查看更多>>摘要：Providing early safety warning for batteries in real-world applications is challenging.In this study,com-prehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three charac-teristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4 ℃,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module valida-tion.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.
查看更多>>摘要：The activity and selectivity of electrocatalytic CO2 reduction reaction(CO2RR)to C2 products on metal catalysts can be regulated by molecular surfactants.However,the mechanism behind it remains elusive and debatable.Herein,copper nanowires(Cu NWs)were fabricated and decorated with cobalt phthalo-cyanine(CoPc).The electronic interaction between the Cu NWs,CoPc,CO2 and CO2RR intermediates were explored by density functional theory(DFT)calculations.It was found that the selectivity and activity of CO2RR towards C2 products on Cu NWs were considerably enhanced from 35.2％to 69.9％by surface dec-oration of CoPc.DFT calculations revealed that CO2RR can proceed in the interphase between Cu substrate and CoPc,and the CO2RR intermediates could synergistically bond with both Cu and Co metal centre in CuNWs-CoPc,which favours the adsorption of CO2,CO and CO2RR intermediates,thus reducing the free energy for CO-CO coupling towards C2 products.The synergistic interaction was further extended to phthalocyanine(Pc)and other metal phthalocyanine derivatives(MPc),where a relatively weaker syner-gistic interaction of CO intermediates with MPc and Cu substrate and only a slight enhancement of CO2RR towards C2 products were observed.This study demonstrates a synergistic catalysis pathway for CO2RR,a novel perspective in interpreting the role of CoPc in enhancing the activity and selectivity of CO2RR on Cu NWs,in contrast to the conventional tandem catalysis mechanism.
查看更多>>摘要：The development of active yet stable catalysts for oxygen reduction reaction(ORR)is still a major issue for the extensive permeation of fuel cells into everyday technology.While nanostructured Pt catalysts are to date the best available systems in terms of activity,the same is not true for stability,particularly under operating conditions.In this work,PtxY alloy nanoparticles are proposed as active and durable electrocat-alysts for ORR.PtxY nanoalloys are synthesized and further optimized by laser ablation in liquid followed by laser fragmentation in liquid.The novel integrated laser-assisted methodology succeeded in producing PtxY nanoparticles with the ideal size(＜10 nm)of commercial Pt catalysts,yet resulting remarkably more active with E1/2=0.943 V vs.RHE,specific activity=1095 μA cm-2 and mass activity＞1000 A g-1.At the same time,the nanoalloys are embedded in a fine Pt oxide matrix,which allows a greater stability of the catalyst than the commercial Pt reference,as directly verified on a gas diffusion electrode.
查看更多>>摘要：Heterojunction construction,especially S-scheme heterojunction,represents an efficient universal strat-egy to achieve high-performance photocatalytic materials.For further performance stimulation of these well-designed heterojunctions,modulating the interfacial internal electric field(IEF)to steer dynamic charge transfer represents a promising approach.Herein,we realized the precise regulation of Fermi level(EF)of the oxidation semiconductor(mesoporous WO3-x)by tailoring the concentration of oxygen vacan-cies(VO),maximizing the IEF intensity in Cs2CuBr4@WO3-x(CCB@WO3-x)S-scheme heterojunction.The augmented IEF affords a robust driving force for directional electron delivery,leading to boosted charge separation.Hence,the developed CCB@WO3-x S-scheme heterojunction demonstrated outstanding pho-tocatalytic CO2 reduction performance,with the electron consumption rate(Relectron)up to 390.34 μmol g-1 h-1,which is 3.28 folds higher than that of pure CCB.An in-depth analysis of the S-scheme electron transfer mode was presented via theoretical investigations,electron spin resonance(ESR),photo-irradiated Kelvin probe force microscopy(KPFM),and in-situ X-ray photoelectron spec-troscopy(XPS).Finally,the CO2 photoconversion route was explored in detail using in-situ diffuse reflec-tance infrared Fourier transform spectroscopy(DRIFTS)and DFT theoretical calculations.
查看更多>>摘要：This work extensively investigates the thermal characteristic evolution of lithium-ion batteries under dif-ferent degradation paths,and the evolution mechanism through multi-angle characterization is revealed.Under different degradation paths,the evolution trend of temperature rise rate remains unchanged with respect to depth of discharge during the adiabatic discharge process,albeit to varying degrees of alter-ation.The temperature rise rate changes significantly with aging during the adiabatic discharge process under low-temperature cycling and high-rate cycling paths.The total heat generation rate,irreversible heat generation rate,and reversible heat generation rate exhibit similar evolution behavior with aging under different degradation paths.The interval range of endothermic process of reversible electrochem-ical reactions increases and the contribution of irreversible heat to the total heat increases with aging.To further standardize the assessment of different degradation paths on the thermal characteristics,this work introduces the innovative concept of"Ampere-hour temperature rise".In low-temperature cycling and high-rate cycling paths,the ampere-hour temperature rise increases significantly with aging,partic-ularly accentuated with higher discharge rates.Conversely,in high-temperature cycling and high-temperature storage paths,the ampere-hour temperature rise remains relatively stable during the initial stages of aging,yet undergoes a notable increase in the later stages of aging.The multi-angle character-ization reveals distinct thermal evolution behavior under different degradation paths primarily attribu-ted to different behavior changes of severe side reactions,such as lithium plating.The findings provide crucial insights for the safe utilization and management of lithium-ion batteries throughout the whole lifecycle.
