Block copolymer (BCP) self-assembly into nano-assemblies is a topic of intense research ever since block copolymer synthesis itself has first been described. Nowadays, a virtually unlimited number of BCPs is available with relative ease, and BCP nanoaggregates are investigated for many possible applications, most notably as payload carriers in biomedical applications. The formation of particles is generally governed by thermodynamics, and the interplay of individual block lengths and miscibility predict generally what type of nanoparticle is formed. . While these processes are in principle well understood, and for example expertly exploited in methods such as polymerization-induced self-assembly (PISA), reproducibility of nanoparticle synthesis is to date not overly satisfactory. In repeated particle synthesis, BCPs tend to form similar, yet not identical particles and show often very considerable batch to batch variation, even under highly controlled conditions. This is due to the process not only being under thermodynamic control, but also being governed by kinetics, especially if one block exhibits low chain mobility, prohibiting dynamic exchange towards the thermodynamically most stable state. Hence, these chain dynamics as well as hydrodynamic interactions among polymer chains can result in kinetically trapped intermediate structures, which are unpredictable by laws of thermodynamics. In consequence, even smallest changes in the mixing profiles during particle formation (most typically water or another solvent is slowly dripped into a solution of the BCP) change the outcome of the synthesis. Eisenberg and co-workers showed that BCPs of same type with different block lengths having smaller hydrophilic coronas relative to their hydrophobic core blocks, can form different "crewcut" morphologies of self-assembled structures. Further they studied the effects of various parameters such as polymer composition, initial concentration, temperature, common solvent, preparation method and presence of additives on particle morphology using batch-mode experiments. Buckinx et al. expanded on this research and found that performing the nanoaggregate synthesis under continuous flow conditions largely improved the reproducibility of synthesis, and concomitantly introduced kinetic control over particle morphology where a single BCP could form a variety of particle morphologies by varying flow parameters. Flow chemistry is generally very well suited for such synthesis, since it allows for a highly controllable environment, with ideal thermodynamic conditions, under highly reproducible fluid dynamics conditions. Even mixing of miscible (or immiscible) fluids can be carried out with high accuracy. Hence, BCP nanoparticle formation in continuous flow can be regarded as superior to batch processes, similar to the advantages seen for lipid self-assembly in continuous flow, which is not without reason used in SARS-CoV-2 vaccine production. Beyond solving the reproducibility crisis, and exerting at least partial control over particle morphology, flow chemistry also gives access to scalability of the polymer nanoparticle synthesis, lowering another significant hurdle that so far had been present for designer particles making the jump from academic research to practical applications. Verstraete et al. demonstrated for example how nanoparticles formed in flow can immediately be purified via in-line dialysis. Moreover, Thiermann et al. studied the controlled and continuous synthesis of polymersomes using micromixer technology, achieving narrow size distributions. They also showed that the geometry of the used micromixer plays a role in determining the particle morphology. Studies have been done to explore the potential of microfluidics as well in providing control over nanoprecipitation in continuous flow. As for further examples, Stenzel and coworkers demonstrated the reproducible synthesis of metastable polymersomes in flow, and O'Reilly used flow reactors to scale the crystallization driven selfassembly of polymers. While flow methods are rapidly adopted due to their apparent advantages, they do not directly solve the issue that particle synthesis often depends on serendipity due to poor predictability.