Results 131 to 140 of about 132,835 (262)
Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design
A large language model (LLM) based pipeline is developed to automatically extract a comprehensive and accurate multicomponent alloy database from literature corpus. The extracted dataset is integrated with sustainability indicators to identify potential alloys that outperform existing industrial benchmark materials in terms of both performance and ...
Aravindan Kamatchi Sundaram +4 more
wiley +1 more source
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su +13 more
wiley +1 more source
Joël Perrin (1945-1999), hommage paru dans la revue Histoire de l’art, n° 42-43
Pierre Curie, Françoise Levaillant
doaj +1 more source
Corrigendum to 'Health networking on cancer in the European Union: a 'green paper' by the EU Joint Action on Networks of Expertise (JANE)': [ESMO Open. Volume 10, Issue 2, February 2025, 104126]. [PDF]
Casali PG +19 more
europepmc +1 more source
Electrolyte–Framework Matching in High‐Voltage TEMPO‐COF Cathodes for Lithium Batteries
TEMPO‐functionalized covalent organic frameworks are investigated as high‐voltage cathodes for lithium batteries. Screening five electrolyte anions identifies lithium difluoro(oxalato)borate (LiDFOB) as the optimal electrolyte, balancing capacity, rate capability, and cycling stability, while buckypaper electrodes achieve high mass loading (40 mg cm−2)
Marilyn Esclance DMello +5 more
wiley +1 more source
Hommage à Gilles Chomer : L’Enfant Jésus retrouvé au Temple, un nouveau Stella dans les Pyrénées
Pierre Curie, Bertrand Ducourau
doaj +1 more source
Up and Down the Hill-Plot: Hydrides of CeGe-Structure and Magnetism. [PDF]
Dorsch LY +6 more
europepmc +1 more source
Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai +3 more
wiley +1 more source

