VSe_(2)/V_(2)C heterocatalyst with built-in electric field for efficient lithium-sulfur batteries:Remedies polysulfide shuttle and conversion kinetics

Lithium sulfur(Li-S)battery is a kind of burgeoning energy storage system with high energy density.However,the electrolyte-soluble intermediate lithium polysulfides(Li PSs)undergo notorious shuttle effect,which seriously hinders the commercialization of Li-S batteries.Herein,a unique VSe_(2)/V_(2)C heterostructure with local built-in electric field was rationally engineered from V_(2)C parent via a facile thermal selenization process.It exquisitely synergizes the strong affinity of V_(2)C with the effective electrocatalytic activity of VSe_(2).More importantly,the local built-in electric field at the heterointerface can sufficiently promote the electron/ion transport ability and eventually boost the conversion kinetics of sulfur species.The Li-S battery equipped with VSe_(2)/V_(2)C-CNTs-PP separator achieved an outstanding initial specific capacity of 1439.1 m A h g^(-1)with a high capacity retention of 73%after 100 cycles at0.1 C.More impressively,a wonderful capacity of 571.6 mA h g^(-1)was effectively maintained after 600cycles at 2 C with a capacity decay rate of 0.07%.Even under a sulfur loading of 4.8 mg cm^(-2),areal capacity still can be up to 5.6 m A h cm^(-2).In-situ Raman tests explicitly illustrate the effectiveness of VSe_(2)/V_(2)C-CNTs modifier in restricting Li PSs shuttle.Combined with density functional theory calculations,the underlying mechanism of VSe_(2)/V_(2)C heterostructure for remedying Li PSs shuttling and conversion kinetics was deciphered.The strategy of constructing VSe_(2)/V_(2)C heterocatalyst in this work proposes a universal protocol to design metal selenide-based separator modifier for Li-S battery.Besides,it opens an efficient avenue for the separator engineering of Li-S batteries.

Modulating charge separation and transfer for high-performance photoelectrodes via built-in electric field

Constructing a built-in electric field has emerged as a key strategy for enhancing charge separation and transfer,thereby improving photoelectrochemical performance.Recently,considerable efforts have been devoted to this endeavor.This review systematically summarizes the impact of built-in electric fields on enhancing charge separation and transfer mechanisms,focusing on the modulation of built-in electric fields in terms of depth and orderliness.First,mechanisms and tuning strategies for built-in electric fields are explored.Then,the state-of-the-art works regarding built-in electric fields for modulating charge separation and transfer are summarized and categorized according to surface and interface depth.Finally,current strategies for constructing bulk built-in electric fields in photoelectrodes are explored,and insights into future developments for enhancing charge separation and transfer in high-performance photoelectrochemical applications are provided.

Interfacial built-in electric field and crosslinking pathways enabling WS_(2)/Ti_(3)C_(2)T_(x) heterojunction with robust sodium storage at low temperature

Developing efficient energy storage for sodium-ion batteries(SIBs)by creating high-performance heterojunctions and understanding their interfacial interaction at the atomic/molecular level holds promise but is also challenging.Besides,sluggish reaction kinetics at low temperatures restrict the operation of SIBs in cold climates.Herein,cross-linking nanoarchitectonics of WS_(2)/Ti_(3)C_(2)T_(x) heterojunction,featuring built-in electric field(BIEF),have been developed,employing as a model to reveal the positive effect of heterojunction design and BIEF for modifying the reaction kinetics and electrochemical activity.Particularly,the theoretical analysis manifests the discrepancy in work functions leads to the electronic flow from the electron-rich Ti_(3)C_(2)T_(x) to layered WS_(2),spontaneously forming the BIEF and“ion reservoir”at the heterogeneous interface.Besides,the generation of cross-linking pathways further promotes the transportation of electrons/ions,which guarantees rapid diffusion kinetics and excellent structure coupling.Consequently,superior sodium storage performance is obtained for the WS_(2)/Ti_(3)C_(2)T_(x) heterojunction,with only 0.2%decay per cycle at 5.0 A g^(-1)(25℃)up to 1000 cycles and a high capacity of 293.5 mA h g^(-1)(0.1A g^(-1)after 100 cycles)even at-20℃.Importantly,the spontaneously formed BIEF,accompanied by“ion reservoir”,in heterojunction provides deep understandings of the correlation between structure fabricated and performance obtained.

