Diehl, H. P., Sweeney, C. B., Tran, T. Q., & Green, M. J. (2023). Computer vision and machine learning for assessing dispersion quality in carbon nanotube / resin systems. Carbon, 213, 118230. https://doi.org/10.1016/j.carbon.2023.118230
Abstract:
The addition of nanomaterials to polymeric resins can enhance a range of bulk material properties, but the nanofiller effectiveness varies strongly on the dispersion quality. The ability to independently, objectively, and quickly assess the dispersion quality of nano-loaded resins based on microscopy is desirable, but current techniques are often subjective and time-consuming. For this paper, we utilize a dispersion metric based on the use of image segmentation of optical microscope images. We then show that by training a computer vision model on a dataset of segmented microscopy images, the model can then quickly and accurately assess the dispersion of nanoparticles in a material. We apply this process to microscope images of carbon nanotube-loaded commercial resins. Our results indicate that this machine-learning methodology can match the accuracy and repeatability of current methods. In principle, this same machine-learning approach can be applied to a broad range of nanomaterials and matrices, allowing for rapid and quantitative analysis of microscope images for in-line quality control.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR Graduate Academy - A*STAR International Fellowship Program
Grant Reference no. : NA