Efficient preparation of size-controlled sodium alginate microspheres via adaptive Bayesian optimization of the spray...

Preparing sodium alginate (SA) microspheres via spray-precipitation challenges precise size control due to complex parameter coupling. We propose an intelligent framework integrating adaptive Bayesian optimization (BO) with microfluidic spraying to maximize the yield of 90-110 µm microspheres. Utilizing a Gaussian process regression model and Latin hypercube sampling, the framework demonstrated exceptional efficiency. Initializing with 10 prior data points achieved convergence in just 12 iterations, reducing iteration cost by 29.4% compared to using 5 priors. Under optimal conditions, the predicted target droplet proportion (18.60%) precisely matched experiments (18.34%), yielding 18.75% target-sized SA gel microspheres post-curing. Additionally, SHAP analysis revealed that gas and liquid pressures dominate size distribution, elucidating the physical mechanism behind multiple local optima via multi-feature compensation. This study provides an efficient, low-cost strategy for customizing polymer microspheres, establishing a robust machine-learning paradigm for optimizing complex multiphase flows.