Results 161 to 170 of about 659 (220)

Upscaling Sodium‐Ion Battery Cells: From Aqueous Processing to Performance Assessment of Hard Carbon|Prussian White Pouch Cells

Advanced Energy Materials, EarlyView.
This study investigates the feasibility of scaling up Prussian White (PW)‐based cathode manufacturing at a pilot scale. Through careful PW dehydration combined with optimized aqueous processing, we report the stepwise development of industrially relevant 1 Ah pouch cells and evaluate their performance under various conditions.
Faduma M. Maddar, Katerina Gonos, Martand Singh, Jacob Compton, Daniel Atkinson, Matthew J. Capener, Mark Copley, Ivana Hasa   +7 more
wiley   +1 more source

The legacy and evolvability of Pere Alberch's ideas. [PDF]

Interface Focus
Nuño de la Rosa L, Müller GB.
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Vertex models capturing subcellular scales in epithelial tissues. [PDF]

PLoS Comput Biol
Lange Z, Matthäus F, Qiu M.
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

What is emergence, after all? [PDF]

PNAS Nexus
Rizi AK.
europepmc   +1 more source

Determining the Protective Effects of Long‐Term Physical Activity, Exercise, Mental Health, and Employment on Sarcopenia—A Result of a Feasibility Study of a Registry

AGING MEDICINE, EarlyView.
This study highlights that physical activity, employment, and mental health significantly influence sarcopenia risk in healthy adults. Working part‐time or full‐time and exercising, especially sports like swimming, alongside good psychological health, may be associated with reduced odds of sarcopenia.
Mohammad Reza Shadmand Foumani Moghadam, Mostafa Shahraki Jazinaki, Sharif Etemadi, Kazem Eslami, Pegah Meghdadi, Parnian Pezeshki, Mohammad Amoushahi, Reyhane Bakhshipour, Majid Ghayour Mobarhan, Majid Khadem‐Rezaiyan, Reza Rezvani, Zohre Hosseini   +11 more
wiley   +1 more source

Null models for comparing information decomposition across complex systems. [PDF]

PLoS Comput Biol
Liardi A, Rosas FE, Carhart-Harris RL, Blackburne G, Bor D, Mediano PAM.   +5 more
europepmc   +1 more source

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

Advanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz, Zakarya Al‐Shaebi, Omer Aydin   +2 more
wiley   +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