Results 161 to 170 of about 1,413,450 (278)

Graphene‐Interfaced Stretchable Sweat Patch for Multiplexed Electrochemical Monitoring of IL‐6, Glucose, and Calcium Ions

open access: yesAdvanced Materials Technologies, EarlyView.
Flexible sweat sensor patch integrating graphene‑interfaced gold microelectrodes functionalized with bio‑receptors and ion‑selective membrane, coupled with a capillary‑driven microfluidic layer and portable potentiostat electronics for multiplexed monitoring of inflammatory, metabolic, and electrolyte biomarkers in microliter sweat volumes.
Roomia Memon   +4 more
wiley   +1 more source

Self‐Soldering, Stretchable and Self‐Healing Liquid Metal Elastomer Interconnections for Soft Electronics Platforms

open access: yesAdvanced Materials Technologies, EarlyView.
A self‐healing and electrically conductive liquid metal elastomer enables robust self‐soldering of surface mount devices for soft electronics. Photothermally activated conductive pathways, strong pressure‐sensitive adhesion, and stable package‐integrated contacts provide high conductivity and extreme stretchability.
Su Thiri San   +5 more
wiley   +1 more source

Fully 3D‐Printed Wave‐Wound Electromagnetic Motors

open access: yesAdvanced Materials Technologies, EarlyView.
This work presents the first fully 3D‐printed wave‐wound electromagnetic motors, which are created using conductive nanoparticle inks, carbon‐filled nylon polymers, and surface mount components. These motors can achieve a stall torque of 7.62N·mmA−1$7.62 \nobreakspace N{\cdot }mm A^{-1}$ and efficiency of 28.2 %, which approaches the performance of ...
Joseph Schwalbe   +4 more
wiley   +1 more source

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

open access: yesAdvanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov   +3 more
wiley   +1 more source

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