Results 181 to 190 of about 672,639 (303)

Air-Coupled Ultrasonic Detection of Surface Roughening and Ink Wettability. [PDF]

Sensors (Basel)
Li G.
europepmc   +1 more source

Morphology‐Driven Electromechanical Performance of Graphene‐Based Electrofluids for Emerging Soft Electronic Systems

Advanced Materials Technologies, EarlyView.
Here, two types of electronic components are presented: a strain sensor and a stable resistor. Electrofluids properties are tuned to match these behaviors by selecting the type of filler. We show that the morphology of the filler together with its oxygen content are the key parameters to create electrical and mechanical networks with distinct responses.
Dominik S. Schmidt, Paolo Fortugno, Claudia‐F. López‐Cámara, Hartmut Wiggers, Lola González‐García   +4 more
wiley   +1 more source

Multi-facility virtual diagnostic for longitudinal phase space predictions. [PDF]

Sci Rep
Lundquist J, Björklund Svensson J, Dijkstal P, Mansten E, Penco G, Werin S, Curbis F.   +6 more
europepmc   +1 more source

Coupling of Er3+ ions and Plasmonic Modes in Vertical Nanocavities for Spectral Broadening in Telecom Bands

Advanced Materials Technologies, EarlyView.
Left: Illustration of the glass & nanostructures system and the NIR emission at 1.5 µm, where the colored spheres represent the Er3+ ions within the doped glass. Right: Representation of the Stark splitting of the 4I13/2 and 4I15/2 manifolds of the Er3+, illustrating how the plasmonic modes facilitate the emission from the broader Stark manifold ...
Gaston Lozano Calderón, Víctor Anthony García Rivera, Stéphan Cagnon, Souleymane Toubou Bah, Younès Messaddeq, Euclydes Marega Junior   +5 more
wiley   +1 more source

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

Advanced 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, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source

Quantifying sorptivity using the contact sponge method: an improved calculation method validated by classical capillary rise experiments and neutron radiography. [PDF]

Mater Struct
Derluyn H, Desarnaud J, Vandevoorde D, Dewanckele J, Boone MN, Peetermans S, Lehmann E, Viles H, Cnudde V.   +8 more
europepmc   +1 more source

Solid Particle‐Liquid Metal Mixtures for Robust High‐Current Interconnects in Stretchable Electronics and Soft Robotics

Advanced Materials Technologies, EarlyView.
Metallic microparticles dispersed in liquid metal interconnects mitigate electromigration‐induced failure by reconfiguring internal current density distributions. These fillers facilitate the growth of a robust, protective gallium oxide shell at elevated temperatures, shifting the failure threshold toward a thermal limit.
Ethan J. Krings, Calan Brant, Carson Emeigh, Donovan Vincent Jr., Ravi Tutika, Sangjin Ryu, Eric J. Markvicka   +6 more
wiley   +1 more source

Recovering Historical eDNA From Museum-Preserved Filter Feeders via Non-Destructive Metabarcoding. [PDF]

Mol Ecol Resour
Jeunen GJ, Mills S, Bailie M, Mauvisseau Q, Lamare M, Mariani S, Pearman W, Zavodna M, Treece J, Ferreira S, Gemmell NJ.   +10 more
europepmc   +1 more source

Agar‐Based Optical Sensing System With a Phosphorescent Grid Pattern for Measuring Stress Distributions of Root Growth Using the Sampling Moiré Method

Advanced Materials Technologies, EarlyView.
This paper presents an agar medium with a phosphorescent grid pattern that measures the stress distribution generated by growing plant roots. Using phosphorescence, the system optically separates the grid pattern from the root and extracts the grid deformation.
Gakuto Kagawa, Hidetoshi Takahashi
wiley   +1 more source

Editorial: Non-destructive quality assessment and intelligent packaging of agricultural products. [PDF]

Front Plant Sci
Li J.
europepmc   +1 more source