Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

3.0K
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...
3.0K
Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

873
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...
873

You might also read

Related Articles

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

Sort by
Same author

Diffraction anomalous fine structure pinpoints electrochemically active sites in robust heterojunctions.

Science advances·2026
Same author

High-Spin Pt Sites of Intermetallic Compound via Pinning Effect Boost Oxygen Reduction Performance.

Angewandte Chemie (International ed. in English)·2026
Same author

Inverse Design of Anthraquinone-Mimicking COFs via Electronic Fingerprints for Sacrificial-Agent-Free Photocatalytic H<sub>2</sub>O<sub>2</sub> Production under Visible Light.

Journal of the American Chemical Society·2026
Same author

Tuning and Shielding Iridium Active Sites Through Tungsten Electron Buffer for Oxygen Evolution Catalysis.

Angewandte Chemie (International ed. in English)·2026
Same author

Surface Gradient Doping Enables High-Capacity and Long-Life Manganese-Based Prussian Blue Cathodes for Sodium-Ion Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

Palladium-Doping-Enabled Interface Synergy for Superb Ampere Level Ammonia Electrosynthesis From Nitrate.

Angewandte Chemie (International ed. in English)·2026
Same journal

Accurate Density Functional Theory Forces for Charged Noncovalent Complexes.

The journal of physical chemistry letters·2026
Same journal

Dopant-Centered versus Intersite Synergistic Mechanisms in H<sub>2</sub> Dissociation on Single-Atom Alloys.

The journal of physical chemistry letters·2026
Same journal

Post-Translational Modification as an Allosteric Switch in Hsp90: How Dual Phosphorylation Locks Chaperone Complexes into Hyperstabilized States.

The journal of physical chemistry letters·2026
Same journal

LHCSR1 Functions as a Dimmer Switch for Light Harvesting.

The journal of physical chemistry letters·2026
Same journal

Sparse Linear Surrogates Match Neural Network Potentials on the SPICE Biomolecular Benchmark with Three Orders of Magnitude Smaller Training Sets.

The journal of physical chemistry letters·2026
Same journal

Solid-State NMR Quantification of Brønsted-Lewis Acid Site Cooperativity in Zeolites for Glucose Conversion.

The journal of physical chemistry letters·2026
See all related articles

Related Experiment Video

Updated: May 2, 2026

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis
14:53

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

Published on: February 3, 2018

6.7K

Machine-Learning-Assisted X-ray Spectroscopy Decoding an Elemental Segregation Mechanism in Pt-Ru Binary Alloy

Quan Zhou1, Sicong Qiao2, Hongwei Shou3

  • 1National Synchrotron Radiation Laboratory, Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230029, P. R. China.

The Journal of Physical Chemistry Letters
|April 30, 2026
PubMed
Summary
This summary is machine-generated.

We developed a machine learning framework to analyze X-ray absorption spectra, revealing platinum surface segregation in alloy nanoparticles. This method precisely decodes nanoscale compositional variations for advanced material design.

More Related Videos

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
07:24

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

Published on: May 10, 2021

5.8K
Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles
10:00

Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

Published on: July 5, 2016

14.2K

Related Experiment Videos

Last Updated: May 2, 2026

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis
14:53

In Situ Detection and Single Cell Quantification of Metal Oxide Nanoparticles Using Nuclear Microprobe Analysis

Published on: February 3, 2018

6.7K
Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
07:24

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis

Published on: May 10, 2021

5.8K
Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles
10:00

Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles

Published on: July 5, 2016

14.2K

Area of Science:

  • Materials Science
  • Nanotechnology
  • Catalysis

Background:

  • Elemental segregation in alloys dictates critical properties like catalytic performance and stability.
  • Understanding nanoscale compositional heterogeneity is key for designing advanced alloy catalysts.
  • Conventional X-ray absorption fine structure (XAFS) methods struggle with ensemble-averaging limitations.

Purpose of the Study:

  • To develop a machine learning-assisted framework for interpreting X-ray absorption near-edge structure (XANES) spectra.
  • To quantitatively deconvolve surface and bulk elemental signals in alloy nanoparticles.
  • To investigate surface segregation in platinum-ruthenium (Pt-Ru) alloy nanoparticles.

Main Methods:

  • Developed a cascade multiscale convolutional neural network to predict Pt coordination numbers from XANES spectra.
  • Employed computational-experimental spectral deconvolution to separate surface and bulk XANES profiles.
  • Validated the methodology using Pt-Ru alloy nanoparticles as a model system.

Main Results:

  • Successfully extracted surface-specific XANES profiles.
  • Confirmed significantly higher surface Pt-Pt coordination numbers compared to the bulk phase.
  • Observed a reverse trend in Pt-Ru coordination, verifying Pt surface segregation.

Conclusions:

  • The integrated machine learning, computational modeling, and XAFS approach enables atomistic-level decoding of physicochemical properties in nanostructured alloys.
  • This methodology offers a novel way to analyze localized physicochemical variations within nanomaterials.
  • Provides new paradigms for the rational design of advanced alloy catalysts.