Rapid assessment of the viability of E. coli and other bacteria pathogens is important for timely monitoring of water quality. Therefore, we propose a label-free method for assessing the viability of E. coli cells in a fast way by using quantitative phase microscopy (QPM) and machine learning. According to the viability levels, E. coli cell populations were divided into two classes that were treated with 0.9% and 25% sodium chloride (NaCl) suspended in phosphate-buffered saline (PBS) solution, respectively. Their high contrast phase images are acquired by a high sensitivity diffraction phase microscope. To determine the viability class of individual E. coli cells, a residual neural network (ResNet) is developed to extract the rich information contained in the phase images. An average testing accuracy as high as 95.5% has been achieved in predicting the two viability classes.
KEYWORDS: Thin films, Microscopes, Metrology, Thin film solar cells, Thin film devices, Atomic force microscopy, Transmission electron microscopy, Phase measurement, Laser interferometry, Transistors
A transmission-mode high sensitivity quantitative phase microscope (QPM) is developed for profiling transparent thin film structures. The QPM system is implemented with a common-path interferometry design and a high well-depth camera, which has allowed us to achieve an optical path length difference sensitivity of around 50 picometers. A frame averaging method can be used to further improve the sensitivity. To account for multiple interference within thin films, a transmission matrix model is developed to achieve accurate height profile reconstruction. With the correction model, the profiling accuracy can be improved from 20.6% to 4.0% for a MoS2 thin film with a thickness of around 25 nm.
We propose and demonstrate a high sensitivity common-path quantitative phase microscopy (QPM) technique that can be used to detect nanoscale dynamics with millisecond temporal resolution. Our system is based on a transmission-mode diffraction phase microscope that is implemented with a high electron well-depth camera to reduce the phase noise. Our current system can achieve ~0.1 mrad temporal phase sensitivity, which is one order of magnitude better over most current QPM systems. Our system can be potentially used for observing morphological changes of cells and probing subnanometer membrane dynamics with millisecond temporal resolution.
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