Why do AI models trained on lab data often fail at manufacturing scale? In conventional batch suspension polymerization, mixing and heat transfer change as tanks grow, so lab results stop predicting factory outcomes. We explain these scaling artifacts and how our continuous microfluidic droplet process keeps droplet formation consistent at every scale, so our chromatography microbead data remain useful from R&D through production and support AI-guided materials design.

Peer‑reviewed in ACS Omega : Matches silica C18 on efficiency, LOD/LOQ, and loading, with stable performance at high pH and 100% aqueous...