Results 151 to 160 of about 156,172 (278)

Laser‐Induced Graphene from Waste Almond Shells

open access: yesAdvanced Functional Materials, EarlyView.
Almond shells, an abundant agricultural by‐product, are repurposed to create a fully bioderived almond shell/chitosan composite (ASC) degradable in soil. ASC is converted into laser‐induced graphene (LIG) by laser scribing and proposed as a substrate for transient electronics.
Yulia Steksova   +9 more
wiley   +1 more source

Positive‐Tone Nanolithography of Antimony Trisulfide with Femtosecond Laser Wet‐Etching

open access: yesAdvanced Functional Materials, EarlyView.
A butyldithiocarbamic acid (BDCA) etchant is used to fabricate various micro‐ and nanoscale structures on amorphous antimony trisulfide (a‐Sb2S3) thin film via femtosecond laser etching. Numerical analysis and experimental results elucidate the patterning mechanism on gold (reflective) and quartz (transmissive) substrates.
Abhrodeep Dey   +12 more
wiley   +1 more source

Selective and Precise Editing of Digital Polymers Through Parallel or Series Toehold‐Mediated Strand Displacement

open access: yesAdvanced Functional Materials, EarlyView.
A sequence‐encoded supramolecular construct containing two accessible toeholds is developed herein for enabling multiple editing operations. By introducing specific input strands, it is possible to selectively erase or rewrite digital content through parallel or series toehold‐mediated strand displacement (PTMSD or STMSD).
Jakub Ossowski   +3 more
wiley   +1 more source

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

On‐Surface Indigo‐Based Bimolecular Coordination Networks with Programmable Regular or Vitreous Structure

open access: yesAdvanced Functional Materials, EarlyView.
A previously unreported coordination motif stabilising single Fe atoms by indigo chelation and pyridyl coordination on Au(111) has been revealed. By using planar tritopic pyridyl linkers (TPyB), extended 2D porous networks of indigo3(TPyB)2Fe6 form. These networks can be crystalline or vitreous and offer an environment where individual coordination ...
Hongxiang Xu   +9 more
wiley   +1 more source

Implementation of Drug‐Induced Rhabdomyolysis and Acute Kidney Injury in Microphysiological System

open access: yesAdvanced Functional Materials, EarlyView.
A modular Muscle–Kidney proximal tubule‐on‐a‐chip integrates 3D skeletal muscle and renal proximal tubule tissues to model drug‐induced rhabdomyolysis and acute kidney injury. The coculture system enables dynamic tissue interaction, functional contraction monitoring, and quantification of nephrotoxicity, revealing drug side effect‐induced metabolic ...
Jaesang Kim   +4 more
wiley   +1 more source

Supraparticles Composed of Graphitic Carbon Nitride Nanoparticles and Silica‐Supported Horseradish Peroxidase as Customizable Hybrid Catalysts for Photo‐Biocatalytic Cascade Reactions in Continuous Flow

open access: yesAdvanced Functional Materials, EarlyView.
Herein presented supraparticles combine the nanoparticulate photocatalyst graphitic carbon nitride with the enzyme horseradish peroxidase, which is immobilized on silica nanoparticles. In an optimized compatibility range, both catalysts operate effectively within the hybrid supraparticles and catalyze a cascade reaction consisting of the photocatalytic
Bettina Herbig   +11 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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