Results 171 to 180 of about 932,989 (354)

Implementation of Drug‐Induced Rhabdomyolysis and Acute Kidney Injury in Microphysiological System

open access: yesAdvanced Functional Materials, EarlyView.
A modular Muscle–Kidney proximal tubule‐on‐a‐chip integrates 3D skeletal muscle and renal proximal tubule tissues to model drug‐induced rhabdomyolysis and acute kidney injury. The coculture system enables dynamic tissue interaction, functional contraction monitoring, and quantification of nephrotoxicity, revealing drug side effect‐induced metabolic ...
Jaesang Kim   +4 more
wiley   +1 more source

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

open access: yesAdvanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva   +9 more
wiley   +1 more source

Root mean square of error values.

open access: green
Yiliang Cao (22103812)   +1 more
openalex   +1 more source

Device Integration Technology for Practical Flexible Electronics Systems

open access: yesAdvanced 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

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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