Low-temperature characteristicsof rubbers and performance testsof type 120 emergencyvalve diaphragms

Purpose–The type 120 emergency valve is an essential braking component of railway freight trains,butcorresponding diaphragms consisting of natural rubber(NR)and chloroprene rubber(CR)exhibit insufficientaging resistance and low-temperature resistance,respectively.In order to develop type 120 emergency valverubber diaphragms with long-life and high-performance,low-temperatureresistant CR and NR were processed.Design/methodology/approach–The physical properties of the low-temperature-resistant CR and NRwere tested by low-temperature stretching,dynamic mechanical analysis,differential scanning calorimetryand thermogravimetric analysis.Single-valve and single-vehicle tests of type 120 emergency valves werecarried out for emergency diaphragms consisting of NR and CR.Findings–The low-temperature-resistant CR and NR exhibited excellent physical properties.The elasticityand low-temperature resistance of NR were superior to those of CR,whereas the mechanical properties of thetwo rubbers were similar in the temperature range of 0℃–150℃.The NR and CR emergency diaphragms metthe requirements of the single-valve test.In the low-temperature single-vehicle test,only the low-temperaturesensitivity test of the NR emergency diaphragm met the requirements.Originality/value–The innovation of this study is that it provides valuable data and experience for futuredevelopment of type 120 valve rubber diaphragms.

Boosting oxygen reduction activity and CO_(2) resistance on bismuth ferrite-based perovskite cathode for low-temperature solid oxide fuel cells below 600℃

Developing efficient and stable cathodes for low-temperature solid oxide fuel cells(LT-SOFCs) is of great importance for the practical commercialization.Herein,we propose a series of Sm-modified Bi_(0.7-x)Sm_xSr_(0.3)FeO_(3-δ) perovskites as highly-active catalysts for LT-SOFCs.Sm doping can significantly enhance the electrocata lytic activity and chemical stability of cathode.At 600℃,Bi_(0.675)Sm_(0.025)Sr_(0.3)FeO_(3-δ)(BSSF25) cathode has been found to be the optimum composition with a polarization resistance of 0.098 Ω cm^2,which is only around 22.8% of Bi_(0.7)Sr_(0.3)FeO_(3-δ)(BSF).A full cell utilizing BSSF25 displays an exceptional output density of 790 mW cm^(-2),which can operate continuously over100 h without obvious degradation.The remarkable electrochemical performance observed can be attributed to the improved O_(2) transport kinetics,superior surface oxygen adsorption capacity,as well as O_(2)p band centers in close proximity to the Fermi level.Moreover,larger average bonding energy(ABE) and the presence of highly acidic Bi,Sm,and Fe ions restrict the adsorption of CO_(2) on the cathode surface,resulting in excellent CO_(2) resistivity.This work provides valuable guidance for systematic design of efficient and durable catalysts for LT-SOFCs.

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.

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.

Structural Engineering of Anode Materials for Low-Temperature Lithium-Ion Batteries:Mechanisms,Strategies,and Prospects

The severe degradation of electrochemical performance for lithium-ion batteries(LIBs)at low temperatures poses a significant challenge to their practical applications.Consequently,extensive efforts have been contributed to explore novel anode materials with high electronic conductivity and rapid Li^(+)diffusion kinetics for achieving favorable low-temperature performance of LIBs.Herein,we try to review the recent reports on the synthesis and characterizations of low-temperature anode materials.First,we summarize the underlying mechanisms responsible for the performance degradation of anode materials at subzero temperatures.Second,detailed discussions concerning the key pathways(boosting electronic conductivity,enhancing Li^(+)diffusion kinetics,and inhibiting lithium dendrite)for improving the low-temperature performance of anode materials are presented.Third,several commonly used low-temperature anode materials are briefly introduced.Fourth,recent progress in the engineering of these low-temperature anode materials is summarized in terms of structural design,morphology control,surface&interface modifications,and multiphase materials.Finally,the challenges that remain to be solved in the field of low-temperature anode materials are discussed.This review was organized to offer valuable insights and guidance for next-generation LIBs with excellent low-temperature electrochemical performance.

