We investigate the convergence of the series arising in Mie theory for the solution of electromagnetic scattering by a sphere. In contrast with previous studies that focused only on the scattering cross section, we here consider a wide spectrum of relevant properties, including scattering, extinction, and absorption cross sections, complex scattering amplitudes (i.e., radiation profile), and near-field properties such as surface electric field and average surface field intensity. The scattering cross section is shown to exhibit the fastest convergence, indicating that existing convergence criteria based on this property are not suitable for the majority of other relevant characteristics computed from Mie theory. Criteria are therefore proposed for those properties.
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http://dx.doi.org/10.1364/AO.53.007224 | DOI Listing |
Materials (Basel)
January 2025
Institute of Microelectronics and Optoelectronics, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland.
A review of natural materials that exhibit negative permittivity or permeability, including gaseous plasma, metals, superconductors, and ferromagnetic materials, is presented. It is shown that samples made of such materials can store large amount of the electric (magnetic) energy and create plasmonic resonators for certain values of permittivity, permeability, and dimensions. The electric and the magnetic plasmon resonances in spherical samples made of such materials are analyzed using rigorous electrodynamic methods, and the results of the analysis are compared to experimental data and to results obtained with other methods.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Institute of Thermodynamics and Thermal Process Engineering, University of Stuttgart, Pfaffenwaldring 9, D-70569 Stuttgart, Germany.
Effective potential methods, obtained by applying a quantum correction to a classical pair potential, are widely used for describing the thermophysical properties of fluids with mild nuclear quantum effects. In case of strong nuclear quantum effects, such as for liquid hydrogen and helium, the accuracy of these quantum corrections deteriorates significantly, but at present no simple alternatives are available. In this work, we solve this issue by developing a new, three-parameter corresponding-states principle that remains applicable in the regions of the phase diagram where quantum effects become significant.
View Article and Find Full Text PDFChem Asian J
January 2025
Departamento de Química, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, 7800003, Santiago, Chile.
Plasmonic materials can be utilized as effective platforms to enhance luminescent signals of luminescent metal nanoclusters (LMNCs). Both surface enhanced fluorescence (SEF) and shell-isolated nanoparticle-enhanced fluorescence (SHINEF) strategies take advantage of the localized and increased external electric field created around the plasmonic metal surface when excited at or near their characteristic plasmonic resonance. In this context, we present an experimental and computational study of different plasmonic composites, (Ag) Ag@SiO and (Au) Au@SiO nanoparticles, which were used to enhance the luminescent signal of Au nanoclusters coated with glutathione (GSH) molecule (AuGSH NCs).
View Article and Find Full Text PDFJ Chem Phys
December 2024
Porelab, Department of Chemistry, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway.
Chapman-Enskog theory has long provided an accurate description of the transport properties of dilute gas mixtures. At elevated densities, revised Enskog theory (RET) provides a framework for describing the departure of the transport properties from their dilute-gas values. Various methods of adapting RET for the description of real fluids have been proposed in the literature.
View Article and Find Full Text PDFNanoscale Adv
January 2025
Department of Electrical and Electronic Engineering, University of Dhaka Dhaka-1000 Bangladesh
Tandem neural networks for inverse design can only make single predictions, which limits the diversity of predicted structures. Here, we use conditional variational autoencoder (cVAE) for the inverse design of core-shell nanoparticles. cVAE is a type of generative neural network that generates multiple valid solutions for the same input condition.
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