lateral mass transfer of Zn ions and suppresses dendrite growth.Moreover,the high electronegativity exhibited by fluorine atoms creates an almost superhydrophobic solid-liquid interface,thereby reducing the interaction between solvent water and the zinc substrate.Consequently,this leads to a significant inhibition of hydrogen evolution corrosion and other side reactions.The modified anode demonstrates exceptional cycling stability,as symmetric cells exhibit sustained cycling for over 1400 h at a current density of 5 mA/cm^(2).Moreover,the full cells with NH_(4)V_(4)O_(10)cathode exhibit an impressive capacity retention rate of 92.2%after undergoing 1000 cycles.

Edge and lithium concentration effects on intercalation kinetics for graphite anodes

Graphite interfaces are an important part of the anode in lithium-ion batteries(LIBs),significantly influencing Li intercalation kinetics.Graphite anodes adopt different stacking sequences depending on the concentration of the intercalated Li ions.In this work,we performed first-principles calculations to comprehensively address the energetics and dynamics of Li intercalation and Li vacancy diffusion near the no n-basal edges of graphite,namely the armchair and zigzag-edges,at high Li concentration.We find that surface effects persist in stage-Ⅱ that bind Li strongly at the edge sites.However,the pronounced effect previously identified at the zigzag edge of pristine graphite is reduced in LiC_(12),penetrating only to the subsurface site,and eventually disappearing in LiC_(6).Consequently,the distinctive surface state at the zigzag edge significantly impacts and restrains the charging rate at the initial lithiation of graphite anodes,whilst diminishes with an increasing degree of lithiation.Longer diffusion time for Li hopping to the bulk site from either the zigzag edge or the armchair edge in LiC_(6) was observed during high state of charge due to charge repulsion.Effectively controlling Li occupation and diffusion kinetics at this stage is also crucial for enhancing the charge rate.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Thermal fatigue and wear of compacted graphite iron brake discs with various thermomechanical properties

The increase in payload capacity of trucks has heightened the demand for cost-effective yet high performance brake discs.In this work,the thermal fatigue and wear of compacted graphite iron brake discs were investigated,aiming to provide an experimental foundation for achieving a balance between their thermal and mechanical properties.Compacted graphite iron brake discs with different tensile strengths,macrohardnesses,specific heat capacities and thermal diffusion coefficients were produced by changing the proportion and strength of ferrite.The peak temperature,pressure load and friction coefficient of compacted graphite iron brake discs were analyzed through inertia friction tests.The morphology of thermal cracks and 3D profiles of the worn surfaces were also discussed.It is found that the thermal fatigue of compacted graphite iron discs is determined by their thermal properties.A compacted graphite iron with the highest specific heat capacity and thermal diffusion coefficient exhibits optimal thermal fatigue resistance.Oxidization of the matrix at low temperatures significantly weakens the function of alloy strengthening in hindering the propagation of thermal cracks.Despite the reduced hardness,increasing the ferrite proportion can mitigate wear loss resulting from low disc temperatures and the absence of abrasive wear.

Residual fluoride self-activated effect enabling upgraded utilization of recycled graphite anode

Recycling graphite anode from spent lithium-ion batteries(SLIBs)is regarded as a crucial approach to promoting sustainable energy storage industry.However,the recycled graphite(RG)generally presents degraded structure and performance.Herein,the residual fluoride self-activated effect is proposed for the upgraded utilization of RG.Simple and low-energy water immersion treatment not only widens the interlayer spacing,but also retains appropriate fluoride on the surface of RG.Theoretical analysis and experiments demonstrate that the residual fluoride can optimize Li~+migration and deposition kinetics,resulting in better Li~+intercalation/deintercalation in the interlayer and more stable Li metal plating/stripping on the surface of RG,As a result,the designed LFP||RG full cells achieve ultrahigh reversibility(~100%Coulombic efficiency),high capacity retention(67%after 200 cycles,0.85 N/P ratio),and commendable adaptability(stable cycling without short-circuiting,0.15 N/P ratio).The energy density is improved from 334 Wh kg^(-1)of 1.1 N/P ratio to 367 Wh kg^(-1)of 0.85 N/P ratio(total mass based on cathode and anode).The exploration of RG by residual fluoride self-activated effect achieves upgraded utilization beyond fresh commercial graphite and highlights a new strategy for efficient reuse of SLIBs.