Advances in sodium-ion batteries at low-temperature: Challenges and strategies

With the continuing boost in the demand for energy storage,there is an increasing requirement for batteries to be capable of operation in extreme environmental conditions.Sodium-ion batteries(SIBs) have emerged as a highly promising energy storage solution due to their promising performance over a wide range of temperatures and the abundance of sodium resources in the earth's crust.Compared to lithiumion batteries(LIBs),although sodium ions possess a larger ionic radius,they are more easily desolvated than lithium ions.Fu rthermore,SIBs have a smaller Stokes radius than lithium ions,resulting in improved sodium-ion mobility in the electrolyte.Nevertheless,SIBs demonstrate a significant decrease in performance at low temperatures(LT),which constrains their operation in harsh weather conditions.Despite the increasing interest in SIBs,there is a notable scarcity of research focusing specifically on their mechanism under LT conditions.This review explores recent research that considers the thermal tolerance of SIBs from an inner chemistry process perspective,spanning a wide temperature spectrum(-70 to100℃),particularly at LT conditions.In addition,the enhancement of electrochemical performance in LT SIBs is based on improvements in reaction kinetics and cycling stability achieved through the utilization of effective electrode materials and electrolyte components.Furthermore,the safety concerns associated with SIBs are addressed and effective strategies are proposed for mitigating these issues.Finally,prospects conducted to extend the environmental frontiers of commercial SIBs are discussed mainly from three viewpoints including innovations in materials,development and research of relevant theoretical mechanisms,and intelligent safety management system establishment for larger-scale energy storage SIBs.

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.

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.

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.

Temperature inversion enables superior stability for low-temperature Zn-ion batteries

It is challenging for aqueous Zn-ion batteries(ZIBs)to achieve comparable low-temperature(low-T)performance due to the easy-frozen electrolyte and severe Zn dendrites.Herein,an aqueous electrolyte with a low freezing point and high ionic conductivity is proposed.Combined with molecular dynamics simulation and multi-scale interface analysis(time of flight secondary ion mass spectrometry threedimensional mapping and in-situ electrochemical impedance spectroscopy method),the temperature independence of the V_(2)O_(5)cathode and Zn anode is observed to be opposite.Surprisingly,dominated by the solvent structure of the designed electrolyte at low temperatures,vanadium dissolution/shuttle is significantly inhibited,and the zinc dendrites caused by this electrochemical crosstalk are greatly relieved,thus showing an abnormal temperature inversion effect.Through the disclosure and improvement of the above phenomena,the designed Zn||V_(2)O_(5)full cell delivers superior low-T performance,maintaining almost 99%capacity retention after 9500 cycles(working more than 2500 h)at-20°C.This work proposes a kind of electrolyte suitable for low-T ZIBs and reveals the inverse temperature dependence of the Zn anode,which might offer a novel perspective for the investigation of low-T aqueous battery systems.

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.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Enhancing performance of low-temperature processed CsPbI2Br all-inorganic perovskite solar cells using polyethylene oxide-modified TiO_(2)

CsPbX_(3)-based(X=I,Br,Cl)inorganic perovskite solar cells(PSCs)prepared by low-temperature process have attracted much attention because of their low cost and excellent thermal stability.However,the high trap state density and serious charge recombination between low-temperature processed TiO_(2)film and inorganic perovskite layer interface seriously restrict the performance of all-inorganic PSCs.Here a thin polyethylene oxide(PEO)layer is employed to modify TiO_(2)film to passivate traps and promote carrier collection.The impacts of PEO layer on microstructure and photoelectric characteristics of TiO_(2)film and related devices are systematically studied.Characterization results suggest that PEO modification can reduce the surface roughness of TiO_(2)film,decrease its average surface potential,and passivate trap states.At optimal conditions,the champion efficiency of CsPbI_(2)Br PSCs with PEO-modified TiO_(2)(PEO-PSCs)has been improved to 11.24%from 9.03%of reference PSCs.Moreover,the hysteresis behavior and charge recombination have been suppressed in PEO-PSCs.

Ca and Sr co-doping induced oxygen vacancies in 3DOM La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts for boosting low-temperature oxidative coupling of methane

It is urgent to develop catalysts with application potential for oxidative coupling of methane(OCM)at relatively lower temperature.Herein,three-dimensional ordered macro porous(3 DOM)La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)(A_(2)B_(2)O_(7)-type)catalysts with disordered defective cubic fluorite phased structure were successfully prepared by a colloidal crystal template method.3DOM structure promotes the accessibility of the gaseous reactants(O2and CH4)to the active sites.The co-doping of Ca and Sr ions in La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts improved the formation of oxygen vacancies,thereby leading to increased density of surface-active oxygen species(O_(2)^(-))for the activation of CH4and the formation of C2products(C2H6and C2H4).3DOM La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts exhibit high catalytic activity for OCM at low temperature.3DOM La1.7Sr0.3Ce1.7Ca0.3O7-δcatalyst with the highest density of O_(2)^(-)species exhibited the highest catalytic activity for low-temperature OCM,i.e.,its CH4conversion,selectivity and yield of C2products at 650℃are 32.2%,66.1%and 21.3%,respectively.The mechanism was proposed that the increase in surface oxygen vacancies induced by the co-doping of Ca and Sr ions boosts the key step of C-H bond breaking and C-C bond coupling in catalyzing low-temperature OCM.It is meaningful for the development of the low-temperature and high-efficient catalysts for OCM reaction in practical application.

