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Updated: Nov 20, 2025

X-ray Powder Diffraction in Conservation Science: Towards Routine Crystal Structure Determination of Corrosion Products on Heritage Art Objects
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Quantifying the Likelihood of Structural Models through a Dynamically Enhanced Powder X-Ray Diffraction Protocol.

Sander Borgmans1, Sven M J Rogge1, Juul S De Vos1

  • 1Center for Molecular Modeling (CMM), Ghent University, Technologiepark 46, 9052, Zwijnaarde, Belgium.

Angewandte Chemie (International Ed. in English)
|January 25, 2021
PubMed
Summary
This summary is machine-generated.

Determining the structure of new, poorly crystalline materials like covalent organic frameworks is difficult. This study introduces a new method using X-ray diffraction (XRD) patterns to predict and identify material structures, enabling advanced functional material design.

Keywords:
X-ray diffractioncovalent organic frameworksmaterials characterizationoperando modelingstructure prediction

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Area of Science:

  • Materials Science
  • Crystallography
  • Chemistry

Background:

  • Characterizing the atomic structure of new materials is crucial for understanding their properties and designing new functionalities.
  • Obtaining single crystals for structural analysis is often not feasible for materials like covalent organic frameworks, posing a significant challenge.
  • Existing methods struggle to accurately determine the structures of poorly crystalline or amorphous materials.

Purpose of the Study:

  • To develop a novel protocol for unambiguously predicting the structure of poorly crystalline materials.
  • To establish a reliable method for structure-function relationship studies in materials where single crystals are unavailable.
  • To facilitate the design of next-generation functional materials through accurate structural identification.

Main Methods:

  • A protocol based on likelihood ordering of predicted structures derived from X-ray diffraction (XRD) patterns was developed.
  • A broad set of potential structures were generated using a limited number of building blocks and topologies.
  • Operando structural characterization, including dynamic averaging, was employed to match experimental powder XRD patterns.

Main Results:

  • The proposed method achieved unparalleled agreement with experimental powder XRD patterns.
  • The protocol successfully accounts for operando conditions and the temporal nature of experimental measurements.
  • The likelihood ordering effectively distinguishes between possible structural models for poorly crystalline materials.

Conclusions:

  • The developed protocol can unambiguously identify the structure of experimentally synthesized, poorly crystalline materials.
  • This structural identification is a critical step towards the rational design of advanced functional materials.
  • The method offers a powerful tool for materials scientists working with challenging crystalline samples.