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Related Concept Videos

X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

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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...
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Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering
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Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization.

Tyler B Martin1,2, Duncan R Sutherland1, Austin McDannald3

  • 1Materials Science & Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States.

Chemistry of Materials : a Publication of the American Chemical Society
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

We developed an Autonomous Formulation Lab (AFL) to speed up the creation of eco-friendly liquid formulations. Our AI-guided system efficiently explores new material combinations for better performance and sustainability.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Artificial Intelligence

Background:

  • Growing demand for sustainable and high-performance soft materials.
  • Traditional formulation development is time-consuming and resource-intensive.
  • Need for automated platforms to accelerate materials discovery.

Purpose of the Study:

  • To introduce the Autonomous Formulation Lab (AFL) platform.
  • To describe the design, tuning, and validation of an active learning agent for AFL.
  • To demonstrate the platform's capability in accelerating eco-friendly formulation development.

Main Methods:

  • Development of an Autonomous Formulation Lab (AFL) integrating automated sample preparation and characterization.
  • Utilizing small-angle neutron and X-ray scattering for microstructure analysis.
  • Employing an active learning agent, extensively tuned *in silico*, to guide experimental design.

Main Results:

  • The active learning agent demonstrated efficiency and robustness against measurement variations and noise.
  • Experimental validation successfully replaced a petroleum-derived component with a natural analog.
  • The platform efficiently mapped formulation landscapes and identified areas for optimization.

Conclusions:

  • The autonomously guided AFL platform significantly accelerates the discovery of novel liquid formulations.
  • The developed active learning agent is a key component for efficient and targeted materials development.
  • This technology supports the transition towards greener and more sustainable chemical products.