Ricerc@Sapienza

The Paper illustrates a specific “joined teaching” experience - addressed to students with a miscellaneous background of the Master of Science in Product Design at Sapienza University of Rome - lead by a Materials Science and Engineering Professor from Politecnico di Milano and a Design Professor from Sapienza University of Rome. This “joined teaching” experience has been built on a strong interaction and hybridization between the methodologies of material science (deductive-logical-analytical) and design (inductive-experiential-synthetical).

Future human or robotic missions to the Moon will require efficient ascent path and accurate orbit injection maneuvers, because the dynamical conditions at injection affect the subsequent phases of spaceflight. This research focuses on the original combination of two techniques applied to lunar ascent modules, i.e., (1) the recently introduced variable-time-domain neighboring optimal guidance (VTD-NOG), and (2) a constrained proportional-derivative (CPD) attitude control algorithm.

Nowadays, liposomes are the most successful drug delivery systems with a dozen drug products available in the clinic. Grafting poly-(ethylene glycol) (PEG) onto the liposome surface prevents protein binding thus prolonging blood circulation, while synthetic modification of the terminal PEG molecule with ligands (e.g. monoclonal antibodies and peptides) should promote selective accumulation in the tumor region with respect to healthy tissues. However, despite big efforts, advances have not outgrown the development stage and just a few targeted liposomal drugs are commercially available.

Today, early disease detection (EDD) is a matter of more importance than ever in medicine. Upon interaction with human plasma, nanoparticles are covered by proteins leading to formation of a biomolecular corona (BC). As the protein patterns of patients with conditions differ from those of healthy subjects, current research into technologies based on the exploitation of personalized BC patterns could be a turning point for early disease detection. Here, we present a framework based on principal component analysis of large personalized BC datasets.