Using deep learning for the prediction of mixing patterns in two component-colored solutions as a proxy to dispersion in nanocomposite coatings

Abstract

Dispersion of nanofiller in the polymer matrix is a vital factor that influences the properties of the fabricated nanocomposites at the laboratory level. Characterization techniques like TEM and FESEM, having a small sample size, tend to miss out on the big picture for the analysis of mixing on a larger scale. At the industrial level, conducting such testing with varying experimental conditions is not viable in terms of both cost and time. Through this study, we propose a simple method to examine the extent of dispersion using a simple camera and employing deep learning (DL) models. For this purpose, an analogous study has been performed to study the sensitivity of the processing techniques, to better understand the findings in our previous articles. A two component-colored solution (oil–water) was utilized as a proxy for the nanofiller-polymer matrix system. Different processing methods were employed namely ultrasonication, homogenization, sequential ultrasonication and homogenization and simultaneous ultrasonication and homogenization. The variation in processing technique significantly affects the dispersion which is attributed to the different mixing mechanisms (turbulent, diffusive, and convective) incurred in these processing techniques. Inferences are withdrawn by detecting patterns in a large sample size which highlights that DL models provide us with a holistic viewpoint of real-time observations. It also ameliorates human interpretation by unraveling obscure information which can go unnoticed by human eyes.

Graphical abstract

Keywords:

• Deep learning
• dispersion
• turbulent mixing
• diffusive mixing
• convective mixing

Acknowledgments

The authors thank PURSE (DST, New Delhi) for providing the necessary equipments. We acknowledge the financial assistance given by DRDO (DMSRDE Kanpur) (TR/0569/CARS-130 dated 16/12/2021), TEQIP-III grants to Dr. SSBUICET and the receiving of collaborative grant from Nottingham Trent University (NTU), United Kingdom under the science and technology initiative (NTU-PU 03/19/2021).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.