Results 171 to 180 of about 135,513 (316)

Thermo‐Optical Analysis of Surface‐ and Volume‐Additivated Polymer Powders for Near‐Infrared Laser Powder Bed Fusion

open access: yesAdvanced Photonics Research, EarlyView.
Precipitated PA11 powders for laser powder bed fusion are additivated with small amounts of carbon black nanoparticles on and in the particles. The position of the nanoparticles has a strong impact on dispersion, crystallization, and thermal oxidation.
Alexander Sommereyns   +4 more
wiley   +1 more source

Optimizing Single‐Cell Measurement Using Dynamic Atomic Force Microscopy

open access: yesAdvanced Photonics Research, EarlyView.
This study optimizes optical detection sensitivity in atomic force microscopy for high‐frequency single‐cell imaging in liquid environments. By analyzing laser positioning on microcantilevers, it enhances measurement accuracy and image resolution. Advanced image analysis techniques assess quality, offering guidelines for high‐resolution imaging of ...
Indrianita Lionadi   +4 more
wiley   +1 more source

Intrinsically Soft Implantable Electronics for Long‐term Biosensing Applications

open access: yesAdvanced Sensor Research, EarlyView.
Intrinsically soft implantable biosensors address the mechanical mismatch of conventional rigid implants, improving biocompatibility and stability. This review explores soft encapsulation matrices, stretchable conductors, implantation strategies, and chronic fixation techniques.
Su Hyeon Lee   +5 more
wiley   +1 more source

Intelligent Eye Tracker Integrated with Cylindrical Capacitive Sensors for Chronic Fatigue Assessment

open access: yesAdvanced Sensor Research, EarlyView.
A wearable capacitive eye tracker for chronic fatigue assessment is presented, utilizing cylindrically shaped capacitive sensors made of a carbon nanotube‐paper composite. By integrating a novel fatigue‐induction protocol with machine learning, the device achieves 0.75‐sensitivity and 0.73‐specificity, providing a practical alternative to existing ...
Tianyi Li   +6 more
wiley   +1 more source

Data‐Driven Lithium Salt Design for Long‐Cycle Lithium Metal Battery

open access: yesAdvanced Sustainable Systems, EarlyView.
This study introduces a data‐driven model to predict Coulombic efficiency and lithium thickness evolution in lithium metal batteries using electrolyte composition and DFT‐derived descriptors. Machine learning models, especially XGBoost and random forest, reduce prediction error by over 50% compared to models using only structural information.
Un Hwan Lee   +4 more
wiley   +1 more source

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