self-assembled colloidal cone
Machine learning analysis of self-assembled colloidal cones
Optical and confocal microscopy is used to image the self-assembly of microscale colloidal particles. The density and size of self-assembled structures is typically quantified by hand, but this is extremely tedious. Here, we investigate whether machine learning can be used to improve the speed and accuracy of identification. This method is applied to confocal images of dense arrays of two-photon lithographed colloidal cones. RetinaNet, a deep learning implementation that uses a convolutional neural network, is used to identify self-assembled stacks of cones.