Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation

Metal-ion batteries(MIBs),including alkali metal-ion(Li^(+),Na^(+),and K^(3)),multi-valent metal-ion(Zn^(2+),Mg^(2+),and Al^(3+)),metal-air,and metal-sulfur batteries,play an indispensable role in electrochemical energy storage.However,the performance of MIBs is significantly influenced by numerous variables,resulting in multi-dimensional and long-term challenges in the field of battery research and performance enhancement.Machine learning(ML),with its capability to solve intricate tasks and perform robust data processing,is now catalyzing a revolutionary transformation in the development of MIB materials and devices.In this review,we summarize the utilization of ML algorithms that have expedited research on MIBs over the past five years.We present an extensive overview of existing algorithms,elucidating their details,advantages,and limitations in various applications,which encompass electrode screening,material property prediction,electrolyte formulation design,electrode material characterization,manufacturing parameter optimization,and real-time battery status monitoring.Finally,we propose potential solutions and future directions for the application of ML in advancing MIB development.

Prospective evaluation of health status, quality of life and clinical outcomes following implantable defibrillator generator exchange

BACKGROUND Little is known about health status and quality of life(QoL)after implantable cardioverter-defibrillator(ICD)generator exchange(GE).METHODS We prospectively followed patients undergoing first-time ICD GE.Serial assessments of health status were performed by administering the 36-Item Short Form Survey(SF-36).RESULTS Mean age was 67.5±14.3 years,left ventricle ejection fraction(LVEF)was 36.5%±15.0%and over 40%of the cohort had improved LVEF to>35%at the time of GE.SF-36 scores were significantly worse in physical/general health domains compared to domains of emotional/social well-being(P<0.001 for each comparison).Physical health scores were significantly worse among those with medical comorbidities including diabetes,chronic obstructive pulmonary disease and atrial fibrillation.Mean follow-up was 1.6±0.5 years after GE.Overall SF-36 scores remained stable across all domains during follow-up.Survival at 3 years post-GE was estimated at 80%.Five patients died during follow-up and most deaths were adjudicated as non-arrhythmic in origin.Four patients experienced appropriate ICD shocks after GE,three of whom had LVEF which remains impaired LVEF(i.e.,<35%)at the time of GE.CONCLUSION Patients undergoing ICD GE have significantly worse physical health compared to emotional/social well-being,which is associated with the presence of medical comorbidities.In terms of clinical outcomes,the incidence of appropriate shocks after GE among those with improvement in LVEF is very low,and most deaths post-procedure appear to be non-arrhythmic in origin.These data represent an attempt to more fully characterize the spectrum of QoL and clinical outcomes after GE.

Current Status and Prospects of Biomass Energy Industry in China

At present biomass energy industry is in its infancy in China and it has a bright future. Biomass energy production used grain as raw materials has entered industrialization phase.Some key technologies of biomass energy industry are coming to mature.China has issued relevant industrial standards laws and regulations,and has provided support in finance,loan,tax,etc.But China's biomass energy industry is faced with many problems which need to be solved.For example,taking grain as raw materials is unsustain...