Bimetallic selenide heterostructure with directional built-in electricfield confined in N-doped carbon nanofibers for superior sodium storage with ultralong lifespan

Constructing heterostructure is considered as an effective strategy to address the sluggish electronic and ionic kinetics of anode materials for sodium ion batteries(SIBs).However,realizing the orientated growth and uniform distribution of the heterostructure is still a great challenge.Herein,the regulated novel CoSe_(2)/NiSe_(2)heterostructure confined in N-doped carbon nanofibers(CoSe_(2)/NiSe_(2)@N-C)are prepared by using Co/Ni-ZIF template,in which,the CoSe_(2)/NiSe_(2)heterostructures realize uniform distribution on a micro level.Benefiting from the unique heterostructure and N-doped carbon nanofibers,the CoSe_(2)/NiSe_(2)@N-C deliveries superior rate capability and durable cycle lifespan with a reversible capacity of 400.5 mA h g^(-1)after 5000 cycles at 2 A g^(-1).The Na-ion full battery with CoSe_(2)/NiSe_(2)@N-C anode and layered oxide cathode displays a remarkable energy density of 563 W h kg^(-1)with 241.1 W kg^(-1)at 0.1 A g^(-1).The theoretical calculations disclose that the periodic and directional built-in electric-field along with the heterointerfaces of CoSe_(2)/NiSe_(2)@N-C can accelerate electrochemical reaction kinetics.The in(ex)situ experimental measurements reveal the reversible conversion reaction and stable structure of CoSe_(2)/NiSe_(2)@N-C during Na+insertion/extraction.The study highlights the potential ability of precisely controlled heterostructure to stimulate the electrochemical performances of advanced anode for SIBs.

Research on Interpolation Method for Missing Electricity Consumption Data

Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.

Optimizing Household Wastes (Rice, Vegetables, and Fruit) as an Environmentally Friendly Electricity Generator

The high consumption of electricity and issues related to fossil energy have triggered an increase in energy prices and the scarcity of fossil resources.Consequently,many researchers are seeking alternative energy sources.One potential technology,the Microbial Fuel Cell(MFC)based on rice,vegetable,and fruit wastes,can convert chemical energy into electrical energy.This study aims to determine the potency of rice,vegetable,and fruit waste assisted by Cu/Mg electrodes as a generator of electricity.The method used was a laboratory experiment,including the following steps:electrode preparation,waste sample preparation,incubation of the waste samples,construction of a reactor using rice,vegetable,and fruit waste as a source of electricity,and testing.The tests included measuring electrical conductivity,electric current,voltage,current density,and power density.Based on the test results,the maximum current and voltage values for the fruit waste samples were 5.53 V and 11.5 mA,respectively,with a current density of 2.300 mA/cm^(2) and a power density of 12.719 mW/cm^(2).The results indicate the potential for a future development.The next step in development involves determining the optimum conditions for utilizing of rice,vegetable,and fruit waste.The results of the electrical conductivity test on rice,vegetable,and fruit waste samples were 1.51,2.88,and 3.98 mS,respectively,with the highest electrical conductivity value found in the fruit waste sample.

