Results 151 to 160 of about 186,193 (271)
Advanced Functional Materials, EarlyView.Non‐covalent protein–protein interactions mediated by SH3, PDZ, or GBD domains enable the self‐assembly of stable and biocatalytically active hydrogel materials. These soft materials can be processed into monodisperse foams that, once dried, exhibit enhanced mechanical stability and activity and are easily integrated into microstructured flow ...
Julian S. Hertel +5 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
Advanced Functional Materials, EarlyView.Sucrose‐derived porous carbon network (SPINE‐C) is formed within CNT fibers via FCCVD, bridging inter‐bundles of CNTs and increasing surface area through introduced microporosity. As a result, both mechanical and electrochemical properties are simultaneously enhanced, leading to a 2.8‐fold increase in the power density of a mechano‐electrochemical ...
Hocheol Gwac +9 more
wiley +1 more source
Device Integration Technology for Practical Flexible Electronics Systems
Advanced Functional Materials, EarlyView.Flexible device integration technologies are essential for realizing practical flexible electronic systems. In this review paper, wiring and bonding techniques critical for the industrial‐scale manufacturing of wearable devices are emphasized based on flexible electronics.
Masahito Takakuwa +5 more
wiley +1 more source
Compact and Simple RLWE Based Key Encapsulation Mechanism
, 2019 Erdem Alkım +2 more
openalex +2 more sources
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
Advanced Functional Materials, EarlyView.This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim +7 more
wiley +1 more source