Results 191 to 200 of about 14,447,046 (396)

The General Factor of Personality (GFP) and parental support: testing a prediction from Life History Theory [PDF]

open access: green, 2012
Dimitri van der Linden   +4 more
openalex   +1 more source

High Dynamic Range Thin‐Film Resistive Flow Sensors for Monitoring Diverse Biofluids

open access: yesAdvanced Functional Materials, EarlyView.
Thin‐film resistive flow sensors with thermoresistive and piezoresistive mechanisms are developed for biofluid monitoring. Fabricated using biocompatible materials and laser micromachining, the devices achieve sub‐millimeter dimensions and a broad dynamic range.
Latifah Almulla   +2 more
wiley   +1 more source

Stochastically Generated Digital Twins of 3D Solid‐State Electrolyte Architecture

open access: yesAdvanced Functional Materials, EarlyView.
Digital Twins of random porous tape‐cast solid‐state battery architectures across µm to mm feature sizes from FIB‐SEM to X‐Ray µCT, respectively. Abstract Solid‐state lithium batteries (SSBs) have the potential to overcome conventional Li‐ion batteries in performance and safety.
Jonathan O'Neill   +3 more
wiley   +1 more source

Geometrically Templated, Ultra‐Lightweight and High Strength Soap Films from Lyotropic Liquid Crystalline Graphene Oxide/Polymer Composites

open access: yesAdvanced Functional Materials, EarlyView.
Shellular materials form spontaneously by dip coating the primitive triply periodic minimal surface (TPMS) wireframe in an aqueous solution of lyotropic liquid crystalline graphene oxide (GO) nanosheets mixed with polymers. Regulated by surface tension, GO nanosheets align on the polymer soap film as the stress builds up during drying.
Yinding Chi   +9 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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