Insights and reviews on battery lifetime prediction from research to practice

The rising demand for energy storage solutions,especially in the electric vehicle and renewable energy sectors,highlights the importance of accurately predicting battery health to enhance their longevity and reliability.This article comprehensively examines various methods used to forecast battery health,including physics-based models,empirical models,and equivalent circuit models,among others.It delves into the promise of data-driven prognostics,utilizing both conventional machine learning and cuttingedge deep neural network techniques.The advantages and limitations of hybrid models are thoroughly analyzed,with a focus on the benefits of integrating diverse data sources to improve prognostic precision.Through practical case studies,the article showcases the effectiveness and flexibility of these approaches.It also critically addresses the challenges encountered in applying battery health prognostics in realworld scenarios,such as issues of scalability,complexity,and data anomalies.Despite these challenges,the article underscores the emerging opportunities brought about by recent technological,academic,and research advancements.These include the development of digital twin models for batteries,the use of data-centric AI and standardized benchmarking,the potential integration of blockchain technology for enhanced data security and transparency,and the synergy between edge and cloud computing to boost data analysis and processing.The primary goal of this article is to enrich the understanding of current battery health prognostic techniques and to inspire further research aimed at overcoming existing hurdles and tapping into new opportunities.It concludes with a visionary perspective on future research directions and potential developments in this evolving field,encouraging both researchers and practitioners to explore innovative solutions.

DI-3-n-butylphthalide exerts neuroprotective effects by modulating hypoxia-inducible factor 1-alpha ubiquitination to attenuate oxidative stress-induced apoptosis

DI-3-n-butylphthalide is used to treat mild and moderate acute ischemic stroke.However,the precise underlying mechanism requires further investigation.In this study,we investigated the molecular mechanism of DI-3-n-butylphthalide action by various means.We used hydrogen peroxide to induce injury to PC12cells and RAW264.7 cells to mimic neuronal oxidative stress injury in stroke in vitro and examined the effects of DI-3-n-butylphthalide.We found that DI-3-nbutylphthalide pretreatment markedly inhibited the reduction in viability and reactive oxygen species production in PC12 cells caused by hydrogen peroxide and inhibited cell apoptosis.Furthermore,DI-3-n-butylphthalide pretreatment inhibited the expression of the pro-apoptotic genes Bax and Bnip3.DI-3-nbutylphthalide also promoted ubiquitination and degradation of hypoxia inducible factor 1α,the key transcription factor that regulates Bax and Bnip3 genes.These findings suggest that DI-3-n-butylphthalide exhibits a neuroprotective effect on stroke by promoting hypoxia inducible factor-1α ubiquitination and degradation and inhibiting cell apoptosis.

Specialized deep neural networks for battery health prognostics:Opportunities and challenges

Lithium-ion batteries are key drivers of the renewable energy revolution,bolstered by progress in battery design,modelling,and management.Yet,achieving high-performance battery health prognostics is a significant challenge.With the availability of open data and software,coupled with automated simulations,deep learning has become an integral component of battery health prognostics.We offer a comprehensive overview of potential deep learning techniques specifically designed for modeling and forecasting the dynamics of multiphysics and multiscale battery systems.Following this,we provide a concise summary of publicly available lithium-ion battery test and cycle datasets.By providing illustrative examples,we emphasize the efficacy of five techniques capable of enhancing deep learning for accurate battery state prediction and health-focused management.Each of these techniques offers unique benefits.(1)Transformer models address challenges using self-attention mechanisms and positional encoding methods.(2) Transfer learning improves learning tasks within a target domain by leveraging knowledge from a source domain.(3) Physics-informed learning uses prior knowledge to enhance learning algorithms.(4)Generative adversarial networks(GANs) earn praise for their ability to generate diverse and high-quality outputs,exhibiting outstanding performance with complex datasets.(5) Deep reinforcement learning enables an agent to make optimal decisions through continuous interactions with its environment,thus maximizing cumulative rewards.In this Review,we highlight examples that employ these techniques for battery health prognostics,summarizing both their challenges and opportunities.These methodologies offer promising prospects for researchers and industry professionals,enabling the creation of specialized network architectures that autonomously extract features,especially for long-range spatial-temporal connections across extended timescales.The outcomes could include improved accuracy,faster training,and enhanced generalization.

