Results 231 to 240 of about 487,374 (296)

Developing Biodegradable Films from Mango (<i>Mangifera indica</i>) Starch and Extract: A Rheological and Physical Study. [PDF]

open access: yesGels
Lastra-Ripoll SE   +5 more
europepmc   +1 more source

Reduced Variability in Threshold Switches Using Heterostructures of SiOx and Vertically Aligned MoS2

open access: yesAdvanced Electronic Materials, EarlyView.
Heterostructures of SiOx and vertically aligned MoS2 exhibit reliable threshold switching by guiding Ag ion migration through van der Waals gaps. Compared to SiOx‐only devices, these heterostructures demonstrate higher switching voltages, faster switching speeds, and reduced variability.
Jimin Lee   +9 more
wiley   +1 more source

Pressure‐Induced Structural and Magnetic Evolution in Layered Antiferromagnet YbMn2Sb2

open access: yesAdvanced Electronic Materials, EarlyView.
Pressure tunes the delicate balance between structure, magnetism, and electronic states in quantum materials. In YbMn2Sb2, high‐pressure X‐ray and neutron diffraction reveal a trigonal‐to‐monoclinic transition near 3.5 GPa, accompanied by unconventional magnetic ordering.
Mingyu Xu   +9 more
wiley   +1 more source

Synergies in Agricultural Biodiversity Conservation: Decomposing the Interaction Between Nature Parks and Agri‐Environment Schemes

open access: yesApplied Economic Perspectives and Policy, EarlyView.
ABSTRACT Understanding how policy instruments with overlapping goals interact is crucial for leveraging their synergies. This study explores the mechanisms for regional nature parks (a form of protected areas that impose no restrictions on agriculture) to enhance the adoption of biodiversity‐conserving agri‐environment schemes (AES) in Switzerland ...
Yanbing Wang   +3 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

open access: yesAdvanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

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