Optimisation of multi-component phase mapping with machine learning and automation
Lead Research Organisation:
Imperial College London
Department Name: Chemical Engineering
Abstract
Understanding the behaviour of complex chemical and physical soft matter systems is important from fundamental and applied (i.e. industrial) points of view. A way of understanding complex chemical and physical systems is characterising their thermodynamic behaviour. This can be described with the aid of phase diagrams. However, constructing a phase diagram is a laborious task and can take years to fully characterise a given system.
Our group is interested in probing and phase-mapping complex polymer and surfactant mixtures with small angle neutron scattering (SANS) and other scattering techniques by coupling these techniques to microfluidic platforms.1-3 Recently, the advent of microfluidics has accelerated phase mapping.4-6 In addition, there have been examples in current literature on optimisation of phase mapping with machine learning.7-10
With that in mind, the rationale behind combining microfluidics, in-line analytics (SANS and other scattering techniques) and active learning optimisation algorithm is described. First, the concept of soft matter and microfluidics is briefly introduced. Second, ways of probing soft matter, particularly with scattering techniques, described. A brief theory of multi-dimensional phase diagrams is covered and literature examples for optimisation of phase mapping given.
Our group is interested in probing and phase-mapping complex polymer and surfactant mixtures with small angle neutron scattering (SANS) and other scattering techniques by coupling these techniques to microfluidic platforms.1-3 Recently, the advent of microfluidics has accelerated phase mapping.4-6 In addition, there have been examples in current literature on optimisation of phase mapping with machine learning.7-10
With that in mind, the rationale behind combining microfluidics, in-line analytics (SANS and other scattering techniques) and active learning optimisation algorithm is described. First, the concept of soft matter and microfluidics is briefly introduced. Second, ways of probing soft matter, particularly with scattering techniques, described. A brief theory of multi-dimensional phase diagrams is covered and literature examples for optimisation of phase mapping given.
Organisations
| Description | The topic of the award was changed to look at out of equilibrium surfactant (surface active agent) systems under flow. In particular, we investigated the formation of vesicles from flat lamellar sheets with the aid of microfluidics. Forming vesicles of a specific size under given conditions is crucial in fast moving consumer goods (e.g. to encapsulate perfume molecules) or pharmaceutical industry (e.g. drug encapsulation) as well as more fundamental research, such as protocell models. Here we investigated how these vesicles would be formed under microfluidic (microfluidics uses channels within micrometre range) flow. The key finding was that we successfully were able to form vesicles from a model surfactant system and were able to control their size by varying the speed of the flow. |
| Exploitation Route | Here we have developed a microfluidic platform which allows to make vesicles under flow and rapidly study the kinetics of vesicle formation with a range of techniques. The research could be taken in a few directions, but the paths that I think would be interesting are: 1) Looking at different lamellar (membrane) phases under flow and investigate if a surfactants used as model systems in membrane biophysics research would undergo a similar transformation; 2) Use microfluidics to induced different metastable states known in surfactant systems, for example micellar to WLM transformation, surfactant crystallisation under flow |
| Sectors | Chemicals Manufacturing including Industrial Biotechology |