Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Perspective on low-temperature electrolytes for LiFePO 4-based lithium-ion batteries

The olivine-type lithium iron phosphate(LiFePO_(4))cathode material is promising and widely used as a high-performance lithium-ion battery cathode material in commercial batteries due to its low cost,environmental friendliness,and high safety.At present,LiFePO_(4)/C sec-ondary batteries are widely used for electronic products,automotive power batteries,and other occasion-related applications with good thermal stability,stable cycle performance,and low room-temperature self-discharge rate.However,LiFePO_(4)-based battery applications are seriously limited when they are operated in a cold climate.This outcome is due to a considerable decrease in Li+transport capabilities within the elec-trode,particularly leading to a dramatic decrease in the electrochemical capacity and power performance of the electrolyte.Therefore,the design of low-temperature electrolytes is important for the further commercial application of LiFePO_(4) batteries.This paper reviews the key factors for the poor low-temperature performance of LiFePO_(4)-based batteries and the research progress of low-temperature electrolytes.Spe-cial attention is paid to electrolyte components,including lithium salts,cosolvents,additives,and the development of new electrolytes.The factors affecting the anode are also analyzed.Finally,according to the current research progress,some viewpoints are summarized to provide suitable modification methods and research suggestions for improving the practicability of LiFePO_(4)/C commercial batteries at low temperat-ures in the future.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

Optimization of decoupling combustion characteristics of coal briquettes and biomass pellets in household stoves

Burning coal briquettes or biomass pellets in household decoupling stoves is of significance to the reduction of residential pollutant emissions such as NO and CO. In order to make full use of the superiority of decoupling combustion technology, the household stoves should be specially designed and optimized to adapt to fuel types and combustion characteristics. Using numerical simulation and experimental validation, this study quantitatively clarified that the reducibility of devolatilization char plays an important role in the suppression of NO emission in the decoupling combustion of coal, while the reducibility of pyrolysis gases has a dominant effect on the reduction of NO in the decoupling combustion of biomass. An optimal parameter combination of throat height and grate angle was obtained for the simultaneous suppression of NO and CO emissions in the household decoupling stove burning coal briquettes. Two types of decoupling stoves were developed to enable the clean combustion of biomass pellets. The A-type biomass stove with a multi-pass smoke tunnel shows a better comprehensive NO and CO reduction effectiveness than the B-type biomass stove consisting of a two-stage grate structure and an S-shaped pyrolysis chamber. The optimal structural parameters provided references for the design and manufacture of commercial decoupling coal and biomass stoves.

二次电池研究进展

能源的存储和利用是当今科学和技术发展中的重大课题之一,尤其是作为高效的电能/化学能转化装置的二次电池相关技术一直是科学家研究的热点领域。在此背景下,本文较为系统地介绍目前二次电池的重要研究进展,将从二次电池的发展历史引入,再到其相关的基础理论知识的介绍。随后较为详细地讨论当前不同体系的二次电池及相关应的关键材料的研究进展,涉及到锂离子电池、钠离子电池、钾离子电池、镁离子电池、锌离子电池、钙离子电池、铝离子电池、氟离子电池、氯离子电池、双离子电池、锂-硫(硒)电池、钠-硫(硒)电池、钾-硫(硒)电池、多价金属-硫基电池、锂-氧电池、钠-氧电池、钾-氧电池、多价金属-氧气电池、锂-溴(碘)电池、水系金属离子电池、光辅助电池、柔性电池、有机电池、金属-二氧化碳电池等。此外,也介绍了电池研究中常见的电极反应过程表征技术,包括冷冻电镜、透射电镜、同步辐射、原位谱学表征、磁性表征等。本文将有助于研究人员对二次电池进行全面系统的了解与把握,并为之后二次电池的研究提供很好的指导作用。

高管激励、商业战略与企业所得税规避程度关系研究

企业管理者的激励报酬对企业避税行为具有重大影响,管理者在进行避税决策时也会受到企业采用的商业战略的影响。本文研究了高管激励与企业所得税规避程度之间的关系以及不同商业战略模式下二者的关系。研究发现,货币激励会在短期内促进企业所得税的避税,而由于避税成本的存在,在长期范围里,股权激励与企业所得税规避呈负相关关系。研究还发现,采用探索者战略的企业更倾向于规避企业所得税,而采用防御者战略的企业的税避税行为更消极。