Protective Graphite Coating for Two-Dimensional Carbon/Carbon Composites

Two-dimensional carbon/carbon(2D C/C)composites are a special class of carbon/carbon composites,generally obtained by combining resin-impregnated carbon fiber clothes,which are then cured and carbonized.This study deals with the preparation of a protective coating for these materials.This coating,based on graphite,was prepared by the slurry method.The effect of graphite and phenolic resin powders with different weight ratios was examined.The results have shown that the coating slurry can fill the pores and cracks of the composite surface,thereby densifying the surface layer of the material.With the increase of the graphite powder/phenolic resin weight ratio,the coating density is enhanced while the coating surface flatness decreases;moreover,the protective ability of coating against erosion first increases(from 1:3 to 2:2)and then decreases(from 2:2 to 3:1).When the weight ratio is about 1:1,the coating for 2D C/C composites exhibits the best erosion resistance,which greatly aids these materials during gas quenching.In this case,the erosion rate is decreased by approximately 41.5%at the impact angle of 30°and 52.3%at normal impact,respectively.This can be attributed to the ability of the coating slurry to infiltrate into the substrate,thereby bonding the fibers together and increasing the compactness of the 2D C/C composites.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Probing the electric double layer structure at nitrogen-doped graphite electrodes by constant-potential molecular dynamics simulations

Electric double layer(EDL)is a critical topic in electrochemistry and largely determines the working performance of lithium batteries.However,atomic insights into the EDL structures on heteroatom-modified graphite anodes and EDL evolution with electrode potential are very lacking.Herein,a constant-potential molecular dynamics(CPMD)method is proposed to probe the EDL structure under working conditions,taking N-doped graphite electrodes and carbonate electrolytes as an example.An interface model was developed,incorporating the electrode potential and atom electronegativities.As a result,an insightful atomic scenario for the EDL structure under varied electrode potentials has been established,which unveils the important role of doping sites in regulating both the EDL structures and the following electrochemical reactions at the atomic level.Specifically,the negatively charged N atoms repel the anions and adsorb Li~+at high and low potentials,respectively.Such preferential adsorption suggests that Ndoped graphite can promote Li~+desolvation and regulate the location of Li~+deposition.This CPMD method not only unveils the mysterious function of N-doping from the viewpoint of EDL at the atomic level but also applies to probe the interfacial structure on other complicated electrodes.

Effect of FeSi additive in dual-chamber sample cup on thermal analysis characteristic values and vermiculating rate of compacted graphite iron

Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements affecting the solidification of molten iron.In this study,FeSi75 was added in one chamber of the dual-chamber sample cup,and the influences of FeSi75 additive on the characteristic values of thermal analysis curves and vermiculating rate were investigated.The results show that with the increase of FeSi75,the start temperature of austenite formation TALfirstly decreases and then increases,but the start temperature of eutectic growth TSEF,the lowest eutectic temperature TEU,temperature at maximum eutectic reaction rate TEM,and highest eutectic temperature TERkeep always an increase.The temperature at final solidification point TEShas little change.The FeSi75 additive has different influences on the vermiculating rate of molten iron with different vermiculation,and the vermiculating rate increases for lower vermiculation molten iron while decreases for higher one.According to the thermal analysis curves obtained by a dual-chamber sample cup with 0.30wt.%FeSi75 additive in one chamber,the vermiculating rate of molten iron can be evaluated by comparing the characteristic values of these curves.The time differenceΔtERcorresponding to the highest eutectic temperature TERhas a closer relationship with the vermiculating rate,and a parabolic regression curve between the time differenceΔtERand vermiculating rateηhas been obtained within the range of 65%to 95%,which is suitable for the qualified melt.