What are the broader impacts of this work?
Liquid formulations are the cornerstone of modern life – from soaps and shampoos to engineered food products, coatings, pharmaceuticals, and beyond. In all these applications the structure of the material is directly tied to how it performs in its application – how well the soap cleans, whether the orange juice tastes fresh, how durable the paint is, how long your vaccine works. Despite this importance, there are very few rational approaches for formulating such mixtures, with most product design driven by trial-and-error in mixtures with hundreds of components. Leveraging the high x-ray intensity available at CHESS, our team has developed a system that can intelligently map out the structure as a function of dozens of components, which will hopefully enable the rational design of more formulations and accelerate the pace of innovation.
Why is this important?
The development of the Autonomous Formulation Laboratory (AFL) is among the first combinations of artificial intelligence, x-ray/neutron scattering, and industrial formulation problems. Formulations are ubiquitous in society and accelerating formulation science is key to improving product performance and reducing environmental footprint in coatings, food products, personal care products, and beyond.
Why did this research need MSN-C & CHESS?
The synchrotron X-ray experiments used to develop the AFL were done exclusively at CHESS. The FMB/ID3B beamline offers a high-resolution small-angle scattering instrument with best-in-class ability to support intensive, user-supplied in situ sample environments such as the AFL.
How was the work funded?
This work was supported by the US Department of Commerce – National Institute of Standards and Technology and by the members of the nSoft consortium at NIST (nist.gov/nsoft).
Beaucage, P.A and Martin, T.B. (2022) ‘The Autonomous Formulation Laboratory: An Open Liquid Handling Platform for Formulation Discovery Using SAXS and SANS’, submitted