Results 171 to 180 of about 83,357 (219)

Unveiling electric-field-driven deformation dynamics in metal nanostructures. [PDF]

Nat Commun
Li Y, Xia L, Li N, Wang J, Tang S, Ge Y, Xiao B, Xie D, Cheng Y, Shan ZW, Djurabekova F, Meng G.   +11 more
europepmc   +1 more source

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

Microstructure and Superconductivity of Mechanically Alloyed Nb_0.67(TiZrHf)_0.33 High-Entropy Alloy. [PDF]

Materials (Basel)
Sobota P, Bartz W.
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

More Than a Buffer in Biochemistry: Tris as an Architect and Gatekeeper of Metal-Oxo Assembly. [PDF]

Angew Chem Int Ed Engl
Gumerova NI, Rompel A.
europepmc   +1 more source

Donor–Acceptor Covalent Organic Framework Enables Ambipolar Charge Storage in Aluminum‐Ion Energy Storage

Advanced Energy Materials, EarlyView.
A donor–acceptor covalent organic framework is designed as an ambipolar cathode for aluminum‐ion energy storage. The crystalline, microporous architecture enables intrinsic charge transport without conductive additives. Multi‐electron redox activity at donor and acceptor sites supports high capacity, excellent stability, and efficient reversible ...
Cataldo Valentini, Verónica Montes‐García, Wojciech Kukułka, Saira Sarwar, Michał Bielejewski, Mateusz Wlazło, Rossella Greco, Viktorija Pankratova, Ignacio Pérez‐Juste, Dawid Pakulski, Paolo Samorì, Artur Ciesielski   +11 more
wiley   +1 more source

The Study on the Capacitive Properties of Tungsten Oxide/Graphene Composites Prepared by Ultrasonic Spray Deposition. [PDF]

ACS Omega
Li CP, Su CC.
europepmc   +1 more source

CFD modeling and sensitivity‐guided design of silicon filament CVD reactors

AIChE Journal, EarlyView.
Abstract Filament‐based chemical vapor deposition (CVD) for silicon (Si) coatings is often treated as an adaptation of planar deposition. But this overlooks fundamental shifts in transport phenomena and reaction kinetics. In filament CVD, the filament acts as a substrate, heat source, and flow disruptor simultaneously. In this work, we ask: What really
G. P. Gakis, G. Loachamín‐Suntaxi, I. G. Aviziotis, E. D. Koronaki, P. P. Filippatos, G. Chatzigiannakis, S. P. A. Bordas, D. Davazoglou, A. G. Boudouvis   +8 more
wiley   +1 more source

Investigating the Performance of X-ray Protection of Various Elements and Alloy Compounds in Aprons for Interventional Radiology. [PDF]

Adv Biomed Res
Papie M, Mansi L, Kitson S, Cheki M.
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source