Results 181 to 190 of about 246,676 (283)

Prevalence and determinants of food addiction among Palestinian university students: a cross-sectional study. [PDF]

Eat Weight Disord
Hamdan M, Al-Amouri F, Jaber MM, Marbu D, Abed MT, Halayqa A, Al-Zabadi B, Qawasmeh E, Shaheen R, Badrasawi M.   +9 more
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

Questioning the validity of food addiction: a critical review. [PDF]

Front Behav Neurosci
Oliveira J, Bestetti GC, Stelmo IC, Antunes L, Vilella PR.   +4 more
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

Comparative Analysis of Food Addiction and Obesity: A Critical Review. [PDF]

Food Sci Nutr
Maqsood S, Ahmed F, Arshad MT, Ikram A, Abdullahi MA.   +4 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

Prevalence of food addiction and sex-specific correlates in a large sample of Iranian adults. [PDF]

Sci Rep
Sarvandani MN, Chen QW, Asadi M, Brunault P, LaFata EM, Burrows TL.   +5 more
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

Ultra-processed food addiction in a nationally representative sample of older adults in the USA. [PDF]

Addiction
Loch LK, Kirch M, Singer DC, Solway E, Roberts JS, Kullgren JT, Gearhardt AN.   +6 more
europepmc   +1 more source