Results 231 to 240 of about 391,755 (343)

Photoresponsive Gas‐Permeable Membranes: Fundamentals, Innovations, and Prospects

open access: yesAdvanced Functional Materials, EarlyView.
Photoresponsive gas‐permeable membranes can be potentially used for smart packing, carbon capture, hydrogen purification, and optical gas valves due to their remote and non‐contact activation, precise spatial and temporal control, and reversible switching capabilities.
Zhuan Wang   +6 more
wiley   +1 more source

Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth

open access: yesAdvanced Functional Materials, EarlyView.
This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh   +4 more
wiley   +1 more source

Mechanically Robust Phase‐Change Multiscale‐Architected Metastructures Integrating Asymmetric MXene/T‐CNF Aerogel for Thermal Energy Storage and Electromagnetic Interference Shielding

open access: yesAdvanced Functional Materials, EarlyView.
A multiscale‐architected phase change material (PCM) composite combines latent heat storage, PCM leakage proof, directional thermal conduction, electromagnetic interference (EMI) shielding, and mechanical reinforcement via asymmetric MXene/cellulose aerogel and 3D‐printed metastructures, enabling effective thermal regulation, strong EMI shielding, and ...
Jiheon Kim   +9 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

open access: yesAdvanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore   +7 more
wiley   +1 more source

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