Simulation and Analysis of Cascading Faults in Integrated Heat and Electricity Systems Considering Degradation Characteristics

Cascading faults have been identified as the primary cause of multiple power outages in recent years.With the emergence of integrated energy systems(IES),the conventional approach to analyzing power grid cascading faults is no longer appropriate.A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance.In this study,an innovative analysis method for cascading faults in integrated heat and electricity systems(IHES)is proposed.It considers the degradation characteristics of transmission and energy supply com-ponents in the system to address the impact of component aging on cascading faults.Firstly,degradation models for the current carrying capacity of transmission lines,the water carrying capacity and insulation performance of thermal pipelines,as well as the performance of energy supply equipment during aging,are developed.Secondly,a simulation process for cascading faults in the IHES is proposed.It utilizes an overload-dominated development model to predict the propagation path of cascading faults while also considering network islanding,electric-heating rescheduling,and load shedding.The propagation of cascading faults is reflected in the form of fault chains.Finally,the results of cascading faults under different aging levels are analyzed through numerical examples,thereby verifying the effectiveness and rationality of the proposed model and method.

Electricity as an Energy Vector: A Performance Comparison with Hydrogen and Biodiesel in Italy

This study presents a comparative analysis of electricity, hydrogen, and biodiesel as energy vectors, with a focus on powering an aluminum smelter in southern Italy. It evaluates these vectors in terms of efficiency, land requirements for carbon-neutral energy production, and capital expenditure, providing insights throughout the entire supply chain (upstream, midstream, and downstream) into their feasibility for industrial applications. The research reveals that biodiesel, despite being carbon neutral, is impractical due to extensive land requirements and lower efficiency if compared to other vectors. Hydrogen, downstream explored in two forms as thermal power generation and fuel cell technology, shows lower efficiency and higher capital expenditure compared to electricity. Additionally, green hydrogen production’s land requirements significantly exceed those of electricity-based systems. Electricity emerges as the most viable option, offering an overall higher efficiency, lower land requirements for its green production, and comparatively lower capital expenditure. The study’s findings highlight the importance of a holistic assessment of energy vectors, considering economic, environmental, and practical aspects along the entire energy supply chain, especially in industrial applications where the balance of these factors is crucial for long-term sustainability and feasibility. This comprehensive analysis provides valuable guidance for similar industrial applications, emphasizing the need for a balanced approach in the selection of energy vectors.

基于区间毕达哥拉斯犹豫模糊熵和交叉熵的ELECTRE II决策方法

针对属性权重完全未知或部分已知,属性值为区间毕达哥拉斯犹豫模糊数的多属性决策问题,提出了基于区间毕达哥拉斯犹豫模糊熵和交叉熵的ELECTRE II法。首先给出区间毕达哥拉斯犹豫模糊数的区间形式的得分函数和精度函数,定义新的距离测度。然后基于区间毕达哥拉斯犹豫模糊数的模糊因子、直觉因子和幅度因子,给出熵和交叉熵公式,并证明其性质,提出了基于熵和交叉熵确定属性权重的方法。最后提出了区间毕达哥拉斯犹豫模糊环境下的改进的ELECTRE II法,利用综合优势值对方案进行排序,并通过算例和比较分析验证了该方法的可行性和有效性。

Construction of strong built-in electric field in binary metal sulfide heterojunction to propel high-loading lithium-sulfur batteries

The practical application of lithium-sulfur(Li-S)batteries is greatly hindered by soluble polysulfides shuttling and sluggish sulfur redox kinetics.Rational design of multifunctional hybrid materials with superior electronic conductivity and high electrocatalytic activity,e.g.,heterostructures,is a promising strategy to solve the above obstacles.Herein,a binary metal sulfide MnS-MoS_(2) heterojunction electrocatalyst is first designed for the construction of high-sulfur-loaded and durable Li-S batteries.The MnS-MoS_(2) p-n heterojunction shows a unique structure of MoS_(2) nanosheets decorated with ample MnS nanodots,which contributes to the formation of a strong built-in electric field at the two-phase interface.The MnS-MoS_(2) hybrid host shows strong soluble polysulfide affinity,enhanced electronic conductivity,and exceptional catalytic effect on sulfur reduction.Benefiting from the synergistic effect,the as-derived S/MnS-MoS_(2) cathode delivers a superb rate capability(643 m A h g^(-1)at 6 C)and a durable cyclability(0.048%decay per cycle over 1000 cycles).More impressively,an areal capacity of 9.9 m A h cm^(-2)can be achieved even under an extremely high sulfur loading of 14.7 mg cm^(-2)and a low electrolyte to sulfur ratio of 2.9μL mg^(-1).This work provides an in-depth understanding of the interfacial catalytic effect of binary metal compound heterojunctions on sulfur reaction kinetics.

