Results 201 to 210 of about 49,842 (269)

Electrochemical inhibition bacterial sensor array for detection of water pollutants: artificial neural network (ANN) approach. [PDF]

Anal Bioanal Chem, 2019
Abu-Ali H, Nabok A, Smith TJ.
europepmc   +1 more source

Artificial Intelligence Tools for Carbon Nanotube Research: Opportunities From Synthesis to Applications

Carbon and Hydrogen, EarlyView.
Artificial intelligence tools are reshaping carbon nanotube research by connecting synthesis, characterization, and application‐oriented design. This review outlines how supervised learning, deep learning, Bayesian optimization, and large language models accelerate data extraction, experiment planning, and structure–property discovery for carbon ...
Yanlong Zhao, Fei Wang, Qixuan Cai, Aike Xi, Kangkang Wang, Khaixien Leu, Rufan Zhang   +6 more
wiley   +1 more source

Modelling of Behavior for Inhibition Corrosion of Bronze Using Artificial Neural Network (ANN). [PDF]

Entropy (Basel), 2018
Millán-Ocampo DE, Parrales-Bahena A, González-Rodríguez JG, Silva-Martínez S, Porcayo-Calderón J, Hernández-Pérez JA.   +5 more
europepmc   +1 more source

Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions

Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.
Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley   +1 more source

Realtime forecasting of COVID 19 cases in Karnataka state using Artificial neural network (ANN)

, 2020
Shetty RP, P SP.
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

Structure‐Function Tailoring of Plasmonic Nanomaterials for Thin‐Film Photovoltaics

Carbon Energy, EarlyView.
This review discusses the mechanisms and recent advancements of plasmonics in achieving effective light management to enhance the performance of thin‐film solar cells. It highlights applications in high‐performance perovskite solar cells and future‐oriented tandem solar cells.
Sen Jiang, Wenhan Yang, Annan Zhu, Yuexin Lin, Zhaohui Xie, Ran Chen, Wenjing Zhu, Jin Liu, Yingjie Zhu, Wenye Jiang, Zhiyuan Gu, Kaiyang Wang, Shengchun Yang, Nan Zhang, Chao Liang   +14 more
wiley   +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

Perovskite Photodetectors at the Intelligence Frontier: From Tunable Materials to Adaptive Optoelectronic Systems

Carbon Energy, EarlyView.
This comprehensive review presents a progressive roadmap for perovskite vision detectors. Centered on perovskite‐based artificial perception, the graphic illustrates a systematic evolution: starting with fundamental material engineering and device architectures, advancing toward complex functional strategies such as flexible neuromorphic imaging ...
Chenglong Li, Wei Qu, Deepak Kumar, Naini Jain, Nur Wardina Syahirah binti Mohamad Fadil, Nur Najiha binti Ahmad Rasid, Emannuel Kymakis, Maria Vasilopoulou, Chunxiong Bao, Feng Gao, Peng Gao, Sanjeev Kumar Sharma, Mohammad Khaja Nazeeruddin, Abd. Rashid bin Mohd Yusoff, Haotong Wei   +14 more
wiley   +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