Battery prognostics and health management for electric vehicles under industry 4.0

Transportation electrification is essential for decarbonizing transport. Currently, lithium-ion batteries are the primary power source for electric vehicles (EVs). However, there is still a significant journey ahead before EVs can establish themselves as the dominant force in the global automotive market. Concerns such as range anxiety, battery aging, and safety issues remain significant challenges.

Boosting extraction of Pb in contaminated soil via interfacial solar evaporation of multifunctional sponge

Interfacial solar water evaporation is a reliable way to accelerate water evaporation and contaminant remediation.Embracing the recent advance in photothermal technology,a functional sponge was prepared by coating a sodium alginate(SA)impregnated sponge with a surface layer of reduced graphene oxide(rGO)to act as a photothermal conversion medium and then subsequently evaluated for its ability to enhance Pb extraction from contaminated soil driven by interfacial solar evaporation.The SA loaded sponge had a Pb adsorption capacity of 107.4 mg g^(-1).Coating the top surface of the SA sponge with rGO increased water evaporation performance to 1.81 kg m^(-2)h^(-1)in soil media under one sun illumination and with a wind velocity of 2 m s^(-1).Over 12 continuous days of indoor evaporation testing,the Pb extraction efficiency was increased by 22.0%under 1 sun illumination relative to that observed without illumination.Subsequently,Pb extraction was further improved by 48.9%under outdoor evaporation conditions compared to indoor conditions.Overall,this initial work shows the significant potential of interfacial solar evaporation technologies for Pb contaminated soil remediation,which should also be applicable to a variety of other environmental contaminants.

Review on supercapacitors:Technologies and performance evaluation

The development of electrochemical capacitors(i.e.supercapacitors)have attracted a lot of attention in recent years because of the increasing demand for efficient,high-power energy storage.Electrochemical capacitors(ECs)are particularly attractive for transportation and renewable energy generation applications,taking advantage of their superior power capability and outstanding cycle life.Over the past decade,various advanced electrode materials and cell design are being studied to improve the energy density of ECs.Hybrid Li-ion capacitors and pseudo-capacitors that utilize fast surface redox reactions of metal oxide and doped polymers are the prime candidates being considered.This paper is concerned with the metrics being used to describe the performance of ECs and how the metrics are evaluated by testing devices and how the data from the testing are best interpreted.Emphasize is on relating testing of advanced ECs using materials more complex than activated carbons to testing electric double-layer capacitors(EDLCs)using carbon in both electrodes.A second focus of the paper is projecting the potential of the advanced materials and ionic liquid electrolytes for the development of complete EC cells having an energy density more than a factor of ten greater the energy density of the EDLC devices currently on the market.This potential was evaluated by calculating the performance(energy and power)of a series of ECs that utilize the advanced materials that have been studied by electrochemists over the past 10-15 years.The capacitance and resistance of the advanced ECs were calculated utilizing specific capacitance(F/g or F/cm^(3))and porosity data for the electrode materials and ionic conductivity of the electrolytes.It was concluded that hybrid ECs can be developed with energy densities of at least 50 Wh/kg,70 Wh/L with efficient power greater than 3 k W/kg.Continued research on micro-porous carbons with specific capacitance of 200 F/g and greater is needed.to achieve these EC performance goals.

New method of predicting surge pressure apply to horizontal well based on casson flow

In order to predict the surge pressure caused in the horizontal well drilling process, a new simple and applicable method has been established. It is based on the general theory of hydrostatic drilling fluid mechanics, and specifically described the flowing physical model towards surge pressure in horizontal well annulus, taking the effect of string eccentricity on the flowing law of drilling fluid into consideration. According to the constitutive equation of casson-mode under one-dimensional steady flow and the equations of annular flow rate under different drill string working conditions, this paper introduced the flow rate computation models of axial laminar flow in eccentric annulus apply to horizontal well, of which the numerical model was calculated by the program called Mathematica, ultimately, a new model for surge pressure prediction towards each interval in horizontal well was put forward. Application examples indicated that it can solve questions easily and precisely, which presents important meaning of guidance to the safety control while horizontal well drilling.

