Geometrically-mediated snap-off of water-in-oil emulsion droplets in microfluidic flow focusing devices

Deterministic Single-Cell Encapsulation in PEG Norbornene Microgels for Promoting Anti-Inflammatory Response and Therapeutic Delivery of Mesenchymal Stromal Cells

Tissue engineering at single-cell resolution has enhanced therapeutic efficacy. Droplet microfluidics offers a powerful platform that allows deterministic single-cell encapsulation into aqueous droplets, yet the direct encapsulation of cells into microgels remains challenging. Here, the design of a microfluidic device that is capable of single-cell encapsulation within polyethylene glycol norbornene (PEGNB) hydrogels on-chip is reported. Cells are first ordered in media within a straight microchannel via inertial focusing, followed by the introduction of PEGNB solution from two separate, converging channels. Droplets are thoroughly mixed by passage through a serpentine channel, and microgels are formed by photo-photopolymerization. This platform uniquely enables both single-cell encapsulation and excellent cell viability post-photo-polymerization. More than 90% of singly encapsulated mesenchymal stromal cells (MSCs) remain alive for 7 days. Notably, singly encapsulated MSCs have elevated expression levels in genes that code anti-inflammatory cytokines, for example, IL-10 and TGF-β, thus enhancing the secretion of proteins of interest. Following injection into a mouse model with induced inflammation, singly encapsulated MSCs show a strong retention rate in vivo, reduce overall inflammation, and mitigate liver damage. These translational results indicate that deterministic single-cell encapsulation could find use in a broad spectrum of tissue engineering applications.

Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles

Controlling droplet breakup characteristics such as size, frequency, regime, and droplet quality within flow-focusing microfluidic devices is critical for different biomedical applications of droplet microfluidics such as drug delivery, biosensing, and nanomaterial preparation. The development of a prediction platform capable of forecasting droplet breakup characteristics can significantly improve the iterative design and fabrication processes required for achieving desired performance. The present study aims to develop a multipurpose platform capable of predicting the working conditions of user-specific droplet size and frequency and reporting the quality of the generated droplets, regime, and hydrodynamical breakup characteristics in flow-focusing microdevices with different cross-junction tilt angles. Four different neural network-based prediction platforms were compared to accurately estimate capsule size, generation rate, uniformity, and circle metric. The trained capsule size and frequency networks were optimized using the heuristic optimization approach for establishing the Pareto optimal solution plot. To investigate the transition of the droplet generation regime (i.e., squeezing, dripping, and jetting), two different classification models (LDA and MLP) were developed and compared in terms of their prediction accuracy. The MLP model outperformed the LDA model with a cross-validation measure evaluated as 97.85%, demonstrating that the droplet quality and regime prediction models can provide an engineering judgment for the decision maker to choose between the suggested solutions on the Pareto front. The study followed a comprehensive hydrodynamical analysis of the junction angle effect on the dispersed thread formation, pressure, and velocity domains in the orifice.