Results 161 to 170 of about 1,227,158 (294)

Electroactive Metal–Organic Frameworks for Electrocatalysis

open access: yesAdvanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska   +7 more
wiley   +1 more source

Photoswitching Conduction in Framework Materials

open access: yesAdvanced Functional Materials, EarlyView.
This mini‐review summarizes recent advances in state‐of‐the‐art proton and electron conduction in framework materials that can be remotely and reversibly switched on and off by light. It discusses the various photoswitching conduction mechanisms and the strategies employed to enhance photoswitched conductivity.
Helmy Pacheco Hernandez   +4 more
wiley   +1 more source

Biomimetic Iridescent Skin: Robust Prototissues Spontaneously Assembled from Photonic Protocells

open access: yesAdvanced Functional Materials, EarlyView.
Uniform nanoparticles are induced to form arrays (photonic crystals) in the cores of biopolymer capsules, endowing these ‘protocells’ with structural color. These protocells are then assembled into large self‐standing objects, i.e., prototissues, with robust mechanical properties as well as iridescent optical properties.
Medha Rath   +6 more
wiley   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

open access: yesAdvanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
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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