Results 141 to 150 of about 349,795 (327)

Microplastics from Wearable Bioelectronic Devices: Sources, Risks, and Sustainable Solutions

open access: yesAdvanced Functional Materials, EarlyView.
Bioelectronic devices (e.g., e‐skins) heavily rely on polymers that at the end of their life cycle will generate microplastics. For research, a holistic approach to viewing the full impact of such devices cannot be overlooked. The potential for devices as sources for microplastics is raised, with mitigation strategies surrounding polysaccharide and ...
Conor S. Boland
wiley   +1 more source

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

The MEET Approach: Securing Cryptographic Embedded Software Against Side Channel Attacks

open access: green, 2015
Giovanni Agosta   +3 more
openalex   +2 more sources

Side-channel Attack

open access: yesThe Journal of The Institute of Image Information and Television Engineers, 2010
Naofumi Homma, Takafumi Aoki
openaire   +2 more sources

Droplet Triboelectrification on Liquid‐Like Polymer Brushes

open access: yesAdvanced Functional Materials, EarlyView.
This work investigates the triboelectrification of water droplets on polymer brush‐coated surfaces exhibiting minimal contact line pinning. Such surfaces enable the systematic study of electrode patterning and controlled changes in droplet contact area.
Mohammad Soltani   +5 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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