Advancing lithium-ion battery performance with heteroatom-based anode architectures for fast charging and high capacity.

Electric vehicles (EVs) are on the brink of revolutionizing transportation, but the current lithium-ion batteries (LIBs) used in them have significant limitations in terms of fast-charging capabilities and energy density. This feature article begins by examining the key challenges of using graphite for fast charging and silicon for achieving high energy density in LIBs. Firstly, it explores various design strategies employed by researchers worldwide to improve the fast-charging performance of graphite, such as surface coatings, morphological modifications, and binder design.

Bio-based poly(benzimidazole--amide)-derived N, O co-doped carbons as fast-charging anodes for lithium-ion batteries.

Lithium-ion batteries (LIBs) that can be charged faster while delivering high capacity are currently in significant demand, especially for electric vehicle applications. In this context, this study introduces a less-explored subject: nitrogen and oxygen dual-doped carbons derived from bio-based copolymers, specifically poly(benzimidazole--amide). The synthesis involved varying proportions of benzimidazole to amide, namely, 8.

A 1-D model to retrieve the vertical profiles of minor atmospheric constituents for cloud microphysical modelling: III. Disturbed weather situations.

A hybrid Monte-Carlo Gear solver developed earlier has been improvised to retrieve the vertical profiles of CH and NO during disturbed weather situations such as western disturbances, tropical cyclones, and heavy rainfall events over the megacities: Delhi, Kolkata, Chennai, and Mumbai. Due to rapid changes in the temperature during the passage of these systems over megacities, the percentage differences of CH and NO number concentrations were more compared to the Community Long-term Infrared Microwave Coupled Atmospheric Product System (CLIMCAPS). Therefore, the hybrid solver has been modified by improving maximum likelihood estimates of vertical temperature profiles.

A 1-D model to retrieve the vertical profiles of minor atmospheric constituents for cloud microphysical modeling: II. Simulation of diurnal cycle.

The accurate forecast of the diurnal cycle of the number concentration of trace gases is vital due to their influence on precipitation processes by controlling the number concentration of cloud condensation nuclei (CCN). 1-D hybrid Monte Carlo-Gear solver developed to retrieve vertical profiles of the number concentration of CCNs for microphysics modeling has been tested for representation of the diurnal cycle in the present paper. The retrieved profiles of CH and SO have been tested with the Copernicus Atmosphere Monitoring Service (CAMS) model at 3-hour time intervals for four megacities: Delhi, Kolkata, Chennai, and Mumbai for rainy and non-rainy days.

Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review.

This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists' arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems.