Results 191 to 200 of about 61,380 (270)

Gas–Solid Interface‐Assisted Growth of Organic Semiconductor Single Crystals: Dimensions, Structures, Mechanisms, and Applications

open access: yesENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Gas–solid interface‐assisted growth strategies have unlocked precise control over crystal structure, morphology, dimension, and molecular packing. The obtained organic semiconductor single crystals represent the ideal candidates for high‐performance organic optoelectronic devices.
Tingyi Yan   +8 more
wiley   +1 more source

A Comprehensive Review of AI‐Powered Energy Systems

open access: yesEnergy Science &Engineering, EarlyView.
The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo   +5 more
wiley   +1 more source

Wide‐Bandgap Semiconductor‐Based Neuromorphic Computing

open access: yesInformation &Functional Materials, EarlyView.
Wide‐bandgap semiconductors enable robust, low‐power neuromorphic devices for extreme environments. This review outlines material advantages, device physics, integration, and future directions for next‐generation brain‐inspired computing. ABSTRACT Neuromorphic computing has emerged as a promising paradigm to overcome the energy inefficiency and data ...
Hongyu Tang   +6 more
wiley   +1 more source

Robustness of Spontaneous Polarization Through Nonlinear Directional Antagonism Without Global Inhibition

open access: yesJournal of the Chinese Chemical Society, EarlyView.
The conventional “local activation–global inhibition” (LAGI) models utilize mean‐field averaging inhibition to suppress distant activations. As the inhibition diminishes with distance, LAGI models struggle to achieve robust single‐axis polarity in large systems.
Chin‐Lin Guo, Chiao‐Yu Tseng
wiley   +1 more source

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles

open access: yesMacromolecular Rapid Communications, EarlyView.
This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig   +7 more
wiley   +1 more source

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