Microfluidic technologies are rapidly becoming the method of choice for micro- and nanoparticle production across a number of sectors, from cell biology research to drug development. This approach offers several advantages over traditional batch methods, including precise process control, enhanced reliability, and the ability to easily scale up production. This article discusses how computational fluid dynamics modelling and artificial intelligence approaches are enabling scientists to predict the outcome of microfluidic experiments before putting new protocols into practice, reducing the time taken to set up and optimise novel experiments.
Dolomite
Microfluidic nanoparticle synthesis
Synthetic polymeric nanoparticles are being increasingly used for drug delivery applications in several fields of nanomedicine as well as in clinical and basic research. Poly(lactic-co-glycolic acid) – PLGA – is one of the most successfully developed nanoparticles to date due to its safety, biocompatibility and biodegradability, and has been approved by the US Food and Drug Administration (FDA) for use in several medical applications, including drug encapsulation. The generation of these particles, however, requires incredible precision; for example, a well-controlled bead size distribution is essential for drug delivery vehicles, as particle size and a lack of monodispersity significantly influence drug release rates. This is where microfluidics is extremely useful, offering exceptional reproducibility and tunability of particle generation, with the advantage of reduced reagent consumption and less waste compared to more traditional batch synthesis.