Results 181 to 190 of about 96,422 (267)

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

4D CT angiography and computational biomechanics dataset for structural integrity assessment of abdominal aortic aneurysms. [PDF]

Data Brief
Jamshidian M, Wittek A, Sekhavat S, Alkhatib F, Ritter JC, Parizel PM, Mufty H, Maleux G, Fourneau I, Gizewski ER, Gassner E, Loizides A, Lutz M, Enzmann FK, Bernard F, Minvielle L, Fondanèche A, Polce J, Wood C, Miller K.   +19 more
europepmc   +1 more source

Sustainable Carbon Fibers Enable Stable Long‐Term Lithium Metal Deposition for Prospective Zero‐Excess Lithium Metal Batteries

Advanced Energy Materials, EarlyView.
This work presents lightweight, lignin‐derived carbon fiber current collectors that enable controlled lithium deposition. Structural defects and intermediate‐sized pores stabilize pre‐nucleation quasi‐metallic lithium clusters, promoting uniform lithium plating and stripping.
Samantha L. S. Southern, Jonathon Cottom, Jana B. Fritzke, Emma N. Antonio, Zhenyu Guo, Bhavini Patel, Romain Tort, Ifan E. L. Stephens, Camilla Di Mino, Clare P. Grey, Rhodri Jervis, Emilia Olsson, Heather Au, Maria‐Magdalena Titirici   +13 more
wiley   +1 more source

Application of randomized quadrature formulas to the finite element method for elliptic equations. [PDF]

BIT Numer Math
Kruse R, Polydorides N, Wu Y.
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

Biomechanical Behavior of Different Framework and Superstructure Material Combinations in Two-Implant-Supported Four-Unit Prostheses: A Dynamic Finite Element Analysis. [PDF]

Materials (Basel)
Hajghani N, Günal-Abdulcelil B.
europepmc   +1 more source

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +4 more
wiley   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

Integrated Dynamic Modeling and Ground Test Validation for Spacecraft Micro-Vibration Suppression Considering Disturbance, Isolation, and Pointing Control. [PDF]

Sensors (Basel)
Wang H, Yan H, Tian L, Zang X, Zu Y.
europepmc   +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