Optical amplification by the stimulated emission of Cs(6pP)-Ar atomic pairs, observed in pump-probe experiments over a ∼290 GHz-wide spectral region lying to the red of the Cs D line (852.1 nm), has been realized by photoexciting thermalized, ground state Cs-Ar atoms in the 834-849 nm wavelength interval. When the gain medium is pumped at the peak of the CsAr BΣ←XΣ transition at 836.7 nm, maximum gain occurs between 852.2 nm and 852.3 nm and >28% of the energy stored in the upper laser level is extracted with 8 ns (FWHM) probe pulses in a single pass. From the measured rate of saturation of the extracted pulse energy with increasing probe intensity, the product of γL and E, the saturation pulse energy, is measured directly to be 400 ± 20 µJ and the lower limit for the saturation intensity (I) of this amplifier is estimated to be 10 kW-cm at 852.2 nm. Circularly polarizing the optical pump beam increases the optical-to-optical conversion efficiency by 20%, and the storage lifetime of the upper laser level is observed from temporally-resolved gain spectra to be 5 ± 1 ns. Pump excitation spectra also reveal a significant contribution from Ar-Cs-Ar (3-body) photoassociation and suprathermal Ar atoms generated by the dissociation of the CsAr BΣ complex. Multipass-amplifier geometries with broad-bandwidth probe signals are expected to yield upper state energy extraction efficiencies above 50%. This alkali-rare gas amplifier demonstrates the efficiencies available with the storage of energy in, and optical extraction from, excited atomic collision pairs.
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http://dx.doi.org/10.1364/OE.390350 | DOI Listing |
Metal halide perovskites have unique luminescent properties that make them an attractive alternative for high quality light-emitting devices. However, the poor stability of perovskites with many defects and the long cycle time for the preparation of perovskite nanocomposites have hindered their production and application. Here, we prepared the perovskite mesostructures by embedding MAPbBr nanocrystals in the mesopores on the surface of silica nanospheres and mixing the nanospheres with silver nanowires and poly(methyl methacrylate) (PMMA), and further explored their optical properties.
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January 2025
Department of Ophthalmology and Visual Sciences, Research Institute of the McGill University Health Centre/Montreal Children's Hospital, 1001 Décarie Boulevard, Glen Site, Block E, Office #EM03238, Montréal, QC, H4A 3J1, Canada.
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Methods: Scotopic ERGs [Intensity: - 6.3 to 0.
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January 2025
Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, 450003, P. R. China.
The modulation of intrinsic magnetic properties of materials is of great importance for the exploration of new materials in the fields of information storage and spintronics. Herein, room-temperature ferromagnetic properties in BaZrO are successfully induced using supercritical CO. The highest saturation magnetization intensity of BaZrO is observed at 16 MPa, with a value of 0.
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January 2025
Research Centre for Biomedical Engineering, City St George's, University of London, London EC1V 0HB, UK.
The effect of skin pigmentation on photoplethysmography and, specifically, pulse oximetry has recently received a significant amount of attention amongst researchers, especially since the COVID-19 pandemic. With most computational studies observing overestimation of arterial oxygen saturation (SpO) in individuals with darker skin, this study seeks to further investigate the root causes of these discrepancies. This study analysed intensity changes from Monte Carlo-simulated reflectance PPG signals across light, moderate, and dark skin types at oxygen saturations of 70% and 100% in MATLAB R2024a.
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January 2025
School of Electronic and Information Engineering, Ankang University, Ankang 725000, China.
Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information.
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