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Related Experiment Video

Updated: Feb 8, 2026

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Published on: April 17, 2018

Distributed X-ray photon correlation spectroscopy data reduction using Hadoop MapReduce.

Faisal Khan¹, Suresh Narayanan¹, Roger Sersted²

¹X-ray Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Lemont, IL 60439, USA.

Journal of Synchrotron Radiation

|July 7, 2018

Summary

Multi-speckle X-ray photon correlation spectroscopy (XPCS) enables complex material analysis. A new Hadoop MapReduce system processes large XPCS datasets efficiently for near real-time results.

Keywords:

Advanced Photon Source MapReduce XPCS multi-speckle X-ray photon correlation spectroscopy parallel implementation

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Last Updated: Feb 8, 2026

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

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Area of Science:

Materials Science
Condensed Matter Physics
Data Science

Background:

Multi-speckle X-ray photon correlation spectroscopy (XPCS) is vital for studying material dynamics.
High-frame-rate detectors generate massive datasets, necessitating advanced processing.
Existing methods struggle with the scale and speed of data from modern XPCS experiments.

Purpose of the Study:

To develop and implement a scalable parallel computing solution for XPCS data analysis.
To enable near real-time processing of large XPCS datasets.
To enhance the capabilities of beamline 8-ID-I at the Advanced Photon Source.

Main Methods:

Implementation of multi-tau and two-time autocorrelation algorithms.
Utilizing the Hadoop MapReduce framework for distributed computing.
Application to data from beamline 8-ID-I at the Advanced Photon Source.

Main Results:

The developed system demonstrates excellent scalability with increasing data size.
Near real-time autocorrelation analysis is achieved for XPCS data.
The system has been operational and serving users for five years.

Conclusions:

The Hadoop MapReduce implementation provides an effective solution for processing large XPCS datasets.
This approach significantly improves the efficiency and accessibility of XPCS data analysis.
The system supports advanced research in complex material dynamics.