Results 241 to 250 of about 108,699 (341)

Investigation on inkjet printing for electromagnetic compatibility application

open access: green, 2017
Melinda Hartwig   +3 more
openalex   +1 more source

Recyclable and Binder‐Free EGaIn–Carbon Liquid Metal Composite: A Sustainable Approach for High‐Performance Stretchable Electronics, Thermal‐Interfacing and EMI‐Shielding

open access: yesAdvanced Materials Technologies, EarlyView.
Binder‐free EGaIn–CB composite deliver printable, recyclable liquid‐metal conductors without sintering or polymer binders. Only 1.5 wt% CB yields shear‐thinning, high‐viscosity rheology, ∼60% bulk EGaIn conductivity, robust stretchability, high thermal conductivity, and strong EMI shielding (35 → 70 dB at 100% strain).
Elahe Parvini   +4 more
wiley   +1 more source

A Multi‐Resonant Tunable Fabry‐Pérot Cavity for High Throughput Spectral Imaging

open access: yesAdvanced Optical Materials, Volume 13, Issue 8, March 13, 2025.
This study introduces a multi‐resonant tunable Fabry‐Pérot filter for high throughput spectral imaging. With a spectral resolution of 10 nm and a switching time of 23 ms, this compact and low‐cost device possesses better spectral imaging accuracy in poor light conditions while maintaining over six times higher optical throughput than standard liquid ...
Xiao Wu   +5 more
wiley   +1 more source

Sub‐THz Multifunctional Metasurfaces for Independent Transmission or Reflection Phase Manipulation

open access: yesAdvanced Optical Materials, EarlyView.
This article presents a rigorous analysis, design, and characterization of a novel sub‐THz metalens and reflective metasurface. Unlike bulky optical systems, the proposed thin, planar metasurface enables dual‐band full‐space wavefront control, via low‐loss transmission and reflection phase tuning, offering compact integration and improved ...
Bilal Ouardi   +5 more
wiley   +1 more source

Inverse Design in Nanophotonics via Representation Learning

open access: yesAdvanced Optical Materials, EarlyView.
This review frames machine learning (ML) in nanophotonics through a classification based on where ML is applied. We categorize methods as either output‐side, which create differentiable surrogates for solving Maxwell's partial differential equations (PDEs), or input‐side, which learn compact representations of device geometry.
Reza Marzban   +2 more
wiley   +1 more source

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