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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Updated: Jan 28, 2026

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling
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refnx: neutron and X-ray reflectometry analysis in Python.

Andrew R J Nelson1, Stuart W Prescott2

  • 1ANSTO, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia.

Journal of Applied Crystallography
|February 26, 2019
PubMed
Summary
This summary is machine-generated.

Refnx is a Python package for neutron and X-ray reflectometry data analysis. It offers modular model construction and Bayesian fitting for reproducible scientific research.

Keywords:
Bayesian analysisX-ray reflectometrycomputer modellingneutron reflectometryrefnx

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Area of Science:

  • Materials Science
  • Physics

Background:

  • Neutron and X-ray reflectometry are powerful techniques for probing thin film interfaces.
  • Analyzing reflectometry data requires sophisticated modeling and fitting procedures.

Purpose of the Study:

  • To introduce refnx, a new Python package for model-based neutron and X-ray reflectometry data analysis.
  • To provide a flexible and user-friendly tool for researchers in materials science and physics.

Main Methods:

  • refnx utilizes a modular approach for model construction, allowing users to combine components.
  • It incorporates physically relevant parameters and supports various fitting approaches, including least-squares and Bayesian Markov-chain Monte Carlo.
  • The software integrates with Jupyter notebooks for a browser-based graphical user interface and reproducible analysis.

Main Results:

  • The package enables co-refinement of multiple datasets and mixed-area models.
  • It facilitates the encoding of prior knowledge through probability distribution functions and parameter bounds.
  • The Bayesian approach aids in examining parameter covariances, model selection, and scattering length density profile variability.

Conclusions:

  • refnx provides a robust and versatile platform for advanced reflectometry data analysis.
  • Its design promotes reproducible research through Jupyter notebook integration.
  • The software empowers researchers to conduct in-depth investigations of material interfaces.