Results 121 to 130 of about 742,392 (286)
Advanced Engineering Materials, EarlyView.Viktoriia Shtefan, Thorgund Nemec, Ute Hempel, Annett Gebert and coworkers demonstrate that anodic treatment of Ti–Cu‐based metallic glass in a nontoxic pyrophosphate electrolyte forms a protective bilayered Ti/Zr‐oxide film enriched with Cu nanocrystals.
Viktoriia Shtefan +8 more
wiley +1 more source
An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials
Advanced Engineering Materials, EarlyView.An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut +16 more
wiley +1 more source
Advanced Functional Materials, EarlyView.In this study, the interplay of dipolar dynamics and ionic charge transport in MOF compounds is investigated. Synthesizing the novel structure CFA‐25 with integrated freely rotating dipolar groups, local and macroscopic effects, including interactions with Cs cations are explored.
Ralph Freund +6 more
wiley +1 more source
Photoswitchable Conductive Metal–Organic Frameworks
Advanced Functional Materials, EarlyView.A conductive material where the conductivity can be modulated remotely by irradiation with light is presented. It is based on films of conductive metal–organic framework type Cu3(HHTP)2 with embedded photochromic molecules such as azobenzene, diarylethene, spiropyran, and hexaarylbiimidazole in the pores.
Yidong Liu +5 more
wiley +1 more source
Advanced Functional Materials, EarlyView.This work explores Li‐substituted P2 layered oxides for Na‐ion batteries by crystallographic and electrochemical studies. The effect of lithium on superstructure orderings, on phase transitions during synthesis and electrochemical cycling and on the interplay of O‐ versus TM‐redox is revealed via various advanced techniques, including semi‐simultaneous
Mingfeng Xu +5 more
wiley +1 more source
Advanced Functional Materials, EarlyView.A pore tuning strategy to amplify the multi‐site MOF‐SO2 interactions is proposed to achieve an enhanced trace SO2 capture and chemiresistive sensing in highly stable isostructural DMOFs by annelating benzene rings. This work provides a facile strategy to achieve tailor‐made stable MOF materials for specific multifunctional applications.
Shanghua Xing +9 more
wiley +1 more source
Laser‐Induced Graphene from Waste Almond Shells
Advanced 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
Advanced Functional Materials, EarlyView.Zinc(II) coordination complexes with tunable aryloxy‐imine ligands exhibit controllable supramolecular self‐assembly into hierarchical fibrous structures. Coordination‐driven stacking, not π–π interactions, enables gelation, dynamic assembly/disassembly, and enhanced nanomechanical properties.
Merlin R. Stühler +10 more
wiley +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Advanced 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
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