Results 171 to 180 of about 3,224,686 (322)

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

Reconstruction of few-group homogenized cross section by kernel method and active learning

, 2019
Ea. Szames, Daniele Tomatis, Kaïs Ammar, J. M. Rico Martínez   +3 more
openalex   +1 more source

Image retrieval over networks: Ant algorithm for long term active learning

, 2008
David Picard, Arnaud Revel, Matthieu Cord   +2 more
openalex   +2 more sources

Using Tiny Research Assignments to Support Active Learning. [PDF]

J Law Med Ethics
Sawicki N.
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning [PDF]

N. M. Harrison, Nathan Wallace, Salah Sukkarieh   +2 more
openalex   +1 more source

Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting [PDF]

, 2019
Remy Dou, Justyna P. Zwolak
openalex   +1 more source

A Multitask Active Learning Framework with Probabilistic Modeling for Multi-Species Acute Toxicity Prediction. [PDF]

Molecules
Han T, Wang J, Zhao Y, Lin Y, Yu L, He S, Zan P, Bo X.   +7 more
europepmc   +1 more source

Chemoselective Sequential Polymerization: An Approach Toward Mixed Plastic Waste Recycling

Advanced Functional Materials, EarlyView.
Inspired by biological protein metabolism, this study demonstrates the closed‐loop recycling of mixed synthetic polymers via ring‐closing depolymerization followed by a chemoselective sequential polymerizations process. The approach recovers pure polymers from mixed feedstocks, even in multilayer formats, highlighting a promising strategy to overcome a
Gadi Slor, Youwei Ma, Hyunwoo Park, Tae‐Lim Choi, Damien Guironnet, Francesco Stellacci   +5 more
wiley   +1 more source