Enhancing Microdroplet Image Analysis with Deep Learning.

Microfluidics is a highly interdisciplinary field where the integration of deep-learning models has the potential to streamline processes and increase precision and reliability. This study investigates the use of deep-learning methods for the accurate detection and measurement of droplet diameters and the image restoration of low-resolution images. This study demonstrates that the Segment Anything Model (SAM) provides superior detection and reduced droplet diameter error measurement compared to the Circular Hough Transform, which is widely implemented and used in microfluidic imaging. SAM droplet detections prove to be more robust to image quality and microfluidic images with low contrast between the fluid phases. In addition, this work proves that a deep-learning super-resolution network MSRN-BAM can be trained on a dataset comprising of droplets in a flow-focusing microchannel to super-resolve images for scales ×2, ×4, ×6, ×8. Super-resolved images obtain comparable detection and segmentation results to those obtained using high-resolution images. Finally, the potential of deep learning in other computer vision tasks, such as denoising for microfluidic imaging, is shown. The results show that a DnCNN model can denoise effectively microfluidic images with additive Gaussian noise up to σ = 4. This study highlights the potential of employing deep-learning methods for the analysis of microfluidic images.

Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach.

The control of droplet formation and size using microfluidic devices is a critical operation for both laboratory and industrial applications, e.g. in micro-dosage. Surfactants can be added to improve the stability and control the size of the droplets by modifying their interfacial properties. In this study, a large-scale data set of droplet size was obtained from high-speed imaging experiments conducted on a flow-focusing microchannel where aqueous surfactant-laden droplets were generated in silicone oil. Three types of surfactants were used including anionic, cationic and non-ionic at concentrations below and above the critical micelle concentration (CMC). To predict the final droplet size as a function of flow rates, surfactant type and concentration of surfactant, two data-driven models were built. Using a Bayesian regularised artificial neural network and XGBoost, these models were initially based on four inputs (flow rates of the two phases, interfacial tension at equilibrium and the normalised surfactant concentration). The mean absolute percentage errors (MAPE) show that data-driven models are more accurate (MAPE = 3.9%) compared to semi-empirical models (MAPE = 11.4%). To overcome experimental difficulties in acquiring accurate interfacial tension values under some conditions, both models were also trained with reduced inputs by removing the interfacial tension. The results show again a very good prediction of the droplet diameter. Finally, over 10 000 synthetic data were generated, based on the initial data set, with a Variational Autoencoder (VAE). The high-fidelity of the extended synthetic data set highlights that this method can be a quick and low-cost alternative to study microdroplet formation in future lab on a chip applications, where experimental data may not be readily available.

Comparison of surfactant mass transfer with drop formation times from dynamic interfacial tension measurements in microchannels.

Dynamic interfacial tension was studied experimentally during drop formation in a flow-focusing microchannel. A low viscosity silicone oil (4.6 mPa s) was the continuous phase and a mixture of 48% w/w water and 52% w/w glycerol was the dispersed phase. An anionic (sodium dodecylsulfate, SDS), a cationic (dodecyltrimethylammonium bromide, DTAB) and a non-ionic (Triton™ X-100, TX100) surfactant were added in the dispersed phase, at concentrations below and above the critical micelle concentration (CMC). For SDS and DTAB the drop size against continuous phase flowrate curves initially decreased with surfactant concentration and then collapsed to a single curve at concentrations above CMC. For TX100 the curves only collapsed at surfactant concentrations 8.6 times the CMC. From the collapsed curves a correlation of drop size with capillary number was derived, which was used to calculate the dynamic interfacial tension at times as low as 3 ms. The comparison of the surfactant mass transport and adsorption times to the interface against the drop formation times indicated that surfactant adsorption also contributes to the time required to reach equilibrium interfacial tension. Criteria were proposed for drop formation times to ensure that equilibrium interfacial tension has been reached and does not affect the drop formation.