Results 191 to 200 of about 383,957 (288)

Topological Materials and Related Applications

Advanced Electronic Materials, EarlyView.
This review covers topological materials—including topological insulators, quantum valley Hall and quantum spin Hall insulators, and topological Weyl and Dirac semimetals—as well as their most recent advancements in fields such as spintronics, electronics, photonics, thermoelectrics, and catalysis.
Carlo Grazianetti, Roberto Mantovan, Emanuele Longo, Harold J. W. Zandvliet, Pantelis Bampoulis, Yu Pan, Fan Li, Xia Wang, Payton Downey, Alessandro Molle   +9 more
wiley   +1 more source

Advancing Energy Materials by In Situ Atomic Scale Methods

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
Progress in in situ atomic scale methods leads to an improved understanding of new and advanced energy materials, where a local understanding of complex, inhomogeneous systems or interfaces down to the atomic scale and quantum level is required. Topics from photovoltaics, dissipation losses, phase transitions, and chemical energy conversion are ...
Christian Jooss, Michael Seibt, Martin Wenderoth, Oliver Bünermann, Ole Bunjes, Till Domröse, Christian Eckel, Francesca Falorsi, Christoph Flathmann, Monica Kolek Martinez de Azagra, Matthias Krüger, Jonas Lindner, Tobias Meyer, Claus Ropers, Ulrich Ross, Kai Rossnagel, Sreeju Sreekantan Nair Lalithambika, Simone Techert, Georg A. Traeger, Cynthia Volkert, R. Thomas Weitz, Alec M. Wodtke   +21 more
wiley   +1 more source

Self-Sensing NiFe@N-doped Carbon Aerogel: Integrating Excellent Radar Stealth, Inherent Structural Health Monitoring, Thermal Management, and Flame Retardancy. [PDF]

Nanomicro Lett
Du X, Zhou J, Yu J, Nie X, Luo M, He X, Xiao A.   +6 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

What a "Landscape of Consciousness" Means for Neurology and Neuroscience. [PDF]

Curr Neurol Neurosci Rep
Kuhn RL.
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

A Hexa-Band Metamaterial Absorber for S, C, X, and Ku Band Applications. [PDF]

Sensors (Basel)
Sultan MA, Ahmad I, Ahmad A, Kim YJ, Choi DY.   +4 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Measuring electric-acoustic heterodyning in piezoelectric materials. [PDF]

Commun Phys
Karpisz T, Lirette RL, Hagerstrom AM, Orloff ND, Stelson AC.   +4 more
europepmc   +1 more source