Results 201 to 210 of about 1,934,800 (291)

Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76)

Advanced Functional Materials, EarlyView.
Luminescent MOF‐76 materials based on Eu(III) and mixed Eu(III)/Y(III) show rapid and reversible changes in emission intensity in response to O2 with very short response times. The effect is based on triplet quenching of the linker ligands that act as photosensitizers. Average emission lifetimes of a few milliseconds turn out to be mostly unaffected by
Zhenyu Zhao, Christian Weinberger, Jakob Steube, Matthias Bauer, Martin Brehm, Michael Tiemann   +5 more
wiley   +1 more source

BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accurate Protein-Ligand Binding Affinity and Binding Pose Prediction. [PDF]

J Chem Inf Model
Sim J, Lee J.
europepmc   +1 more source

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

A Frequency- and Power-Dependent Semi-Analytical Model for Wideband RF Energy Harvesting Rectifiers. [PDF]

Micromachines (Basel)
Zuhur S.
europepmc   +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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Detecting Positive Selection by Modeling Structure Within Images of Genetic Variation. [PDF]

Genome Biol Evol
Amin MR, Arnab SP, Khan M, DeGiorgio M.
europepmc   +1 more source

Auto‐Generated Valence States in Electrocatalysts for Boosting Oxygen and Hydrogen Evolution Kinetics in Alkaline Water/Alkaline Seawater/Simulated Seawater/Natural Seawater

Advanced Functional Materials, EarlyView.
This review systematically highlights the latest achievements in mixed‐valence states relevant to hydrogen and oxygen evolution reactions, providing essential insights into future directions and methods for large‐scale practical implementation. This critical review is expected to provide an overview of recent advancements in diverse valence‐state metal
Jitendra N. Tiwari, Gokul Bhaskaran, Krishan Kumar, Yun Suk Huh, Young‐Kyu Han   +4 more
wiley   +1 more source

Insight into Genome-Wide Associations of Growth Trajectories Using a Hierarchical Non-Linear Mixed Model. [PDF]

Biology (Basel)
Zhang Y, Yang L, Cui W, Yang R.
europepmc   +1 more source

Nanoscale Mapping of Plasmonic Charge Transport in Nano‐Resonators Based on Resistive Switching Materials

Advanced Functional Materials, EarlyView.
In this strategy, a conductive nano‐probe is employed to induce nanoscale phase transitions and map the nanoscale conductivity and trap density of GST films. By utilizing the contrasting properties of phase‐change states, nano‐resonators are fabricated that exhibit plasmonic conduction and dramatically different transport characteristics.
Sunwoo Bang, Seunghyo Park, Shashank Shekhar, Yuhyeon Oh, Seunghun Hong   +4 more
wiley   +1 more source

Comparative evaluation of several models for forecasting hourly electricity use in a steel plant. [PDF]

Sci Rep
Gu F, Zhao Y.
europepmc   +1 more source