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Rapid mixing is a critical step in many nanoparticle syntheses that can impact the ability to scale production from bench to industrial levels. This study combines experimental and computational approaches to characterize mixing dynamics in crossflow jet mixing reactors (JMRs) with millimeter-scale internal dimensions. The Villermaux-Dushman reaction system is used to quantify experimental mixing times across different reactor sizes and flow rates. Complementary computational fluid dynamics (CFD) simulations assess changes in the state of the flow and estimate mixing times under varying operating conditions. Mixing times derived from CFD results agree well with the experimental results for mixing indices between 0.95 and 0.98. To demonstrate the impact of mixing on nanoparticle formation, we synthesize polybutylacrylate-b-polyacrylic acid (PBA-PAA) block co-polymer nanoparticles, confirming the existence of a critical flow rate beyond which particle size stabilizes. Additionally, we produce polylactic acid-co-glycolic acid (PLGA) nanoparticles incorporating a hydrophobic dye, achieving an average particle size below 300 nm at a throughput of ∼ 1.3 kg/day. These results provide insights into optimizing JMRs for high-throughput, reproducible nanoparticle synthesis, bridging the gap between benchtop and industrial-scale production. 

Performative prediction, as introduced by Perdomo et al, is a framework for studying social prediction in which the data distribution itself changes in response to the deployment of a model. Existing work in this field usually hinges on three assumptions that are easily violated in practice: that the performative risk is convex over the deployed model, that the mapping from the model to the data distribution is known to the model designer in advance, and the first-order information of the performative risk is available. In this paper, we initiate the study of performative prediction problems that do not require these assumptions. Specifically, we develop a reparameterization framework that reparametrizes the performative prediction objective as a function of the induced data distribution. We then develop a two-level zeroth-order optimization procedure, where the first level performs iterative optimization on the distribution parameter space, and the second level learns the model that induces a particular target distribution at each iteration. Under mild conditions, this reparameterization allows us to transform the non-convex objective into a convex one and achieve provable regret guarantees. In particular, we provide a regret bound that is sublinear in the total number of performative samples taken and is only polynomial in the dimension of the model parameter.