Results 111 to 120 of about 16,490 (305)

Magneto‐X Effects in Magnetic Soft Materials and Their Applications

Advanced Science, EarlyView.
This review systematically explores magnetic soft materials (MSMs), a novel class of composites that transform under magnetic fields. It catalogs fundamental “Magneto‐X” effects, classifies materials by their matrix and magnetic fillers, and highlights transformative applications in soft robotics, biomedical devices, flexible electronics, etc. Finally,
Ziyin Xiang, Xiangling Xia, Benjamin Ducharne, Yujie Ye, Jie Shang, Run‐Wei Li   +5 more
wiley   +1 more source

A Coefficient Inverse Problem with a Single Measurement of Phaseless Scattering Data [PDF]

, 2019
Michael V. Klibanov, Dinh-Liem Nguyen, Loc H. Nguyen   +2 more
openalex   +1 more source

Emerging Advanced Electronic Packaging Materials for Thermal Management in Power Electronics

Advanced Science, EarlyView.
This review surveys emerging materials for thermal management in advanced electronic packaging, with emphasis on ceramic substrates and thermal interface materials. Multiscale simulations and mechanistic analyses are highlighted, alongside the emerging role of artificial intelligence in predicting thermal properties and guiding design, offering ...
Yongjun Huo, Jiaqi Song, Wenqian Li, Jian Zhang, Yujin Zhang, Yang Fu, Wangchao Yuan, Xin Chen, Sichen Liu, Miao Jiang, Yuan Cheng, Gang Zhang   +11 more
wiley   +1 more source

Toward Reliable Metal Halide Perovskite FETs: From Electronic Structure and Device Physics to Stability and Performance Engineering

Advanced Electronic Materials, EarlyView.
Metal halide perovskite field‐effect transistors (PeFETs) offer great promise for flexible, low‐cost, and high‐performance due to their excellent charge carrier properties. However, challenges like ion migration, hysteresis, and instability limit their performance.
Georgios Chatzigiannakis, Anastasia Soultati, Leonidas C. Palilis, Ermioni Polydorou, Konstantinos Davazoglou, Petros‐Panagis Filippatos, Goutham Raj Perumallapelli, Ravindra Naik Bukke, Abd. Rashid bin Mohd Yusoff, Ilhwan Ryu, Peng Gao, Alexander Chroneos, Mohammad Khaja Nazeeruddin, Maria Vasilopoulou   +13 more
wiley   +1 more source

Functional-Input Gaussian Processes with Applications to Inverse Scattering Problems

, 2023
Chih‐Li Sung, Wenjia Wang, Fioralba Cakoni, Isaac Harris, Ying Hung   +4 more
openalex   +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

Deep Neural Network-Oriented Indicator Method for Inverse Scattering Problems Using Partial Data

Mathematics
We consider the inverse scattering problem to reconstruct an obstacle using partial far-field data due to one incident wave. A simple indicator function, which is negative inside the obstacle and positive outside of it, is constructed and then learned ...
Yule Lin, Xiaoyi Yan, Jiguang Sun, Juan Liu   +3 more
doaj   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +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

Flexible Memory: Progress, Challenges, and Opportunities

Advanced Intelligent Discovery, EarlyView.
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan, Sen Chen, Minghui Guo, Ziliang Cui, Yujie Du, Jing Liu   +5 more
wiley   +1 more source