Results 191 to 200 of about 33,661 (311)

Synthesis of Core–Shell Te@Se Quantum Dots and Their Broadband Photodetector Performance in Low Concentration Electrolytes

open access: yesLaser &Photonics Reviews, EarlyView.
Te@Se core–shell heterojunction quantum dots (QDs) are synthesized by a two‐step strategy via combining liquid‐phase exfoliation and epitaxial growth. As active materials in photoelectrochemical photodetectors, Te@Se QDs exhibit excellent photo‐response performance in low‐concentration electrolytes and deionized water.
Yiming Zhao   +8 more
wiley   +1 more source

Panel Data Stochastic Convergence Analysis of the Mexican Regions [PDF]

open access: yes
The stochastic convergence amongst Mexican Federal entities is analyzed in panel data framework. The joint consideration of cross-section dependence and multiple structural breaks is required to ensure that the statistical inference is based on ...
Vicente German-Soto   +1 more
core  

Bayesian Optimization of Grayscale Patterns for Layer‐Height Accuracy in Projection Multi‐Photon 3D Printing

open access: yesLaser &Photonics Reviews, EarlyView.
Bayesian optimization combined with in situ quantitative phase imaging enables autonomous correction of layer‐height deviations in projection multi‐photon lithography. By jointly tuning model parameters and grayscale exposure settings, the method achieves more uniform and accurate layers within 300 prints, offering a fast, data‐efficient route to ...
Jason E. Johnson, Xianfan Xu
wiley   +1 more source

Theory of Supercritical Coupling and Generalized Bound States in the Continuum

open access: yesLaser &Photonics Reviews, EarlyView.
We develop a general theory of supercritical coupling and generalized bound states in the continuum (gBICs), revealing how interference between radiative and absorptive channels enables quality factors beyond conventional material‐loss limits. The framework unifies non‐Hermitian mode coupling, causality‐driven reactive interactions, and interference ...
Sergio Balestrieri   +3 more
wiley   +1 more source

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles

open access: yesMacromolecular Rapid Communications, EarlyView.
This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig   +7 more
wiley   +1 more source

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