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

$X-ray Diffraction of Biological Samples$ 01:10

X-ray Diffraction of Biological Samples

X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are scattered by the electron clouds around the sample atoms. The X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...

X-ray Crystallography

X-ray Crystallography

The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...

You might also read

Related Articles

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

Sort by

Same author

Risk Factors for Diabetic Foot Ulcers and the Role of Thermal Imaging in Early Detection.

Cureus·2026

Same author

Fine Characterization of Co/Fe-Based Materials: Insights into the Influence of Cation Ratios Between 2/2 and 10/2 on Obtaining Layered Double Hydroxides.

Materials (Basel, Switzerland)·2026

Same author

An Evaluation of the Effect of Dimple Insoles on Foot Temperature in Diabetic Patients.

Sensors (Basel, Switzerland)·2025

Same author

Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025

Same author

Probing the Redox Reactivity of a Reduced Nontronite: A Quick XAS Operando Study.

Environmental science & technology·2025

Same author

Characterization of Nanoparticles in Ethanolic Suspension Using Single Particle Inductively Coupled Plasma Mass Spectrometry: Application for Cementitious Systems.

ACS omega·2024

Same journal

Towards light-coupled sample preparation for time-resolved cryoEM studies.

IUCrJ·2026

Same journal

Cryo-EM analysis of cooperative conformational changes in the SARS-CoV-2 spike protein trimer.

IUCrJ·2026

Same journal

Towards time-resolved MicroED grid preparation using mix-and-inject gas dynamic virtual nozzles.

IUCrJ·2026

Same journal

How cryoEM has advanced our understanding of bacteriophages and bacteriocins targeting Clostridioides difficile.

IUCrJ·2026

Same journal

CryoEM structures reveal allosteric regulation of the catalytic activity of the multi-protein human MAT enzyme complexes.

IUCrJ·2026

Same journal

Cryo-EM-guided subtractive optimization of a novel VCP/p97 inhibitor.

IUCrJ·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2025

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

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

Published on: June 19, 2018

Phase quantification using deep neural network processing of XRD patterns.

Titouan Simonnet¹, Sylvain Grangeon², Francis Claret²

¹Institut Denis Poisson, Université d'Orléans, Université de Tours, CNRS, France.

|August 12, 2024

Summary

This summary is machine-generated.

This study introduces a novel neural network (NN) for automated mineral phase identification and quantification using powder X-ray diffraction (XRD) data. The method significantly speeds up analysis, especially for large datasets, improving accuracy and efficiency.

Keywords:

calcite composite materials computational modeling deep neural network dolomite gibbsite hematite powder X-ray diffraction

More Related Videos

$Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene$

Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

Published on: August 22, 2017

Quantifying X-Ray Fluorescence Data Using MAPS

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

Related Experiment Videos

Last Updated: Jun 17, 2025

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

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

Published on: June 19, 2018

$Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene$

Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene

Published on: August 22, 2017

Quantifying X-Ray Fluorescence Data Using MAPS

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

Area of Science:

Materials Science
Geology
Computational Science

Background:

Accurate mineral identification and quantification are crucial for predicting material properties.
Powder X-ray diffraction (XRD) with Rietveld refinement is the standard for mineral quantification, but requires manual phase identification.
Manual phase identification is time-consuming and impractical for large datasets, such as those from synchrotron X-ray diffraction computed tomography.

Purpose of the Study:

To develop and validate a novel neural network (NN) method for automating mineral phase identification and quantification from XRD data.
To overcome the limitations of manual phase identification in XRD analysis, particularly for large and complex datasets.
To provide a freely available, versatile tool for mineralogical analysis applicable to any dataset.

Main Methods:

Generation of large synthetic XRD datasets using an XRD pattern calculation code for NN training.
Development of a specialized loss function for proportion inference to enhance NN performance, efficiency, and stability.
Training a neural network exclusively on synthetic data and testing its performance on both synthetic and real experimental XRD patterns.

Main Results:

The trained NN accurately identified and quantified mineral phases in both synthetic and real XRD patterns.
Achieved low error rates: 0.5% for phase quantification on synthetic data and 6% on experimental data (calcite, gibbsite, dolomite, hematite).
Demonstrated the NN's ability to handle contrasting crystal structures and its applicability to diverse mineralogical datasets.

Conclusions:

The proposed NN method offers a significant advancement in automating mineral phase identification and quantification from XRD data.
This approach enables efficient analysis of large datasets, overcoming bottlenecks associated with manual methods.
The freely available tool has broad applicability across various mineralogical analyses, regardless of the specific phases present.