Results 231 to 240 of about 4,260,606 (351)

Grain Boundary Space Charge Engineering of Solid Oxide Electrolytes: Model Thin Film Study

open access: yesAdvanced Functional Materials, EarlyView.
This study demonstrates unprecedented control of grain boundary electrical properties in solid electrolytes. Selective diffusion of cations through grain boundaries in thin films enables 12 orders of magnitude variation in ionic resistance, proving that systematic chemical modification of grain boundary electrical properties is feasible.
Thomas Defferriere   +5 more
wiley   +1 more source

Visual analysis of research hotspots and emerging trends in the field of newly graduated nurses. [PDF]

open access: yesFront Med (Lausanne)
Xu J   +8 more
europepmc   +1 more source

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

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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