Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Close-packed monolayers of colloidal dielectric spheres are used as near-field masks for parallel processing of various substrates with a single laser shot. Each of the spheres produces its own near-field maximum within the substrate. However, the deposited monolayers can contain certain defects either occasionally or intentionally. The missing spheres, i.e., vacancies, often occur. In this work, we show that the field maximum beneath the vacancy may be comparable or even stronger than that beneath the regular spheres in the array. The phenomenon of energy concentration by the vacancy is theoretically studied and explained in terms of the interference of the spherical harmonics of the scattered laser field provided by the spheres that surround the vacancy.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/OL.572160 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Study of Local Band Bending in -Channel InO Thin-Film Transistors under Gate and Drain Voltage Stress Using Hard X-ray Photoelectron Spectroscopy.
ACS Appl Mater Interfaces
September 2025
National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan.
In this study, we analyze InO thin-film transistors (InO-TFT) using synchrotron-based hard X-ray photoelectron spectroscopy (HAXPES) in conditions. A bottom-gate InO-TFT with a high- AlO gate dielectric, grown on thermally oxidized silicon (SiO/p-Si), was examined while operating at varying and . The results reveal that the In 3d core level binding energy varies along the horizontal channel length, driven by the potential gradient induced by .
View Article and Find Full Text PDFClose-packed monolayers of colloidal dielectric spheres are used as near-field masks for parallel processing of various substrates with a single laser shot. Each of the spheres produces its own near-field maximum within the substrate. However, the deposited monolayers can contain certain defects either occasionally or intentionally.
View Article and Find Full Text PDFZnO thin films made by sputtering: room temperature ferromagnetism due to Zn defects/vacancies?
RSC Adv
March 2025
Institute of Physics of Materials, v. v. i., Czech Academy of Sciences Žižkova 22 Brno 616 00 Czechia.
Unlike TiO and SnO, room temperature ferromagnetism in pristine ZnO films does not appear to originate from oxygen vacancies. In this study, we investigated thin films of ZnO deposited on -cut AlO by sputtering. The ZnO films were ferromagnetic, with a very high of about 800 K and were quite magnetically homogenous.
View Article and Find Full Text PDFQuantitatively Profiling the Evolution of Hydrogen Storage and Defect Healing Processes in Palladium at the Nanoscale.
ACS Nano
March 2025
Center for Condensed Matter Sciences, National Taiwan University, Taipei 10617, Taiwan.
Light elements or compounds with an average atomic number () of less than 10 are difficult to detect due to their weak interactions with electrons and photons. Here, we introduce a direct thermal absorbance measurement platform for scanning electron microscopy. The technique, named ZEM, is particularly sensitive to low materials, including hydrogen ( = 1) and vacancy ( = 0).
View Article and Find Full Text PDFStrategic Integration of Machine Learning in the Design of Excellent Hybrid Perovskite Solar Cells.
J Phys Chem Lett
January 2025
College of Chemistry and Materials Science, Hebei University, Baoding 071002, P. R. China.
The photoelectric conversion efficiency (PCE) of perovskites remains beneath the Shockley-Queisser limit, despite its significant potential for solar cell applications. The present focus is on investigating potential multicomponent perovskite candidates, particularly on the application of machine learning to expedite band gap screening. To efficiently identify high-performance perovskites, we utilized a data set of 1346 hybrid organic-inorganic perovskites and employed 11 machine learning models, including decision trees, convolutional neural networks (CNNs), and graph neural networks (GNNs).
View Article and Find Full Text PDF