细菌纤维素气凝胶ARGET ATRP接枝聚甲基丙烯酸缩水甘油酯

以含溴的细菌纤维素气凝胶为引发剂,维生素C(Vc)为还原剂,N,N,N',N,'N″-五甲基二亚乙基三胺(PMDETA)/溴化铜(Cu Br2)为催化剂,通过电子转移活化再生催化剂原子转移自由基聚合(ARGET ATRP)方法,制备了含聚甲基丙烯酸缩水甘油酯(PGMA)刷的细菌纤维素气凝胶规整接枝共聚物。采用能谱对大分子引发剂元素含量进行测定,并利用红外光谱、X射线衍射、扫描电镜、热失重分析对其结构和性能进行表征。结果表明,接枝聚合反应可控,大分子引发剂Br元素含量为6.77%;GMA接枝聚合后纤维直径增大且在纤维表面形成覆盖层;起始热分解温度由205℃升高到246℃,热稳定性增强。

Surface runoff processes and sustainable utilization of water resources in Manas River Basin, Xinjiang, China

Water is the important resource to guarantee the existence and development of oases in arid areas. To improve the utilization efficiency of water resources in Manas River Basin, this paper investigated the trends and periods of runoff based on the runoff and climate data for the past 50 years. Subsequently, with the socioeconomic and water resources data, we studied a comprehensive evaluation on the water security in this area. The results indicated that the stream flows in the three hydrological stations of Hongshanzui, Kensiwat and Bajiahu have sig- nificantly increased and undergone abrupt changes, with periods of 18 and 20 years. According to assessment, water security in the Manas River Basin was at an unsafe level in 2008. In criterion layer, the ecological security index and the index of supply-demand situation are both at the relatively secure level; the quantity index and so- cioeconomic index of water resources are at the unsafe level and basic security level, respectively. Therefore, in order to achieve sustainable economic and social development within the Manas River Basin, it is vital to take a series of effective measures to improve the status of water security.

Nonlinear health evaluation for lithium-ion battery within full-lifespan

Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.

Designer uniform Li plating/stripping through lithium–cobalt alloying hierarchical scaffolds for scalable high-performance lithium-metal anodes

Lithium metal anodes are of great interest for advanced high-energy density batteries such as lithiumair, lithium-sulfur and solid-state batteries, due to their low electrode potential and ultra-high theoretical capacity. There are, however, several challenges limiting their practical applications, which include low coulombic efficiency, the uncontrollable growth of dendrites and poor rate capability. Here, a rational design of 3D structured lithium metal anodes comprising of in-situ growth of cobalt-decorated nitrogen-doped carbon nanotubes on continuous carbon nanofibers is demonstrated via electrospinning.The porous and free-standing scaffold can enhance the tolerance to stresses resulting from the intrinsic volume change during Li plating/stripping, delivering a significant boost in both charge/discharge rates and stable cycling performance. A binary Co-Li alloying phase was generated at the initial discharge process, creating more active sites for the Li nucleation and uniform deposition. Characterization and density functional theory calculations show that the conductive and uniformly distributed cobalt-decorated carbon nanotubes with hierarchical structure can effectively reduce the local current density and more easily absorb Li atoms, leading to more uniform Li nucleation during plating. The current work presents an advance on scalable and cost-effective strategies for novel electrode materials with 3D hierarchical microstructures and mechanical flexibility for lithium metal anodes.

EMMS-based modeling of gas-solid generalized fluidization:Towards a unified phase diagram

Hydrodynamic features of gas-solid generalized fluidization can be well expressed in the form of phase diagrams,which are important for engineering design.Mesoscale structure presents almost universally in generalized fluidization and should be considered in such phase diagrams.However,current phase diagrams were mainly proposed for cocurrent upward flow according to experimental data or empirical correlations with homogeneous assumption.The energy-minimization multiscale(EMMS)model has shown the capability of capturing mesoscale structure in generalized fluidization,so EMMS-based phase diagrams of generalized fluidization were proposed in this article,which describe more reasonable global hydrodynamics over all regimes including the important engineering phenomena of choking and flooding.These characteristics were also found in discrete particle simulation under various conditions.For wider range of application,the typical hydrodynamic parameters of the phase diagrams were correlated to non-dimensional numbers reflecting the effects of material properties and operation conditions.This study thus shows a possible route to develop a unified phase diagram in the future.

Binary outcome variables and logistic regression models

Biomedical researchers often study binary variables that indicate whether or not a specific event,such as remission of depression symptoms,occurs during the study period.The indicator variable Y takes two values,usually coded as one if the event (remission) is present and zero if the event is not present(non-remission).Let p be the probability that the event occurs ( Y =1),then 1-p will be the probability that the event does not occur ( Y =0).

