Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis

TL;DR

This paper presents a data-driven, Bayesian optimization approach for synthesizing microgels with specific sizes in a continuous flow reactor, reducing experimental effort and enabling tailored microgel production.

Contribution

It introduces a novel optimization framework combining Bayesian methods and experimental validation for microgel synthesis, addressing the lack of mechanistic models.

Findings

Successfully optimized microgel size and production efficiency

Validated Pareto optimal solutions through experiments

Reduced number of experiments needed for microgel development

Abstract

Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their size in the nanometer range allows them to pass human cell boundaries. For applications with specified requirements regarding size, producing tailored microgels in a continuous flow reactor is advantageous because the microgel properties can be controlled tightly. However, no fully-specified mechanistic models are available for continuous microgel synthesis, as the physical properties of the included components are only studied partly. To address this gap and accelerate tailor-made microgel development, we propose a data-driven optimization in a hardware-in-the-loop approach to efficiently synthesize microgels with defined sizes. We optimize the synthesis regarding conflicting objectives (maximum production efficiency, minimum energy consumption, and the desired microgel radius) by applying Bayesian optimization via the solver ``Thompson sampling efficient multi-objective optimization'' (TS-EMO). We validate the optimization using the deterministic global solver ``McCormick-based Algorithm for mixed-integer Nonlinear Global Optimization'' (MAiNGO) and verify three computed Pareto optimal solutions via experiments. The proposed framework can be applied to other desired microgel properties and reactor setups and has the potential of efficient development by minimizing number of experiments and modelling effort needed.