In each iteration, pumps are autonomously adjusted to the suggested flow rates, and DLS analysis followed upon reaching steady state conditions after each run, with dead volume in tubes and the quartz cuvette filled accordingly. The DLS response then updates the surrogate model, guiding the selection of the next sampling points using the BOEI algorithm. The optimization algorithm in principle ended when the target experiment size was reached, as determined by user-defined tolerance levels. All codes used within this work are documented in the Supporting Information. Nonetheless, the algorithm was instructed to test for stability of the result by repeating the experiment that were identified as most optimal condition. Only if this second datapoint falls within expected experimental error (as estimated from the data in Figure ), the optimization is deemed fully successful.