Optimally arranged TiO_(2)@MoS_(2) heterostructures with effectively induced built-in electric field for high-performance lithium-sulfur batteries

To overcome the serious technological issues affecting lithium-sulfur(Li-S) batteries,such as sluggish sulfur redox kinetics and the detrimental shuttle effect,heterostructure engineering has been investigated as a strategy to effectively capture soluble lithium polysulfide intermediates and promote their conversion reaction by integrating highly polar metal oxides with catalytically active metals sulfides.However,to fully exploit the outstanding properties of heterostructure-based composites,their detailed structure and interfacial contacts should be designed rationally.Herein,optimally arranged TiO_(2)and MoS_(2)-based heterostructures(TiO_(2)@MoS_(2)) are fabricated on carbon cloth as a multifunctional interlayer to efficiently trap polysulfide intermediates and accelerate their redox kinetics.Owing to the synergistic effects between TiO_(2)and MoS_(2)and the uniform heterointerface distribution that induces the ideally oriented built-in electric field,Li-S batteries with TiO_(2)@MoS_(2)interlayers exhibit high rate capability(601 mA h g^(-1)at 5 C),good cycling stability(capacity-fade rate of 0.067% per cycle over 500 cycles at2 C),and satisfactory areal capacity(5.2 mA h cm^(-2)) under an increased sulfur loading of 5.2 mg cm^(-2).Moreover,by comparing with a MoS_(2)@TiO_(2)interlayer composed of reversely arranged heterostructures,the effect of the built-in electric field’s direction on the electrocatalytic reactions of polysulfide intermediates is thoroughly investigated for the first time.The superior electrocatalytic activities of the rationally arranged TiO_(2)@MoS_(2)interlayer demonstrate the importance of optimizing the built-in electric field of heterostructures for producing high-performance Li-S batteries.

A Review of Atmospheric Electricity Research in China from 2019 to 2022

Atmospheric electricity is composed of a series of electric phenomena in the atmosphere.Significant advances in atmospheric electricity research conducted in China have been achieved in recent years.In this paper,the research progress on atmospheric electricity achieved in China during 2019-22 is reviewed focusing on the following aspects:(1)lightning detection and location techniques,(2)thunderstorm electricity,(3)lightning forecasting methods and techniques,(4)physical processes of lightning discharge,(5)high energy emissions and effects of thunderstorms on the upper atmosphere,and(6)the effect of aerosol on lightning.

面向RISC-V适配开发的x86 built-in函数转换方法

新兴架构RISC-V的生态建设需要将其他架构函数或软件包向RISC-V架构迁移适配。在研究GCC特定架构适配的built-in函数向RISC-V架构迁移时,提出一套x86到RISC-V的built-in函数转换方法,对于非扩展指令集(属非向量类型)built-in函数,采用RISC-V架构下相同功能的built-in或标准库函数替代;对于SSE扩展指令集built-in函数,建立数据类型映射和向量函数操作映射实现向RISC-V架构向量扩展函数或标准库函数的迁移,其中RVV函数迁移方式占比67%。实验结果表明:方法迁移的程序功能正确,方法有效。本文方法对其他扩展指令集built-in函数的迁移提供了指导,且与现有工作相比,更易扩展、覆盖面更广。

