Valentina Marassi

Valentina Marassi

Valentina Marassi is a tenured researcher and senior assistant professor in analytical chemistry at the Analytical Methods for Nano and Biosciences group, University of Bologna. She received her Ph.D. in 2017, and since then, her work has focused on instrumental innovation, multidetection strategies, and chemometric modeling for native profiling of complex biological and colloidal systems, applied to bioanalytical chemistry, nanomedicine, and liquid biopsy. Marassi is PI of the national project DOMANI (native profiling of nanoplastics in real-life matrices), and co-PI of RESOLVE (FFF coupling to Lab on Chip for prompt analysis of extracellular vesicles), and partner in the international project AF4-AI (FFF profiling of cerebrospinal fluid for traumatic brain injury prognosis). She is co-owner of the academic spin-off byFlow srl, developing FFF-based technologies and analytical solutions in nanobiopharma. Her awards include the Accademia delle Scienze Top-10 recognition and the national Giovannoli prize for young researchers.

She has 15 years of work experience with FFF.

Articles by Valentina Marassi

Dynamic particle waves flowing through a cyber grid, neon hologram effects with deep purples and blues for a futuristic technology vibe  | Image Credit: © afrah - stock.adobe.com.

Field-flow fractionation (FFF), and, in particular, asymmetrical flow field-flow fractionation (AF4), is transitioning from a specialized separation technique into an application-driven analytical platform. From the perspective of the Young Scientists of FFF, we describe how advances in inline detection, data analysis, and validation are expanding AF4’s capacity to deliver size-resolved structural and compositional insights into complex systems. We highlight how this evolution enables more reliable characterization of heterogeneous and dynamically assembled materials across disciplines. We argue that realizing this potential will require deliberate choices (by the community, instrument developers, and end users) to move AF4 from niche expert knowledge to broadly trusted analytical practice.