查看更多>>摘要：Polyethylene oxide(PEO)-based solid polymer electrolytes(SPEs)with good electrochemical stability and excellent Li salt solubility are considered as one of the most promising SPEs for solid-state lithium metal batteries(SSLMBs).However,PEO-based SPEs suffer from low ionic conductivity at room temperature and high interfacial resistance with the electrodes due to poor interfacial contact,seriously hindering their practical applications.As an emerging technology,in-situ polymerization process has been widely used in PEO-based SPEs because it can effectively increase Li-ion transport at the interface and improve the interfacial contact between the electrolyte and electrodes.Herein,we review recent advances in design and fabrication of in-situ polymerized PEO-based SPEs to realize enhanced performance in LMBs.The merits and current challenges of various SPEs,as well as their stabilizing strategies are pre-sented.Furthermore,various in-situ polymerization methods(such as free radical polymerization,catio-nic polymerization,anionic polymerization)for the preparation of PEO-based SPEs are summarized.In addition,the application of in-situ polymerization technology in PEO-based SPEs for adjustment of the functional units and addition of different functional filler materials was systematically discussed to explore the design concepts,methods and working mechanisms.Finally,the challenges and future pro-spects of in-situ polymerized PEO-based SPEs for SSLMBs are also proposed.
查看更多>>摘要：Acid loss and plasticization of phosphoric acid(PA)-doped high-temperature polymer electrolyte mem-branes(HT-PEMs)are critical limitations to their practical application in fuel cells.To overcome these barriers,poly(terphenyl piperidinium)s constructed from the m-and p-isomers of terphenyl were synthe-sized to regulate the microstructure of the membrane.Highly rigid p-terphenyl units prompt the forma-tion of moderate PA aggregates,where the ion-pair interaction between piperidinium and biphosphate is reinforced,leading to a reduction in the plasticizing effect.As a result,there are trade-offs between the proton conductivity,mechanical strength,and PA retention of the membranes with varied m/p-isomer ratios.The designed PA-doped PTP-20m membrane exhibits superior ionic conductivity,good mechanical strength,and excellent PA retention over a wide range of temperature(80-160 ℃)as well as satisfactory resistance to harsh accelerated aging tests.As a result,the membrane presents a desirable combination of performance(1.462 W cm-2 under the H2/O2 condition,which is 1.5 times higher than that of PBI-based membrane)and durability(300 h at 160 ℃ and 0.2 A cm-2)in the fuel cell.The results of this study pro-vide new insights that will guide molecular design from the perspective of microstructure to improve the performance and robustness of HT-PEMs.
查看更多>>摘要：Achieving composition tunability and structure editability of nanoalloys with high level strain may be an efficient strategy to remarkably boost catalytic performance toward oxygen evolution reaction(OER)in acidic water oxidation.Herein,lotus root-like Rulr alloys with native micro-strain were constructed by an epitaxial growth of Ru-richened hcp-(0001)branches on Ir-richened fcc-(1 11)seeds using a polyol ther-mal synthesis strategy.The resultant Ru60Ir40 alloy shows an OER overpotential of 197 mV at 10 mA cm-2 and a Tafel slope of 46.59 mV dec-1,showing no obvious activity decay for 80 h continuous chronopoten-tiometry test in 0.5 M H2SO4.The related characterizations including X-ray absorption fine structure(XAFS)spectroscopy and density functional theory(DFT)calculations show that that the remarkably improved activity of the lotus root-like alloy can be attributed to the(0001)facet-triggered strain,which can efficiently optimize the electronic band structure of the active metal and the weakening of the chemisorption of oxygen-containing substances to boost OER electrocatalysis.Therefore,this work pro-vides a new strategy to designing a class of advanced electrocatalysts with high strain using diverse nanostructures as building materials for carbon-free clean energy conversion systems.
Xingjun LiDan YuS?ren Byg VilsenDaniel Ioan Stroe...
591-604页
查看更多>>摘要：State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consist-ing of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian pro-cess regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05％,2.57％,and 2.82％for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging pro-files with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.
查看更多>>摘要：Accurate capacity estimation is of great importance for the reliable state monitoring,timely maintenance,and second-life utilization of lithium-ion batteries.Despite numerous works on battery capacity estima-tion using laboratory datasets,most of them are applied to battery cells and lack satisfactory fidelity when extended to real-world electric vehicle(EV)battery packs.The challenges intensify for large-sized EV battery packs,where unpredictable operating profiles and low-quality data acquisition hinder precise capacity estimation.To fill the gap,this study introduces a novel data-driven battery pack capac-ity estimation method grounded in field data.The proposed approach begins by determining labeled capacity through an innovative combination of the inverse ampere-hour integral,open circuit voltage-based,and resistance-based correction methods.Then,multiple health features are extracted from incre-mental capacity curves,voltage curves,equivalent circuit model parameters,and operating temperature to thoroughly characterize battery aging behavior.A feature selection procedure is performed to deter-mine the optimal feature set based on the Pearson correlation coefficient.Moreover,a convolutional neu-ral network and bidirectional gated recurrent unit,enhanced by an attention mechanism,are employed to estimate the battery pack capacity in real-world EV applications.Finally,the proposed method is val-idated with a field dataset from two EVs,covering approximately 35,000 kilometers.The results demon-strate that the proposed method exhibits better estimation performance with an error of less than 1.1％compared to existing methods.This work shows great potential for accurate large-sized EV battery pack capacity estimation based on field data,which provides significant insights into reliable labeled capacity calculation,effective features extraction,and machine learning-enabled health diagnosis.
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