Probing the electric double layer structure at nitrogen-doped graphite electrodes by constant-potential molecular dynamics simulations

Electric double layer(EDL)is a critical topic in electrochemistry and largely determines the working performance of lithium batteries.However,atomic insights into the EDL structures on heteroatom-modified graphite anodes and EDL evolution with electrode potential are very lacking.Herein,a constant-potential molecular dynamics(CPMD)method is proposed to probe the EDL structure under working conditions,taking N-doped graphite electrodes and carbonate electrolytes as an example.An interface model was developed,incorporating the electrode potential and atom electronegativities.As a result,an insightful atomic scenario for the EDL structure under varied electrode potentials has been established,which unveils the important role of doping sites in regulating both the EDL structures and the following electrochemical reactions at the atomic level.Specifically,the negatively charged N atoms repel the anions and adsorb Li~+at high and low potentials,respectively.Such preferential adsorption suggests that Ndoped graphite can promote Li~+desolvation and regulate the location of Li~+deposition.This CPMD method not only unveils the mysterious function of N-doping from the viewpoint of EDL at the atomic level but also applies to probe the interfacial structure on other complicated electrodes.

A self-adaptive,data-driven method to predict the cycling life of lithium-ion batteries

Accurately forecasting the nonlinear degradation of lithium-ion batteries(LIBs)using early-cycle data can obviously shorten the battery test time,which accelerates battery optimization and production.In this work,a self-adaptive long short-term memory(SA-LSTM)method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data.Specifically,two features were extracted from discharge voltage curves by a time-series-based approach and forecasted to further cycles using SA-LSTM model.The as-obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model.The proposed method achieved an average online prediction error of 6.00%and 6.74%for discharge capacity and end of life,respectively,when using the early-cycle discharge information until 90%capacity retention.Fur-thermore,the importance of temperature control was highlighted by correlat-ing the features with the average temperature in each cycle.This work develops a self-adaptive data-driven method to accurately predict the cycling life of LIBs,and unveils the underlying degradation mechanism and the impor-tance of controlling environmental temperature.

Artificial intelligence in pediatrics

The rapid development of information technology has involved advances in artificial intelligence(AI),big data processing,and cloud computing,with significant and farreaching effects on the structure and efficiency of the traditional healthcare industry,as well as the establishment and maintenance of modern medical management information systems.AI solutions for handling data in the medical field,such as electronic medical records,medical imaging technology,medical big data,intelligent drug design,and smart health management systems have emerged,which improve the standardization and accuracy of clinical decision making,while providing more dimensions of data accumulation for medical knowledge-based systems.These developments can also support physicians and researchers in the optimization of treatment plans,and decision making about optimal treatment options.This review aims to summarize recent advances in the research and clinical use of AI in pediatrics.