Results 191 to 200 of about 559,511 (294)

Tuning the Electronic Structure and Spin State of Fe─N─C Catalysts Using an Axial Oxygen Ligand and Fe Clusters for High‐Efficiency Rechargeable Zinc–Air Batteries

open access: yesAdvanced Functional Materials, EarlyView.
A FeN4─O/Clu@NC‐0.1Ac catalyst containing atomically‐dispersed FeN4─O sites (medium‐spin Fe2+) and Fe clusters delivered a half‐wave potential of 0.89 V for ORR and an overpotential of 330 mV at 10 mA cm−2 for OER in 0.1 m KOH. When the catalyst was used in a rechargeable Zn–air battery, a power density of 284.5 mW cm−2 was achieved with excellent ...
Yongfang Zhou   +8 more
wiley   +1 more source

A Tracer Diffusion Study of Diverse Photo‐Ionic Phenomena in Strontium Titanate

open access: yesAdvanced Functional Materials, EarlyView.
Two strong interfacial photo‐ionic effects are demonstrated for the model system SrTiO3 through the application of isotope exchange experiments: UV illumination is found to enhance the oxygen surface exchange coefficient by several orders of magnitude and to depress the surface space‐charge potential substantially.
David M. Schwenkel   +3 more
wiley   +1 more source

Reimagining Recruitment and Retention in Academic Plastic Surgery. [PDF]

open access: yesPlast Reconstr Surg Glob Open
Harbour P   +3 more
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

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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