Bio-physico-chemical Insights by Fluorescence-free Flow-induced Taylor Analysis using Neuronal Networks for Ensembles of Nanoparticles

Characterizing polydisperse suspensions made of complex microscopic entities is a key task for most control and purification processes in the water-treatment, pharmaceutical, agri-food and cosmetics industries. Existing methods and products to perform such a task are often costly, low-throughput, large-volume, mono-modal, label-based, with moderate resolution, and typically only provide partial information such as an average particle size. By substantially benefitting from the fundamental knowledge produced, as well as the practical methods and equipment developed and assembled within the context of the mother ERC-CoG project on transport of microscopic entities in confinement, the current application ambitions to overcome the above limitations. The main goal is to develop and valorise a novel and versatile bio-physico-chemical characterization platform. It aims at combining in a unique fashion state-of-the-art microfluidic, imaging and deep-learning methods, through absorption-based Taylor-dispersion analysis, spectrometry, and advanced statistical inference. Importantly, the platform is expected to be low-cost, high-throughput, low-volume,label-free, high-resolution, and to provide access for the first time to the full distribution of sizes and formulation of complex suspensions of any microscopic agents of interest. The program is divided into four work packages, respectively dealing with: the mounting of the ultra-stable microfluidic platform with automated robotic arms for product selection and injection; the imaging and characterization tests on suspensions, with increasing degrees of complexity; the implementation of AI methods for high-dimensionality statistical inference; and the valorisation and refined market identification. Preliminary results already obtained from the mother ERC-CoG project indicate rapid feasibility with mastered risks.