Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

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).
There are three main types of inductively coupled plasma atomic emission spectroscopy (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A generic solvothermal defluorination method for carboxylate and sulfonate PFAS.

Chemical science·2026

Same author

PVC Waste to Fuel: Pyrolysis Oil Upgrading through Catalytic Hydrodechlorination.

Energy & fuels : an American Chemical Society journal·2026

Same author

Equilibrium and kinetic aspects of Am(III)/Eu(III) pair extraction by a bipyridine-based N,O-donor ligand in different solvents.

Physical chemistry chemical physics : PCCP·2026

Same author

Heterogeneous Transfer Dehydrogenation for Catalytic Upcycling of Polyethylene.

ChemSusChem·2026

Same author

DeepFit: Physically and Chemically Informed XAS-Structure Fitting Made Simple.

The journal of physical chemistry letters·2026

Same author

Selective Carbocation Functionalization by Catalytic Transchalcogenation Reactions.

Angewandte Chemie (International ed. in English)·2026

Search research articles

Related Experiment Video

Updated: Oct 25, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

Revisiting the Extended X-ray Absorption Fine Structure Fitting Procedure through a Machine Learning-Based Approach.

A Martini^1,2, A L Bugaev^1,3, S A Guda^1,4

¹The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia.

The Journal of Physical Chemistry. A

|August 5, 2021

Summary

A new machine learning method precisely analyzes extended X-ray absorption fine structure (EXAFS) spectra. This approach directly determines 3D atomic structures, overcoming limitations of traditional fitting methods.

More Related Videos

Quantifying X-Ray Fluorescence Data Using MAPS

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

$Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples$

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

Related Experiment Videos

Last Updated: Oct 25, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

Quantifying X-Ray Fluorescence Data Using MAPS

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

$Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples$

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples

Published on: June 19, 2018

Area of Science:

Materials Science
Spectroscopy
Computational Chemistry

Background:

Extended X-ray Absorption Fine Structure (EXAFS) spectroscopy is a powerful tool for determining local atomic structure.
Traditional EXAFS analysis relies on complex parametrization and fitting procedures, which can be challenging due to nonlinear dependencies.

Purpose of the Study:

To develop a novel, inverse machine learning-based algorithm for EXAFS spectral analysis.
To precisely account for nonlinear geometry dependence in photoelectron scattering paths.
To directly relate determined parameters to 3D atomic structure without complex parametrization.

Main Methods:

An inverse machine learning algorithm was developed for EXAFS data analysis.
The algorithm accounts for nonlinear geometry dependence of photoelectron backscattering phases and amplitudes.
Single and multiple scattering paths were analyzed.

Main Results:

The developed approach precisely analyzes EXAFS spectra.
Parameters determined by the algorithm directly correlate with 3D atomic structure.
The method demonstrated advantages over classical fitting approaches.

Conclusions:

The inverse machine learning approach offers a precise and direct method for EXAFS analysis.
This technique simplifies the determination of 3D atomic structures from EXAFS data.
The study highlights the potential of machine learning in materials characterization.