Results 231 to 240 of about 118,194 (287)

Hybrid Magnetoactive Soft Elastomers: Material Development Toward Application in Diaphragm Pumps

open access: yesAdvanced Materials Technologies, EarlyView.
Hybrid magnetoactive soft elastomeric membranes are developed by combining two different types of magnetic particles. The membranes exhibit distinct mechanical, magnetic and thermal properties owing to the unique characteristics of the two magnetic particles.
Somashree Mondal   +4 more
wiley   +1 more source

Hydrogel‐Based 3D‐Printable Stretchable Pressure Sensor

open access: yesAdvanced Materials Technologies, EarlyView.
We present a carbon‐black‐functionalized double‐network granular hydrogel (DNGH) pressure sensor capable of detecting pressures from 200 Pa, equivalent to a light finger touch, up to 500 kPa. The sensor exhibits signal drifts below 3.5% after 800 cycles and response times around 80 ms. Leveraging this broad sensing range, we 3D print this material into
Tianyu Yuan   +4 more
wiley   +1 more source

Editorial: Computer vision and human behaviour to recognize emotions. [PDF]

open access: yesFront Psychol
Caprì T   +3 more
europepmc   +1 more source

Pilot Randomised Controlled Trial of a Prototype Emotion Socialisation Parenting Program: An Ecological Momentary Intervention

open access: gold
Stefanie Ewald   +9 more
openalex   +1 more source

3D Printed Multimaterial Microfluidic Transistors

open access: yesAdvanced Materials Technologies, EarlyView.
We introduce a biocompatible, high resolution photopolymer resin that closely mimics the Young's Modulus (elasticity) and reversible stretchability (no hysteresis) of poly(dimethylsiloxane) (PDMS), enabling the fabrication of microfluidic transistors (i.e., microvalves capable of proportional amplification) by multimaterial stereolithography (mSLA ...
Alireza Ahmadianyazdi   +7 more
wiley   +1 more source

Advanced Design for Weakly Coupled Resonators by Automatic Active Optimization

open access: yesAdvanced Materials Technologies, EarlyView.
An Automatic Active Optimization (AAO) strategy integrates machine learning predictors and genetic algorithms in a closed‐loop workflow. By iteratively expanding its dataset with new discoveries, AAO overcomes the limits of conventional methods. This approach finds superior microstructural designs beyond the initial sample space. We demonstrate this on
Wei Yue   +8 more
wiley   +1 more source

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