ISIS Neutron and Muon Source Data Journal
This is a page describing data taken during an experiment at the ISIS Neutron and Muon Source. Information about the ISIS Neutron and Muon Source can be found at https://www.isis.stfc.ac.uk.
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.
Public release date: 04 December 2023
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.RB2010619-1
Parent DOI: 10.5286/ISIS.E.RB2010619
ISIS Experiment Number: RB2010619
Part Number: 1
Date of Experiment: 30 November 2020
Publisher: STFC ISIS Neutron and Muon Source
Data format: RAW/Nexus
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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.RB2010619-1
Data is released under the CC-BY-4.0 license.
