Results 181 to 190 of about 1,408,015 (336)

Correlative Super‐Resolution Imaging of Cellular Nanopores Facilitated by Transparent Polymer Waveguide Chips

open access: yesAdvanced Optical Materials, Volume 13, Issue 8, March 13, 2025.
Correlative super‐resolution optical imaging of the ultrastructure of rat liver sinusoidal endothelial cells (LSECs) across a large field of view (FOV) with 3D structured illumination microscopy (3D‐SIM) and single‐molecule localization microscopy (SMLM), facilitated by a transparent polymer photonic waveguide chip, is presented.
Surjendu Bikash Dutta   +9 more
wiley   +1 more source

Development of High‐Performance Multilayer Monochromators

open access: yesAdvanced Optical Materials, EarlyView.
Detailed insights into the optical and multilayer design of Double Multilayer Monochromators (DMM) are presented and fully characterized, including key optimization strategies and solutions to fabrication challenges. By integrating in‐house Ion Beam Figuring and a high uniformity multilayer coating process, the developed DMMs achieve high reflectivity,
Wadwan Singhapong   +11 more
wiley   +1 more source

Tides of Change in the Andaman and Nicobar Islands

open access: yesEcology, Economy and Society – The INSEE Journal, 2019
Meera Anna Oommen , Madhuri Ramesh
doaj   +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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