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相关概念视频

Photoluminescence: Applications01:14

Photoluminescence: Applications

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Photoluminescence offers a wide range of applications due to its inherent sensitivity and selectivity. This technique allows for both direct and indirect analyses of the analyte. Direct quantitative analysis is possible when the analyte exhibits a favorable quantum yield for fluorescence or phosphorescence. However, an indirect analysis may be feasible if the analyte is not fluorescent or phosphorescent, or if the quantum yield is unfavorable. Indirect methods include reacting the analyte with...
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Flame Photometry: Overview01:02

Flame Photometry: Overview

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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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Updated: Jul 4, 2025

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由支向量机驱动的发光温度测量:一种精确的热传感策略.

Wei Xu, Chenglong Xu, Junqi Cui

    Optics letters
    |February 1, 2024
    PubMed
    概括

    机器学习提高了发光温度计的精度. 支持矢量机 (SVM) 显著改善了使用Gd3Ga5O12:Er3+-Yb3+的非接触温度测量,其性能优于传统方法.

    科学领域:

    • 材料科学 材料科学 材料科学
    • 频谱学是一种光谱学.
    • 机器学习 机器学习

    背景情况:

    • 发光温度计提供非接触式温度传感,但面临着精度和可靠性的挑战.
    • 现有的方法,如发光强度比 (LIR) 和多重线性回归 (MLR),在准确性和稳定性方面存在局限性.

    研究的目的:

    • 使用机器学习开发用于发光温度测量的先进的热传感策略.
    • 为了比较支持矢量机 (SVM) 与温度测量的LIR和MLR方法的性能.

    主要方法:

    • 作为发光传感材料使用的加多花,添加了和伊特 (Gd3Ga5O12:Er3+-Yb3+).
    • 采用支向量机 (SVM) 来与温度对比上升转换的排放光谱.
    • 将SVM性能与LIR和MLR方法在广泛的温度范围 (303-853K) 中进行比较.

    主要成果:

    • 与LIR (3.75 K, 1.37 K) 和MLR (1.82 K, 0.43 K) 相比,SVM方法实现了显著较低的最大 (0.38 K) 和平均 (0.12 K) 误差.
    • 基于SVM的温度计证明了对环境干扰引起的光谱扭曲的高稳定性,而LIR和MLR则被证明是无效的.

    结论:

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  • 支持向量机 (SVM) 是一个强大的工具,用于推进发光温度计.
  • 这种机器学习方法可以实现非常精确,可靠和强大的非接触式温度测量.