Hot compressive deformation of eutectic Al-17at%Cu alloy on the interface of the Cu-Al composite plate produced by horizontal continuous casting

On the interface of the Cu-Al composite plate from horizontal continuous casting,the eutectic microstructure layer thickness ac-counts for more than 90%of the total interface thickness,and the deformation in rolling forming plays an important role in the quality of the composite plate.The eutectic microstructure material on the interface of the Cu-Al composite plate was prepared by changing the cooling rate of ingot solidification and the deformation in hot compression was investigated.The results show that when the deformation temperature is over 300℃,the softening effect of dynamic recrystallization ofα-Al is greater than the hardening effect,and uniform plastic deformation of eutectic microstructure is caused.The constitutive equation of flow stress in the eutectic microstructure layer was established by Arrhenius hy-perbolic-sine mathematics model,providing a reliable theoretical basis for the deformation of the Cu-Al composite plate.

Enabling dendrite-free charging for lithium batteries based on transport-reaction competition mechanism in CHAIN framework

Worldwide trends in mobile electrification will skyrocket demands for lithium-based battery production,driven by the popularity of electric vehicles.However,both lithium metal batteries and lithium ion batteries face severe safety issues due to dendrite nucleation and growth process.Li deposition is significantly influenced by interfacial factors and charging conditions.In this paper,an electrochemical model considering the internal and external factors is proposed based on Monte Carlo method.The influence of internal solid electrolyte interphase(SEI)porosity,thickness and the external conditions on dendrite growth process is systematically described.The simulation results support that the three factors investigated in this model could synergistically regulate the dendrite growth process.Three competition mechanisms are proposed to tailor lithium deposition for Li-based batteries and numerical solutions for variation pattern of dendrite growth with time are fitted.A three-step process describing kinetic process of lithium deposition is proposed.To achieve dendrite-free charging process,charging strategies and emerging materials design should be considered,including physicochemical materials engineering,artificial SEI,and design for dynamic safety boundary.This work could contribute to the foundation for insights of Li deposition mechanism,which is promising to provide guidelines for next-generation high-energy-density and safe batteries in CHAIN framework.

Highly Aligned Ultra-Thick Gel-Based Cathodes Unlocking Ultra-High Energy Density Batteries

Increasing electrode thickness can substantially enhance the specific energy of lithium-ion batteries;however,ionic transport,electronic conductivity,and ink rheology are current barriers to adoption.Here,a novel approach using a mixed xanthan gum and locust bean gum binder to construct ultrathick electrodes is proposed to address above issues.After combining aqueous binder with single-walled carbon nanotubes(SWCNT),active material(LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)) and subsequent vacuum freeze-drying,highly aligned,and low-tortuosity structures with a porosity of ca.50%can be achieved with an average pore size of 10μm,whereby the gum binder-SWCNT-NMC811 forms vertical structures supported by tissue-like binder/SWCNT networks allowing for excellent electronic conducting phase percolation.As a result,ultra-thick electrodes with a mass loading of about 511 mg cm^(−2) and 99.5 wt%active materials have been demonstrated with a remarkable areal capacity of 79.3 mAh cm^(−2),which is the highest value reported so far.This represents a>25×improvement compared with conventional electrodes with an areal capacity of about 3 mAh cm^(−2).This route also can be expanded to other electrode materials,such as LiFePO_(4) and Li_(4)Ti_(5)O_(12),and thus opens the possibility for low-cost and sustainable ultra-thick electrodes with increased specific energy for future lithium-ion batteries.

Model-guided design of a high performance and durability Ni nanofiber/ceria matrix solid oxide fuel cell electrode

Mixed ionic electronic conductors(MIECs)have attracted increasing attention as anode materials for solid oxide fuel cells(SOFCs)and they hold great promise for lowering the operation temperature of SOFCs.However,there has been a lack of understanding of the performance-limiting factors and guidelines for rational design of composite metal-MIEC electrodes.Using a newly-developed approach based on 3 D-tomography and electrochemical impedance spectroscopy,here for the first time we quantify the contribution of the dual-phase boundary(DPB)relative to the three-phase boundary(TPB)reaction pathway on real MIEC electrodes.A new design strategy is developed for Ni/gadolinium doped ceria(CGO)electrodes(a typical MIEC electrode)based on the quantitative analyses and a novel Ni/CGO fiber-matrix structure is proposed and fabricated by combining electrospinning and tape-casting methods using commercial powders.With only 11.5 vol%nickel,the designer Ni/CGO fiber-matrix electrode shows 32%and 67%lower polarization resistance than a nano-Ni impregnated CGO scaffold electrode and conventional cermet electrode respectively.The results in this paper demonstrate quantitatively using real electrode structures that enhancing DPB and hydrogen kinetics are more efficient strategies to enhance electrode performance than simply increasing TPB.

