Results 131 to 140 of about 10,388,998 (324)

Semi‐Transparent Organic Photodiodes with Near‐Infrared Detection Fabricated by Inkjet Printing

Advanced Electronic Materials, EarlyView.
This work shows the inkjet printing of semi‐transparent and opaque organic photodiodes that enable light detection in the near‐infrared regime. Their transparency and high detection speed make them ideal for applications in soft robotics, wearable devices, and light communication systems.
Luis Arturo Ruiz‐Preciado, Peter Krebsbach, Mervin Seiberlich, Gerardo Hernandez‐Sosa   +3 more
wiley   +1 more source

A hybrid deep boosting framework with adaptive label stabilization for SEM-based porosity estimation in fly-ash cement mortar

Frontiers in Artificial Intelligence
IntroductionAccurate measurements of porosity of cementitious matrices are critical in pre- Q7 dicting mechanical behavior, durability, and transportation processes.
Abdullah, Abdullah, Muhammad Ateeb Ather, Muhammad Ateeb Ather, José Luis Oropeza Rodríguez, Carlos Guzmán Sánchez-Mejorada   +5 more
doaj   +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

Adaptive Fractal Representation Learning for Data-Efficient Materials Property Prediction

IEEE Access
Predicting material properties is a cornerstone of materials discovery but remains challenging for geometrically complex crystals (e.g., those with large unit cells or low symmetry), where conventional descriptors fail to capture multiscale structural ...
Xue Qiyang, Wei Jiang
doaj   +1 more source

Epitaxial Growth of p‐Type β‐Ga2O3 Thin Films and Demonstration of a p–n Diode

Advanced Electronic Materials, EarlyView.
This study demonstrates p‐type conductivity in β‐Ga2O3 via Te–Mg co‐doping using MOCVD. The films show tunable hole concentrations up to 1.78×1017 cm−3, and a fabricated p–n diode exhibits rectifying behavior. Density functional theory reveals that Te introduces an intermediate band, lowering the Mg acceptor ionization energy and enabling p‐type ...
Chuang Zhang, Hanzhao Song, Chee Keong Tan   +2 more
wiley   +1 more source

Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization

Nanomaterials
The rational design of photoanode materials is pivotal for advancing photoelectrochemical (PEC) water splitting toward sustainable hydrogen production.
Xiongwei Liang, Shaopeng Yu, Bo Meng, Yongfu Ju, Shuai Wang, Yingning Wang   +5 more
doaj   +1 more source

Comparative Insights and Overlooked Factors of Interphase Chemistry in Alkali Metal‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review presents a comparative analysis of Li‐, Na‐, and K‐ion batteries, focusing on the critical role of electrode–electrolyte interphases. It especially highlights overlooked aspects such as SEI/CEI misconceptions, binder effects, and self‐discharge relevance, emphasizing the limitations of current understanding and offering strategies for ...
Changhee Lee, Zachary T. Gossage, Shinichi Kumakura, Shinichi Komaba   +3 more
wiley   +1 more source

Materials Informatics for Thermistor Properties of Mn–Co–Ni Oxides

Journal of Physical Chemistry C, 2023
Shogo Hashimura, Yudai Yamaguchi, Hayami Takeda, Naoto Tanibata, Masanobu Nakayama, Naohiro Niizeki, Takayuki Nakaya   +6 more
semanticscholar   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
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