Results 211 to 220 of about 472,137 (304)

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

Mechanical Properties of Architected Polymer Lattice Materials: A Comparative Study of Additive Manufacturing and CAD Using FEM and µ‐CT

open access: yesAdvanced Functional Materials, EarlyView.
This study examines how pore shape and manufacturing‐induced deviations affect the mechanical properties of 3D‐printed lattice materials with constant porosity. Combining µ‐CT analysis, FEM, and compression testing, the authors show that structural imperfections reduce stiffness and strength, while bulk material inhomogeneities probably enhance ...
Oliver Walker   +5 more
wiley   +1 more source

3D‐Printed Sulfur‐Derived Polymers With Controlled Architectures for Lithium‐Sulfur Batteries

open access: yesAdvanced Functional Materials, EarlyView.
Rheology‐guided formulation design for direct ink writing enables the fabrication of 3D sulfur copolymer cathodes with controlled architectures for lithium‐sulfur batteries. The printed electrodes exhibit multiscale porosity and high sulfur utilization, delivering enhanced electrochemical performance compared to conventional cast electrodes.
Bin Ling   +7 more
wiley   +1 more source

Milling Parameters and Quality of Machined Surface of Wire Arc Additive Manufactured AISI 321 Steel. [PDF]

open access: yesMaterials (Basel)
Zhang Q   +6 more
europepmc   +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

Spinal Compression Fracture Mechanisms in Mountain Biking: An Accident Reconstruction Approach. [PDF]

open access: yesGlobal Spine J
Bonte S   +5 more
europepmc   +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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