Results 181 to 190 of about 44,463 (307)

Engineering Biochar‐Derived Functional Materials for High‐Performance Supercapacitors: Design Principles, Mechanisms, and Scalable Strategies

Carbon Energy, EarlyView.
ABSTRACT Biochar has emerged as a useful and adaptable source of carbon for supercapacitor electrodes. Its value comes from the way biomass chemistry, thermal conversion, and activation conditions shape the resulting pore network, surface groups, and degree of carbon ordering.
Soumen Mandal, Avinash C. Mendhe, Mayna Roy, Neha S. Barse, Taejoon Park, Han‐Seung Lee, Haigun Lee   +6 more
wiley   +1 more source

Listeria Genome Identification Using DNABERT Embedding With LightGBM and SHAP-Based Explainable Classification. [PDF]

Bioinform Biol Insights
Arumugam SR, P AJ, Karuppasamy SG, Murugan VS, Mbasso WF, Harrison A.   +5 more
europepmc   +1 more source

Porous Carbon Materials for Carbon Dioxide Capture

Carbon Energy, EarlyView.
This work aims to address the current status and challenges associated with the regulation of pore structures, as well as the influence of pore structures on CO2 capture. Systematic quantitative analysis of structure–property relationships, combined with machine learning approaches, can effectively evaluate the contributions of structural ...
Zhifu Liu, Chuhan Fu, Tao Qi, Hui Zhou, Guangping Hao, Yalou Guo, Guoping Hu   +6 more
wiley   +1 more source

Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspection. [PDF]

Auton Intell Syst
Stefenon SF, Matos-Carvalho JP, Mariani VC, Dos Santos Coelho L, Yow KC.   +4 more
europepmc   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Inter‐Verified Binary Classification Models: Unveiling Key Polymer Features Against Staphylococcus aureus

Carbon Innovation, EarlyView.
ABSTRACT Antimicrobial peptides (AMPs) are promising alternatives to antibiotics but require precise and costly sequence control for efficacy. Synthetic polymers composed of acrylate and acrylamide offer a simpler and more time‐efficient antibacterial strategy compared to AMPs.
Jooyoung Roh, Cyrille Boyer, Priyank Kumar   +2 more
wiley   +1 more source

What gets Redditors talking? Predicting discussion initiation and size on Reddit. [PDF]

PLoS One
Lynch C, Livan G.
europepmc   +1 more source

Random forest regression for catalyst performance prediction and validation of tri‐reforming of methane (TRM)

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Carbon dioxide‐reduced hydrogen can be synthesized through various methods such as dry‐reforming (DRM), steam reforming (SMR), and partial oxidation (POX). Tri‐reforming of methane (TRM) is a promising technology which combines all the above‐mentioned processes for the simultaneous production of hydrogen and syngas with high energy efficiency.
Paulo A. L. de Souza, Raja Muhammad Afzal, Felipe Gomes Camacho, Nader Mahinpey   +3 more
wiley   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Explainable machine-learning model for coronary artery disease diagnosis in non-AMI patients: integrating inflammatory and traditional risk factors. [PDF]

Open Heart
Yang K, Liu S, Wang C, Ni S, Bi L, Yang Y, Zhang M.   +6 more
europepmc   +1 more source