Assessing the properties of a colloidal suspension with the aid of deep learning

Authors:

• Tomasz Jakubczyk,
• Daniel Jakubczyk,
• Andrzej Stachurski

Abstract

Convolution neural networks were applied to classify speckle images generated from nanoparticle suspen- sions and thus to recognise suspensions. The speckle images in the form of movies were obtained from suspensions placed in a thin cuvette. The classifier was trained, validated and tested on both single com- ponent monodispersive suspensions, as well as on two-component suspensions. It was able to properly recognise all the 73 classes –different suspensions from the training set, which is far beyond the capabil- ities of the human experimenter, and shows the capability of learning many more. The classes comprised different nanoparticle material and size, as well as different concentrations of the suspended phase. We also examined the capability of the system to generalise, by testing a system trained on single-component suspensions with two-component suspensions. The capability to generalise was found promising but sig- nificantly limited. A classification system using neural network was also compared with the one using support vector machine (SVM). SVM was found much more resource-consuming and thus could not be tested on full-size speckle images. Using image fragments very significantly deteriorates results for both SVM and neural networks. We showed that nanoparticle (colloidal) suspensions comprising even a large multi-parameter set of classes can be quickly identified using speckle images classified with convolution neural network. ©