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Atomic Spectroscopy: Effects of Temperature01:27

Atomic Spectroscopy: Effects of Temperature

287
Atomization, converting samples into gas-phase atoms and ions, is essential for atomic spectroscopy. The flame temperature required for atomization affects the efficiency of the atomic spectroscopic methods by increasing the atomization efficiency and the relative population of the excited and ground states.
At thermal equilibrium, the relative populations of excited and ground state atoms can be estimated using the Maxwell–Boltzmann distribution. For example, an increase in temperature...
287
Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
144
Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

1.5K
Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
1.5K
Flame Photometry: Overview01:02

Flame Photometry: Overview

444
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...
444
Atomic Emission Spectroscopy: Instrumentation01:22

Atomic Emission Spectroscopy: Instrumentation

330
The instrumentation of atomic emission spectrometry (AES) involves various components, including atomization devices that convert samples into gas-phase atoms and ions. There are two main types of atomization devices: continuous and discrete atomizers.  Continuous atomizers, like plasmas and flames, introduce samples in a constant stream, while discrete atomizers inject individual samples using syringes or autosamplers. The most common discrete atomizer is the electrothermal atomizer.
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Related Experiment Video

Updated: May 31, 2025

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings
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Comparative analysis of data-driven models for spatially resolved thermometry using emission spectroscopy.

Ruiyuan Kang1, Dimitrios C Kyritsis2, Panos Liatsis3

  • 1Directed Energy Research Center, Technology Innovation Institute, Abu Dhabi, UAE.

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|January 24, 2025
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Summary
This summary is machine-generated.

This study introduces a novel data-driven methodology for spatially resolved temperature measurements using emission spectroscopy. Feature engineering combined with machine learning models effectively measures non-uniform temperature distributions, even with unknown gas concentrations.

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Area of Science:

  • Spectroscopy
  • Data Science
  • Thermodynamics

Background:

  • Line-of-sight emission spectroscopy has limitations in measuring temperature in non-homogeneous fields.
  • Spatially resolved temperature measurements are crucial for understanding complex systems.

Purpose of the Study:

  • To develop and evaluate data-driven models for spatially resolved temperature measurements using emission spectroscopy.
  • To compare the performance of feature engineering with classical machine learning against end-to-end convolutional neural networks (CNNs).

Main Methods:

  • Investigated two categories of data-driven methods: feature engineering with classical machine learning and CNNs.
  • Evaluated fifteen feature groups combined with fifteen classical machine learning models.
  • Assessed eleven CNN models for temperature distribution measurement.

Main Results:

  • Feature engineering combined with machine learning outperformed direct CNN application.
  • Physics-guided transformation, signal representation, and Principal Component Analysis proved most effective for feature extraction.
  • The light blender ensemble model, using extracted features, achieved the best performance (RMSE: 64.3, RE: 0.017, RRMSE: 0.025, R: 0.994).

Conclusions:

  • The proposed methodology, leveraging feature engineering and the light blender model, accurately measures non-uniform temperature distributions from low-resolution spectra.
  • This approach is effective even when species concentration distributions are unknown, overcoming limitations of traditional spectroscopy.