Results 191 to 200 of about 7,224 (264)

Self‐Activating Electrocatalysts for Water Splitting: Advancing Structure–Performance Understanding and Beyond

Advanced Energy Materials, EarlyView.
Self‐activating electrocatalysts offer strong potential for advanced water splitting, enabled by their dynamic compositional and structural evolution during operation. This review highlights recent advances in self‐activating OER and HER catalysts, emphasizing the driving forces and mechanisms underlying their adaptive behavior. A conceptual network of
Christean Nickel, Bahareh Feizi Mohazzab, Kiarash Torabi, David Leander Troglauer, Guillermo Corea, Shikang Han, Dandan Gao   +6 more
wiley   +1 more source

Comparative Wear of Opposing Natural Enamel by Different Ceramic Materials in Fixed Dental Protheses: A Systematic Review and Meta-Analysis. [PDF]

Dent J (Basel)
Rosa CDDRD, Bento VAA, Duarte ND, Gomes JML, Okamoto R, Buchaim RL, Buchaim DV, Issa JPM, Pellizzer EP.   +8 more
europepmc   +1 more source

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +1 more source

Multi-Objective Optimization of Extrusion Parameters for High-Performance Honeycomb Cordierite Ceramics via Orthogonal Design. [PDF]

Materials (Basel)
Huang X, Wei N, Wang F, Zhang X.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Deep learning-based automated detection of Micro-cracks in monolithic zirconia crowns using Micro-CT imaging: An <i>in vitro</i> study. [PDF]

Bioinformation
Chauhan GK, Sinha RR, Mehta R, Chhabra U, Gawande SD, Arora P.   +5 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Removal of Contaminants of Emerging Concern from Wastewater Using Photocatalytic Membranes: Current Status and Challenges. [PDF]

Membranes (Basel)
Kipchumba N, Mkhize IG, Otieno B, Rutto HL, Ntwampe SK.   +4 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Large Language Models and Machine Learning Framework for Predicting Dental Ceramics Performance. [PDF]

Int Dent J
Zhou H, Bai Y, Chen Y, Fan D, Wang P, Ji P, Chen T.   +6 more
europepmc   +1 more source