Results 141 to 150 of about 31,305 (267)

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

Research on a Predictive Model for Microalbuminuria in Type 2 Diabetes Based on Machine Learning and SHAP Analysis. [PDF]

Int J Endocrinol
Liu Z, Zhou Z, Sun Y, Du X, Zhang H, Ji C.   +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

Machine learning for oral frailty factors in hospitalized schizophrenia patients: two-stage feature selection and SHAP analysis. [PDF]

Front Psychiatry
Fu Y, Yang X, Zhang T, Wang Y, Tang S, Luo Z, Yang C, Wu D.   +7 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

Integrated metabolomics and machine learning identify predictive biomarkers via SHAP analysis for sintilimab-induced rash in lung cancer patients. [PDF]

Front Pharmacol
Tian W, Tang H, Liu X, Lv J, Xu Y, Hua Y, Xie F, Ding Y.   +7 more
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

Machine translationese of large language models: Dependency triplets, text classification, and SHAP analysis. [PDF]

PLoS One
Zhang S, Zhao C.
europepmc   +1 more source

Hybrid machine learning and genetic algorithm approach for catalyst and process optimization in Fischer–Tropsch synthesis toward sustainable fuel production

The Canadian Journal of Chemical Engineering, EarlyView.
Graphical representation of a data‐driven framework for Fischer‐Tropsch synthesis (FTS) modelling and optimization. Abstract This study presents a data‐driven approach for predicting the relationships between catalyst design, process conditions, and product selectivity in Fischer–Tropsch synthesis (FTS).
Doaa M. Hassan, Ryan Gosselin, Nicolas Abatzoglou   +2 more
wiley   +1 more source

Accurate and interpretable prediction of chemical oxygen demand using explainable boosting algorithms with SHAP analysis. [PDF]

Sci Rep
Merabet K, Kim S, Heddam S, Di Nunno F, Granata F, Kisi O, Adnan RM, Zounemat-Kermani M, Külls C.   +8 more
europepmc   +1 more source