Results 141 to 150 of about 10,090 (236)

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

Neuromodulation of Working Memory: Mechanisms, Targets, and Behavioral Outcomes. [PDF]

Neurosci Insights
Sadat Rafiei SK, Ghorbani F, Rezaei J, Saremi A, Rafiei M, Hajizamani S, Asadi Anar M, Samiei M.   +7 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

Multimorbidity: a core priority for learning health systems amidst vertical disease programme cuts. [PDF]

Health Res Policy Syst
Dixon J, Dhodho E, Webb K, Chimberengwa P, Mundoga F, Choga K, Chinyanga TT, Midzi N, Mudavanhu J, Nkala L, Gwati G, Gongora R, Zizhou S, Shambira G, Makurumidze R, Midzi SM, Abimbola S, Chandler CIR, Ferrand RA.   +18 more
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

A hemoglobin concentration dataset derived from triple-wavelength photoplethysmography for machine learning applications. [PDF]

Data Brief
Abuzairi T, Maharani DB.
europepmc   +1 more source

Wine Tourism as a Catalyst for Sustainable Performance: The Mediating Role of Corporate Legitimacy and Green Innovation

Agribusiness, EarlyView.
ABSTRACT This study aims to explore the influence of Wine Tourism (WT) on the Sustainable Performance (SP) of wineries in Spain. It particularly investigates how Corporate Social Legitimacy (CSL) and Green Innovation (GI) may act as intermediary factors in this relationship.
Javier Martínez‐Falcó, Eduardo Sánchez‐García, Bartolomé Marco‐Lajara, Luis A. Millán‐Tudela   +3 more
wiley   +1 more source

Transforming district health systems into learning health systems: An improved strategy from the District.Team experience in Benin and Guinea. [PDF]

Learn Health Syst
Millimouno TM, Bello KOA, Dossou JP, Delamou A, Meessen B.   +4 more
europepmc   +1 more source

Robert L. Flood, Norma R.A. Romm : Diversity management; triple loop learning [Rezension]

, 1997
published
openaire   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source