Numerical and theoretical study of large-scale failure of strata overlying sublevel caving mines with steeply dipping discontinuities

The deformation and fracture evolution mechanisms of the strata overlying mines mined using sublevel caving were studied via numerical simulations.Moreover,an expression for the normal force acting on the side face of a steeply dipping superimposed cantilever beam in the surrounding rock was deduced based on limit equilibrium theory.The results show the following:(1)surface displacement above metal mines with steeply dipping discontinuities shows significant step characteristics,and(2)the behavior of the strata as they fail exhibits superimposition characteristics.Generally,failure first occurs in certain superimposed strata slightly far from the goaf.Subsequently,with the constant downward excavation of the orebody,the superimposed strata become damaged both upwards away from and downwards toward the goaf.This process continues until the deep part of the steeply dipping superimposed strata forms a large-scale deep fracture plane that connects with the goaf.The deep fracture plane generally makes an angle of 12°-20°with the normal to the steeply dipping discontinuities.The effect of the constant outward transfer of strata movement due to the constant outward failure of the superimposed strata in the metal mines with steeply dipping discontinuities causes the scope of the strata movement in these mines to be larger than expected.The strata in the metal mines with steeply dipping discontinuities mainly show flexural toppling failure.However,the steeply dipping structural strata near the goaf mainly exhibit shear slipping failure,in which case the mechanical model used to describe them can be simplified by treating them as steeply dipping superimposed cantilever beams.By taking the steeply dipping superimposed cantilever beam that first experiences failure as the key stratum,the failure scope of the strata(and criteria for the stability of metal mines with steeply dipping discontinuities mined using sublevel caving)can be obtained via iterative computations from the key stratum,moving downward toward and upwards away from the goaf.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

Low-Temperature Soft-Cover-Assisted Hydrolysis Deposition of Large-Scale TiO_2 Layer for Efficient Perovskite Solar Modules

Perovskite solar cells with TiO_2 electron transport layers exhibit power conversion efficiency(PCE) as high as 22.7% in single cells. However, the preparation process of the TiO_2 layer is adopted by an unscalable method or requires high-temperature sintering, which precludes its potential use for mass production of flexible devices. In this study, a scalable low-temperature softcover-assisted hydrolysis(SAH) method is presented,where the precursor solution is sandwiched between a soft cover and preheated substrate to form a closed hydrolysis environment. Compact homogeneous TiO_2 films with a needle-like structure were obtained after the hydrolysis of a TiCl_4 aqueous solution. Moreover, by careful optimization of the TiO_2 fabrication conditions, a high PCE of 14.01% could be achieved for a solar module(4 × 4 cm^2) prepared using the SAH method. This method provides a novel approach for the efficient scale-up of the low-temperature TiO_2 film growth for industrial applications.

Low-temperature growth of large-scale,single-crystalline graphene on Ir(111)

Iridium is a promising substrate for self-limiting growth of graphene. However, single-crystalline graphene can only be fabricated over 1120 K. The weak interaction between graphene and Ir makes it challenging to grow graphene with a single orientation at a relatively low temperature. Here, we report the growth of large-scale, single-crystalline graphene on Ir(111) substrate at a temperature as low as 800 K using an oxygen-etching assisted epitaxial growth method. We firstly grow polycrystalline graphene on Ir. The subsequent exposure of oxygen leads to etching of the misaligned domains.Additional growth cycle, in which the leftover aligned domain serves as a nucleation center, results in a large-scale and single-crystalline graphene layer on Ir(111). Low-energy electron diffraction, scanning tunneling microscopy, and Raman spectroscopy experiments confirm the successful growth of large-scale and single-crystalline graphene. In addition, the fabricated single-crystalline graphene is transferred onto a SiO_2/Si substrate. Transport measurements on the transferred graphene show a carrier mobility of about 3300 cm^2·V^(-1)·s^(-1). This work provides a way for the synthesis of large-scale,high-quality graphene on weak-coupled metal substrates.

Gleaning insights from German energy transition and large-scale underground energy storage for China’s carbon neutrality

The global energy transition is a widespread phenomenon that requires international exchange of experiences and mutual learning.Germany’s success in its first phase of energy transition can be attributed to its adoption of smart energy technology and implementation of electricity futures and spot marketization,which enabled the achievement of multiple energy spatial–temporal complementarities and overall grid balance through energy conversion and reconversion technologies.While China can draw from Germany’s experience to inform its own energy transition efforts,its 11-fold higher annual electricity consumption requires a distinct approach.We recommend a clean energy system based on smart sector coupling(ENSYSCO)as a suitable pathway for achieving sustainable energy in China,given that renewable energy is expected to guarantee 85%of China’s energy production by 2060,requiring significant future electricity storage capacity.Nonetheless,renewable energy storage remains a significant challenge.We propose four large-scale underground energy storage methods based on ENSYSCO to address this challenge,while considering China’s national conditions.These proposals have culminated in pilot projects for large-scale underground energy storage in China,which we believe is a necessary choice for achieving carbon neutrality in China and enabling efficient and safe grid integration of renewable energy within the framework of ENSYSCO.