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

The transaction behavior of cryptocurrency and electricity consumption

Rapidly increasing cryptocurrency prices have encouraged cryptocurrency miners to participate in cryptocurrency production,increasing network hashrates and electricity consumption.Growth in network hashrates has further crowded out small cryptocurrency investors owing to the heightened costs of mining hardware and electricity.These changes prompt cryptocurrency miners to become new investors,leading to cryptocurrency price increases.The potential bidirectional relationship between cryptocurrency price and electricity consumption remains unidentified.Hence,this research thus utilizes July 312015–July 122019 data from 13 cryptocurrencies to investigate the short-and long-run causal effects between cryptocurrency transaction and electricity consumption.Particularly,we consider structural breaks induced by external shocks through stationary analysis and comovement relationships.Over the examined time period,we found that the series of cryptocurrency transaction and electricity consumption gradually returns to mean convergence after undergoing daily shocks,with prices trending together with hashrates.Transaction fluctuations exert both a temporary effect and permanent influence on electricity consumption.Therefore,owing to the computational power deployed to wherever high profit is found,transactions are vital determinants of electricity consumption.

Optimization of Electricity Purchase and Sales Strategies of Electricity Retailers under the Condition of Limited Clean Energy Consumption

In the process of my country’s energy transition,the clean energy of hydropower,wind power and photovoltaic power generation has ushered in great development,but due to the randomness and volatility of its output,it has caused a certain waste of clean energy power generation resources.Regarding the purchase and sale of electricity by electricity retailers under the condition of limited clean energy consumption,this paper establishes a quantitative model of clean energy restricted electricity fromthe perspective of power system supply and demand balance.Then it analyzes the source-charge dual uncertain factors in the electricity retailer purchasing and selling scenarios in the mid-to long-term electricity market and the day-ahead market.Through the multi-scenario analysis method,the uncertain clean energy consumption and the user’s power demand are combined to form the electricity retailer’s electricity purchase and sales scene,and the typical scene is obtained by using the hierarchical clustering algorithm.This paper establishes a electricity retailer’s risk decisionmodel for purchasing and selling electricity in themid-and long-term market and reduce-abandonment market,and takes the maximum profit expectation of the electricity retailer frompurchasing and selling electricity as the objective function.At the same time,in themediumand longterm electricity market and the day-ahead market,the electricity retailer’s purchase cost,electricity sales income,deviation assessment cost and electricity purchase and sale risk are considered.The molecular results show that electricity retailers can obtain considerable profits in the reduce-abandonment market by optimizing their own electricity purchase and sales strategies,on the premise of balancing profits and risks.

Ferroelectricity induced by the absorption of water molecules on double helix SnIP

We study the ferroelectricity in a one-dimensional(1D)system composed of a double helix SnIP with absorbing water molecules.Our ab initio calculations reveal two factors that are critical to the electrical polarization.The first one is the orientation of polarized water molecules staying in the R2 region of SnIP.The second one is the displacement of I atom which roots from subtle interaction with absorbed water molecules.A reasonable scenario of polarization flipping is proposed in this study.In the scenario,the water molecule is rolling-up with keeping the magnitude of its electrical dipole and changing its direction,meanwhile,the displacement of I atoms is also reversed.Highly tunable polarization can be achieved by applying strain,with 26.5%of polarization enhancement by applying tensile strain,with only 4%degradation is observed with 4%compressive strain.Finally,the direct band gap is also found to be correlated with strain.

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud center.This provides a credible data source for enterprise credit scoring,which is one of the most vital elements during the financial decision-making process.Accordingly,this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data.Instead of predicting the credit rating,our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net(rankXGB).To boost the performance,the rankXGB model combines several weak ranking models into a strong model.Due to the high computational cost and the vast amounts of data,we design an edge computing framework to reduce the latency of enterprise credit evaluation.Specially,we design a two-stage deep learning task architecture,including a cloud-based weak credit ranking and an edge-based credit score calculation.In the first stage,we send the electricity consumption data of the evaluated enterprise to the computing cloud server,where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results.In the second stage,the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result,which is used to calculate an absolute credit score by score normalization.The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).