ISIS Neutron and Muon Source Data Journal

Combining microfluidic-SANS with Deep Learning for the Rapid Mapping of Micellar Structure

Abstract: Surfactants are extensively used in industrial formulations impacting the personal care, food, coatings, oil/lubricant, and agri-chemical sectors, corresponding to an annual turnover of around £200bn in the UK alone. Formulations are often complex multi-component mixtures, comprising of surfactant solutions and various active ingredients, each expected to play a specific role. Microfluidics-SANS will be combined with deep learning analysis to rapidly determine the micellar structure of surfactant systems. The 2D SANS images will be used directly and fed into a convolutional neural network which will then predict the micellar structure. The micellar structures that will be examined are: spherical, ellipsoidal, rod-like and worm-like.

Principal Investigator: Professor Joao Cabral
Experimenter: Mr Joash Alonso
Experimenter: Mr Dale Seddon
Experimenter: Miss Aysha Rafique
Local Contact: Dr Najet Mahmoudi
Experimenter: Dr William Sharratt

DOI: 10.5286/ISIS.E.RB2010619

ISIS Experiment Number: RB2010619

Publisher: STFC ISIS Neutron and Muon Source

Data format: RAW/Nexus
Select the data format above to find out more about it.

Data Citation

The recommended format for citing this dataset in a research publication is as:
[author], [date], [title], [publisher], [doi]

For Example:
Professor Joao Cabral et al; (2020): Combining microfluidic-SANS with Deep Learning for the Rapid Mapping of Micellar Structure, STFC ISIS Neutron and Muon Source, https://doi.org/10.5286/ISIS.E.RB2010619

Data is released under the CC-BY-4.0 license.