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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...
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Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Molecules possess discrete energy levels called quantum states. Unlike atoms, which have simpler energy levels, molecules possess additional rotational and vibrational energy levels.  Each energy level is separated by an energy gap, with the gaps between adjacent electronic, vibrational, and rotational levels varying significantly. The three types of energy levels in a diatomic molecule are shown in Figure 1.
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Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
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Data-driven ELNES/XANES analysis: predicting spectra, unveiling structures and quantifying properties.

Teruyasu Mizoguchi1

  • 1Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 113-8505, Japan.

Microscopy (Oxford, England)
|October 5, 2025
PubMed
Summary
This summary is machine-generated.

Data-driven methods revolutionize core-loss spectroscopy (electron energy loss near-edge structures/ELNES and X-ray absorption near-edge structures/XANES). These advanced techniques accelerate simulations, extract material properties, and enable faster materials discovery.

Keywords:
EELSELNESXAFSXANESdata drivenmachine learning

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Area of Science:

  • Materials Science
  • Spectroscopy
  • Computational Materials Science

Background:

  • Core-loss spectroscopies like ELNES and XANES are vital for materials characterization.
  • Traditional analysis relies on qualitative interpretation or reference spectra.
  • Limitations exist in quantitative analysis and predictive capabilities.

Purpose of the Study:

  • To review novel data-driven methodologies for ELNES/XANES analysis.
  • To highlight advancements beyond conventional spectral interpretation.
  • To showcase the potential for accelerated materials discovery.

Main Methods:

  • Application of machine learning (ML) and data-driven approaches to spectral data.
  • Development of methods for accelerating ELNES/XANES simulations.
  • Utilizing sensitivity analysis to interpret ML model predictions.

Main Results:

  • Data-driven methods enable quantitative extraction of radial distribution functions.
  • Multiple material properties can be quantified directly from spectral data.
  • Accelerated simulations and enhanced interpretability of ML models.

Conclusions:

  • Novel data-driven approaches significantly enhance ELNES/XANES analysis.
  • These methods facilitate deeper understanding and faster materials discovery.
  • The future points to automated, interpretable, and scalable spectroscopy for materials science.