The interest in plasmonic technologies surrounds many emergent optoelectronic applications, such as plasmon lasers, transistors, sensors and information storage. Although plasmonic materials for ultraviolet-visible and near-infrared wavelengths have been found, the mid-infrared range remains a challenge to address: few known systems can achieve subwavelength optical confinement with low loss in this range. With a combination of experiments and ab initio modelling, here we demonstrate an extreme peak of electron mobility in Dy-doped CdO that is achieved through accurate 'defect equilibrium engineering'. In so doing, we create a tunable plasmon host that satisfies the criteria for mid-infrared spectrum plasmonics, and overcomes the losses seen in conventional plasmonic materials. In particular, extrinsic doping pins the CdO Fermi level above the conduction band minimum and it increases the formation energy of native oxygen vacancies, thus reducing their populations by several orders of magnitude. The substitutional lattice strain induced by Dy doping is sufficiently small, allowing mobility values around 500 cm(2) V(-1) s(-1) for carrier densities above 10(20) cm(-3). Our work shows that CdO:Dy is a model system for intrinsic and extrinsic manipulation of defects affecting electrical, optical and thermal properties, that oxide conductors are ideal candidates for plasmonic devices and that the defect engineering approach for property optimization is generally applicable to other conducting metal oxides.
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Sci Rep
January 2025
Faculty of Frontiers of Innovative Research in Science and Technology (FIRST), Konan University, Chuo-ku, Kobe, 650-0047, Hyogo, Japan.
Environmental pollution caused by heavy metals are problems worldwide. In particular, pollution and poisoning by lead ions (Pb) continue to be common and serious problems. Hence, there is a need for a widely usable method to easily detect Pb from solutions containing organic materials from environmental water such as seas, ponds, etc.
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January 2025
Environmental and Occupational Hazards Control Research Center, Research Institute for Health Sciences and Environment, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
The magnetic material Nd2Fe14B is one of the strongest magnetic materials found in nature. The demand for the production of these nanoparticles is significantly high due to their exceptional properties. The aim of the present study is to synthesize magnetic nanoparticles of Nd2Fe14B using ethanol in the wet ball milling technique (WBMT).
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January 2025
School of Physics and Electronic Engineering, Guangzhou University, Guangzhou 510006, China.
Refractive index (RI) and temperature (T) are both critical environmental parameters for environmental monitoring, food production, and medical testing. The paper develops a D-shaped photonic crystal fiber (PCF) sensor to measure RI and T simultaneously. Its cross-sectional structure encompasses a hexagonal-hole lattice, with one hole selectively filled with toluene for temperature sensing.
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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.
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January 2025
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, 14115-194, Iran.
With the increasing global attention to deep learning and the advancements made in applying convolutional neural networks in electromagnetics, we have recently witnessed the utilization of deep learning-based networks for predicting the spectrum and electromagnetic properties of structures instead of traditional tools like fully numerical-based methods. In this study, a Convolutional Neural Network (CNN is proposed for predicting spoof surface plasmon polaritons, enabling the examination of the absorption spectrum of metallic multilevel-grating structures (MMGS) and designing various sensor devices and absorbers in the shortest time possible. To expedite the training process of this network, a semi-analytical method of rigorous coupled-wave analysis (RCWA) enhanced with the fast Fourier factorization (FFF) technique has been employed, significantly reducing the data generation time for training.
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