Results 231 to 240 of about 1,590,994 (364)

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning

open access: yesAdvanced Functional Materials, EarlyView.
Tabular foundation model interrogates the synthetic likelihood of metal−organic frameworks. Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic ...
Xiaoyu Wu   +3 more
wiley   +1 more source

On Not Betraying Our Trainees, Especially Now. [PDF]

open access: yesAm J Respir Crit Care Med
Iwashyna TJ.
europepmc   +1 more source

Oral Dosed Organo‐Silica Nanoparticles Restore Glucose Homeostasis and β‐Cell Function in Diabetes Rats

open access: yesAdvanced Functional Materials, EarlyView.
An oral nanoplatform, MOP@T@D, which can maintain glucose homeostasis and restore islet β cells in diabetic rats is developed. It achieves efficient intestinal absorption and liver‐targeted delivery. The nanoparticle disintegrates only in response to hyperglycemia to release insulin on demand and provides antioxidant protection through selenoprotein ...
Chenxiao Chu   +14 more
wiley   +1 more source

Recent insights on direct democracy: Arguments, drivers, effects and conditions. [PDF]

open access: yesOpen Res Eur
Şimşek C   +4 more
europepmc   +1 more source

Photo‐Switching Thermal and Lithium‐Ion Conductivity in Azobenzene Polymers

open access: yesAdvanced Functional Materials, EarlyView.
Light‐responsive azobenzene polymers control thermal and ionic transport simultaneously through structural transitions. UV illumination disrupts π–π stacking, converting crystalline trans states to amorphous cis configurations. Thermal conductivity drops from 0.45 to 0.15 W·m−1·K−1 while Li+ diffusivity increases 100 fold. This dual transport switching
Jaeuk Sung   +7 more
wiley   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

open access: yesAdvanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee   +17 more
wiley   +1 more source

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