Atomic/nano-scale in-situ probing the shuttling effect of redox mediator in Na-O_(2) batteries

Sodium-oxygen batteries(Na-O_(2))have attracted extensive attention as promising energy storage systems due to their high energy density and low cost.Redox mediators are often employed to improve Na-O_(2) battery performance,however,their effect on the formation mechanism of the oxygen reduction product(NaO_(2))is still unclear.Here,we have investigated the formation mechanism of NaO_(2) during the discharge process in the presence of a redox mediator with the help of atomic/nano-scale in-situ characterization tools used in concert(e.g.atomic force microscope,electrochemical quartz crystal microbalance(EQCM)and laser nano-particle analyzer).As a result,real-time observations on different time scales show that by shuttling electrons to the electrolyte,the redox mediator enables formation of NaO_(2) in the solution-phase instead of within a finite region near the electrode surface.These findings provide new fundamental insights on the understanding of Na-O_(2) batteries and new consequently perspectives on designing high performance metal-O_(2) batteries and other related functions.

The Development and Applications of Collagen-Based Biomedical Materials and Its Relevant Theory

In this paper,the structure and properties of collagen,the rationale of collagen modification,the isoelectric point theory of collagen and its aggregation,and the multi-level collagen aggregates and its level division methods,etc,are clarified systematically.Meanwhile,the development progress of colla-

基于粒子图像测速技术的快速流态化颗粒速度分布函数分析

量化非均匀气固流动特征对流化床反应器的设计和优化具有重要意义。反应器内颗粒团聚物的形成和演化使得系统远离平衡状态,从而导致颗粒速度分布呈现非麦克斯韦分布特征。本工作利用自适应粒子图像测速(PIV)、粒子追踪测速技术(PTV)及数字图像处理(DIA)相结合的实验研究方法,对快速流化床中的颗粒运动特性和速度分布函数进行统计分析。研究发现,无论在沿重力方向还是水平方向,颗粒速度分布函数均明显偏离麦克斯韦分布,而呈现偏峰长拖尾或双峰分布特征。使用双峰分布函数可以较好地拟合不同实验条件下的颗粒速度分布。进一步探讨了颗粒速度分布函数的各向异性和非麦克斯韦分布的影响因素,发现颗粒速度分布函数偏离理想分布的程度与颗粒团聚程度、局域颗粒速度脉动量及颗粒浓度脉动量大小正相关。这说明颗粒团聚物的形成和演化使气固流化系统远离平衡态,从而导致颗粒速度分布呈现非麦克斯韦分布特征。最后结合能量最小多尺度(EMMS)模型对稀密相结构进行解析,并与实验获得的速度分布函数进行了对比,发现实验结果符合EMMS模型的稀密相共存假设,证明了采用双峰分布对颗粒速度分布函数进行近似在理论上可行。

Multifunctional electroactive bio-adhesive for robustly-integrated wound therapy and postoperative wound-status warning and assessment

Wound abnormalities such as secondary wound laceration and inflammation are common postoperative health hazards during clinical procedures.The continuous treatment,healing induction,and real-time visualization of wound status and complications,including wound re-tearing,inflammation,and morphology,are key focal points for comprehensive healthcare.Herein,an on-demand quadruple energy dissipative strategy was proposed for the nanoengineering of a physically and chemically synergistic double-layer gelatin-based bio-adhesive(DLGel)by combining a multi-network adhesive layer and a versatile electroactive energy dissipative layer based on contrivable interlocking micro-pillar arrays and crosslinked polymer chains.The subtly multiple energy dissipation designs enable DLGel with robust adhesive strength to omnipotently wet and dynamic tissue,providing a basis for reliable wound closure.DLGel achieves comprehensive wound-healing induction through electrical stimulation and possesses reversible underwater light/thermal adhesion,excellent hemostatic performance,outstanding antimicrobial properties,and self-repair capability.Furthermore,a novel deep-learning strategy is creatively established to respond to mechanical deformation due to wound anomalies.This strategy translates biological information into visual graphics,providing real-time early warning and assessment of postoperative wound-abnormality/-morphology,such as laceration,inflammation,and necrosis.Therefore,DLGel and its associated signal collection and processing protocol enable the integration of reliable wound closure,wound healing,and real-time postoperative wound-status warning and assessment within the unobservable and undetectable“black box”regions in a context of non-clinical comprehensive therapy.