Preparation and characterization of grafted nonwoven membranes for bioseparations

Protein purification plays a crucial role in biotechnology and biomanufacturing, where downstream unit operations account for 40%-80% of the overall costs. To overcome this issue, companies strive to simplify the separation process by reducing the number of steps and replacing expensive separation devices. In this context, commercially available polybutylene terephthalate (PBT) melt-blown nonwoven membranes have been developed as a novel disposable membrane chromatography support. The PBT nonwoven membrane is able to capture products and reduce contaminants by ion exchange chromatography. The PBT nonwoven membrane was modified by grafting a poly(glycidyl methacrylate) (GMA) layer by either photo-induced graft polymerization or heat induced graft polymerization. The epoxy groups of GMA monomer were subsequently converted into cation and anion exchangers by reaction with either sulfonic acid groups or diethylamine (DEA), respectively. Several parameters of the procedure were studied, especially the effect of (i) % weight gain and (ii) ligand density on the static protein binding capacity. Bovine Serum Albumin (BSA) and human Immunoglobulin G (hIgG) were utilized as model proteins in the anion and cation exchange studies. The performance of ion exchange PBT nonwovens by HIG was evaluated under flow conditions. The anion- and cation- exchange HIG PBT nonwovens were evaluated for their ability to selectively adsorb and elute BSA or hIgG from a mixture of proteins. Cation exchange nonwovens were not able to reach a good protein separation, whereas anion exchange HIG nonwovens were able to absorb and elute BSA with very high value of purity and yield, in only one step of purification.

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.