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Related Concept Videos

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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IR Spectrometers01:25

IR Spectrometers

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There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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IR Spectrum01:19

IR Spectrum

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
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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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Updated: Jun 13, 2025

Near-Infrared Temperature Measurement Technique for Water Surrounding an Induction-heated Small Magnetic Sphere
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Temperature distribution inversion in infrared multispectral imaging based on ensemble network.

Shuowen Yang, Hanlin Qin, Yang Dai

    Optics Letters
    |September 13, 2024
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    Summary
    This summary is machine-generated.

    Ensemble learning improves non-contact temperature measurement using infrared multispectral imaging. This method enhances radiometric temperature inversion accuracy and reliability, outperforming traditional algorithms and neural networks.

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    Area of Science:

    • Optics and Photonics
    • Artificial Intelligence
    • Thermodynamics

    Background:

    • Non-contact temperature measurement often uses multispectral imaging.
    • Radiometric temperature inversion is challenging due to unknown emissivity.
    • Existing methods, including neural networks, struggle with precision and reliability.

    Purpose of the Study:

    • To propose an ensemble learning approach for accurate temperature distribution inversion.
    • To enhance the precision and reliability of radiometric temperature measurement.
    • To develop a robust network architecture for infrared multispectral imaging.

    Main Methods:

    • Utilized ensemble learning with a base-learner and meta-learner network.
    • Established nonlinear relationships between temperature and multispectral measurements.
    • Validated the approach through simulations and real-world multispectral imaging experiments.

    Main Results:

    • Ensemble learning demonstrated superior inversion performance compared to other neural networks.
    • The proposed network showed high robustness against environmental noise.
    • Achieved enhanced accuracy and reliability in temperature distribution measurement.

    Conclusions:

    • Ensemble learning is a potent tool for multispectral imaging radiation temperature measurement.
    • The developed method offers a significant advancement in non-contact temperature sensing.
    • The approach provides a reliable solution for unknown emissivity challenges in temperature inversion.