Results 71 to 80 of about 1,343,925 (281)
Advanced Functional Materials, EarlyView.This work describes a novel approach to obtain composite submicrometric structures of nickel hexacyanoferrate integrating trigonal selenium with the aim of enhancing the catalytic efficiency in oxygen evolution reaction (OER). Interestingly, the nanostructures undergo a cube‐to‐sphere transition that optimizes surface properties, leading to superior ...
Edlind Lushaj +10 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Sustainable films with heterosymmetric structures, harmoniously integrating symmetry and asymmetry, are fabricated using cellulose nanocrystals and hydrophilic nanolignin via evaporation‐induced self‐assembly, which exhibit excellent multiple polarization optical properties.
Qun Song +7 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Free‐standing undulating graphene oxide (GO) modular tapered pipes fabricated based on the Marangoni effect and viscosity control are modular tubular structures that effectively utilize the lamination process between GO films. This novel tubular GO structure allows for the realization of various forms through the assembly of identical modules ...
Cheol‐Hwi Kim +8 more
wiley +1 more source
Advanced Functional Materials, EarlyView.A reproducible synthesis to control 3D/0D phase ratios via water‐tuned solvent–antisolvent methods is presented. Enhanced scintillation yield and ultrafast decay are achieved. Defect‐driven emission mechanisms are revealed through cathodoluminescence and radioluminescence, shedding light on the underexplored role of the 0D Cs4PbBr6 and mixed 0D/3D ...
Mario Calora +18 more
wiley +1 more source
Corrigendum to "On the dimension of an Abelian group" [Notes on Number Theory and Discrete Mathematics, 2022, Volume 27, Number 4, Pages 267-275] [PDF]
, 2022 Timo Tossavainen, Pentti Haukkanen
openalex +1 more source
Advanced Functional Materials, EarlyView.An ultra‐robust memristor based on SrTiO3‐CeO2 (S‐C) vertically aligned nanocomposite (VAN) achieving exceptional endurance of 1012 switching cycles via interface engineering. Artificial neural networks (ANNs) integrated with S‐C VAN memristors exhibit high training accuracy across multiple datasets.
Zedong Hu +12 more
wiley +1 more source
Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth
Advanced 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
Amorphous High Entropy Alloy Nanosheets Enabling Robust Li–S Batteries
Advanced Functional Materials, EarlyView.Amorphous ultrathin FeCoNiMoW high entropy alloy nanosheets are incorporated into the polypropylene separator of lithium‐sulfur batteries, enhancing their capacity, rate performance, and cycling stability. Abstract High‐entropy alloys (HEAs) show great potential for catalyzing complex multi‐step reactions, but optimizing their parameters, i.e ...
Ren He +20 more
wiley +1 more source
Advanced Functional Materials, EarlyView.A bespoke multilayer thin film configuration has been designed, which overcomes the material dependency of conventional isotope exchange Raman spectroscopy (IERS). This universal IERS methodology is efficient, non‐destructive and provides additional structural information and time resolution, which can be further extended to various isotopic elements ...
Zonghao Shen +7 more
wiley +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced 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