English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Microgels are cross-linked, colloidal polymer networks with great potentialfor stimuli-response release in drug-delivery applications, as their size inthe nanometer range allows them to pass human cell boundaries. For applicationswith specified requirements regarding size, producing tailored microgels in acontinuous flow reactor is advantageous because the microgel properties can becontrolled tightly. However, no fully-specified mechanistic models areavailable for continuous microgel synthesis, as the physical properties of theincluded components are only studied partly. To address this gap and acceleratetailor-made microgel development, we propose a data-driven optimization in ahardware-in-the-loop approach to efficiently synthesize microgels with definedsizes. We optimize the synthesis regarding conflicting objectives (maximumproduction efficiency, minimum energy consumption, and the desired microgelradius) by applying Bayesian optimization via the solver ``Thompson samplingefficient multi-objective optimization'' (TS-EMO). We validate the optimizationusing the deterministic global solver ``McCormick-based Algorithm formixed-integer Nonlinear Global Optimization'' (MAiNGO) and verify threecomputed Pareto optimal solutions via experiments. The proposed framework canbe applied to other desired microgel properties and reactor setups and has thepotential of efficient development by minimizing number of experiments andmodelling effort needed.

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