Optimizing electronic structure of Mo_(2)TiC_(2)T_(x) MXene through Nb doping for enhanced electrochemical performance

MXene is a promising electrode material for both high volumetric capacitance and high-rate performance in supercapacitors.However,the current study has mainly focused on the monometallic element Ti_(3)C_(2)T_(x) MXene until now,while the bimetallic and multimetallic MXene have received comparatively less attention.In this work,we demonstrate that the electronic structure of the Mo_(2)TiC_(2)T_(x) MXene could be regulated by fine-tuning the content of doped Nb atoms.The enhanced electron cloud density of surface–O termination and the electron spin of the Mo atoms in the Mo_(2)TiC_(2)T_(x) MXene,leads to the boost of electric double-layer capacitor(EDLC)and improvement of pseudocapacitance.As a consequence,the electrochemical performance of Nb-doped Mo_(2)TiC_(2)T_(x) MXene(Nb-0.3-MXene)demonstrates a capacitance of 398 F·cm^(−3),roughly doubling that of the pristine Mo_(2)TiC_(2)T_(x) MXene electrode at 197 F·cm^(−3) in the 3 M H_(2)SO_(4) electrolyte.At the same time,the Nb-0.3-MXene could even maintain a capacitance of 82.75% at 200 mV·s−1,with high cyclic stability for 19,000 cycles at 10 A·g−1.Additionally,Nb-0.3-MXene-based hybrid supercapacitors deliver a remarkable volumetric energy density of 48.1 W·h·L^(−1)at 230.7 W·L^(−1),and 34.4 W·h·L^(−1)at a high power density of 82.6 kW·L^(−1).There exists a balance between the volumetric capacitance and rate performance with different ratios of Nb atoms in the Nb-doped MXene due to the strong interaction between the Nb-doped MXene and the intercalated protons.Therefore,optimizing the electronic structure of MXene through heteroatom doping is of great potential for enhanced supercapacitor performance.

Enhancing solar steam generation using a highly thermally conductive evaporator support

Interfacial solar steam generation is an efficient water evaporation technology which has promising applications in desalination,sterilization,water purification and treatment.A common component of evaporator design is a thermal-insulation support placed between the photothermal evaporation surface and bulk water.This configuration,common in 2-dimensional(2 D)evaporation systems,minimizes heat loss from evaporation surface to bulk water,thus localizing the heat on the evaporation surface for efficient evaporation.This design is subsequently directly adopted for 3-dimensional(3 D)evaporators without any consideration if it is appropriate.However,unlike 2 D solar evaporators,the 3 D evaporators can also harvest additional energy(other than solar light)from the air and bulk water to enhance evaporation rate.In this scenario,the use of thermal insulator support is not proper since it will hinder energy extraction from water.Here,the traditional 3 D evaporator configuration was completely redesigned by using a highly thermally conductive material,instead of a thermal insulator,to connect evaporation surfaces and the bulk water.Much higher evaporation rates were achieved by this strategy,owing to the rapid heat transfer from the bulk water to the evaporation surfaces.Indoor and outdoor tests both confirmed that evaporation performance could be significantly improved by substituting a thermal insulator with thermally conductive support.These findings will redirect the future design of 3 D photothermal evaporators.

Implantable nerve guidance conduits:Material combinations,multi-functional strategies and advanced engineering innovations

Nerve guidance conduits(NGCs)have attracted much attention due to their great necessity and applicability in clinical use for the peripheral nerve repair.Great efforts in recent years have been devoted to the development of high-performance NGCs using various materials and strategies.The present review provides a comprehensive overview of progress in the material innovation,structural design,advanced engineering technologies and multi functionalization of state-of-the-art nerve guidance conduits NGCs.Abundant advanced engineering technologies including extrusion-based system,laser-based system,and novel textile forming techniques in terms of weaving,knitting,braiding,and electrospinning techniques were also analyzed in detail.Findings arising from this review indicate that the structural mimetic NGCs combined with natural and synthetic materials using advanced manufacturing technologies can make full use of their complementary advantages,acquiring better biomechanical properties,chemical stability and biocompatibility.Finally,the existing challenges and future opportunities of NGCs were put forward aiming for further research and applications of NGCs.