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

X-ray Crystallography02:18

X-ray Crystallography

26.6K
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...
26.6K
Determination of Crystal Structures01:29

Determination of Crystal Structures

41
In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...
41
X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

5.1K
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...
5.1K

You might also read

Related Articles

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

Sort by
Same author

Hunting ecology predicts eye arrangements in the modular visual system of spiders.

Current biology : CB·2026
Same author

Degeneration dependent changes in human knee cartilage mechanical properties revealed by synchrotron tomography based finite element modeling.

Osteoarthritis and cartilage open·2026
Same author

Synchrotron phase-contrast microtomography reveals the impact of degeneration on the 3D structure of human articular cartilage.

Osteoarthritis and cartilage·2026
Same author

Analysis and Comparison of Natural Shear and Induced Tensile Fractures for Caprock Leakage Assessment.

Transport in porous media·2026
Same author

Dynamic microtomography of human tympanic membrane motions.

Hearing research·2025
Same author

A standing wave tube-like setup designed for tomographic imaging of the sound-induced motion patterns in fish hearing structures.

BMC biology·2025

Related Experiment Video

Updated: Mar 16, 2026

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

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

Published on: June 19, 2018

9.7K

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data.

Nouamane Laanait1, Zhan Zhang, Christian M Schlepütz

  • 1Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

Nanotechnology
|August 10, 2016
PubMed
Summary

This study introduces a machine learning method to analyze nanoscale lattice variations in crystalline materials using x-ray diffraction microscopy. The technique effectively identifies structural defects like dislocations in thin films.

More Related Videos

Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene
08:44

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

Published on: August 22, 2017

8.2K
Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography
11:48

Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography

Published on: April 24, 2018

15.3K

Related Experiment Videos

Last Updated: Mar 16, 2026

Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
10:12

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

Published on: June 19, 2018

9.7K
Measurements of Long-range Electronic Correlations During Femtosecond Diffraction Experiments Performed on Nanocrystals of Buckminsterfullerene
08:44

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

Published on: August 22, 2017

8.2K
Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography
11:48

Microfluidic Chips for In Situ Crystal X-ray Diffraction and In Situ Dynamic Light Scattering for Serial Crystallography

Published on: April 24, 2018

15.3K

Area of Science:

  • Materials Science
  • Crystallography
  • Machine Learning Applications

Background:

  • X-ray Bragg diffraction microscopy captures hybrid real and reciprocal space information.
  • Analyzing multidimensional (5D) data from this technique is challenging.
  • Understanding nanoscale lattice variations is crucial for material properties.

Purpose of the Study:

  • To develop a machine learning methodology for extracting nanoscale lattice variations.
  • To interpret structural distortions from complex diffraction data.
  • To demonstrate the approach on ferroelectric thin films.

Main Methods:

  • Utilizing a full-field microscopy setup for x-ray Bragg diffraction imaging.
  • Applying unsupervised machine learning and multivariate analysis to 5D data.
  • Extracting physically interpretable features related to lattice distortions.

Main Results:

  • Successfully extracted features corresponding to lattice tilts and dislocation arrays.
  • Demonstrated the capability to identify structural defects in a lead zirconate titanate thin film.
  • Validated the 'big data' approach for x-ray diffraction microscopy.

Conclusions:

  • The novel machine learning methodology enables detailed nanoscale analysis of crystalline materials.
  • This approach provides physical insights into structural distortions from diffraction data.
  • The technique is effective for characterizing defects in advanced thin-film materials.