Results 191 to 200 of about 721,781 (268)

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

Active Speaker Detection Using Audio, Visual, and Depth Modalities: A Survey

open access: gold
Siti Nur Aisyah Mohd Robi   +4 more
openalex   +1 more source

Universal Neuromorphic Element: NbOx Memristor with Co‐Existing Volatile, Non‐Volatile, and Threshold Switching

open access: yesAdvanced Functional Materials, EarlyView.
A W/NbOx/Pt memristor demonstrates the coexistence of volatile, non‐volatile, and threshold switching characteristics. Volatile switching serves as a reservoir computing layer, providing dynamic short‐term processing. Non‐volatile switching, stabilized through ISPVA, improves reliable long‐term readout. Threshold switching operates as a leaky integrate
Ungbin Byun, Hyesung Na, Sungjun Kim
wiley   +1 more source

Integrative Approaches for DNA Sequence‐Controlled Functional Materials

open access: yesAdvanced Functional Materials, EarlyView.
DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo   +4 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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