Dielectrophoresis (dep) and electrowetting (ewod) as an anti-fouling process for antibacterial surfaces

Nowadays the medical field is struggling to decrease bacteria biofilm formation which leads to infection. Biomedical devices sterilization has not changed over a long period of time. This results in high costs for hospitals healthcare managements. The objective of this project is to investigate electric field effects and surface energy manipulation as solutions for preventing bacteria biofilm for future devices. Based on electrokinectic environments 2 different methods were tested: feasibility of electric gradient through mediums (DEP) reinforced by numerical simulations; and EWOD by the fabrication of golden interdigitated electrodes on silicon glass substrates, standard ~480 nm Teflon (PTFE) layer and polymeric gasket to contain the bacteria medium. In the first experiment quantitative analysis was carried out to achieve forces required to reject bacteria without considering dielectric environment limitations as bacteria and medium frequency dependence. In the second experiment applied voltages was characterized by droplets contact angle measurements and put to the live bacteria tests. The project resulted on promising results for DEP application due to its wide range of frequency that can be used to make a “general” bacteria rejecting; but in terms of practicality, EWOD probably have higher potential for success but more experiments are needed to verify if can prevent biofilm adhesion besides the Teflon non-adhesive properties (including limitations as Teflon breakthrough, layer sensitivity) at incubation times larger than 24 hours.

A study of air-sea interaction processes on water mass formation and upwelling in the Mediterranean sea

Air-sea interactions are a key process in the forcing of the ocean circulation and the climate. Water Mass Formation is a phenomenon related to extreme air-sea exchanges and heavy heat losses by the water column, being capable to transfer water properties from the surface to great depth and constituting a fundamental component of the thermohaline circulation of the ocean. Wind-driven Coastal Upwelling, on the other hand, is capable to induce intense heat gain in the water column, making this phenomenon important for climate change; further, it can have a noticeable influence on many biological pelagic ecosystems mechanisms. To study some of the fundamental characteristics of Water Mass Formation and Coastal Upwelling phenomena in the Mediterranean Sea, physical reanalysis obtained from the Mediterranean Forecating System model have been used for the period ranging from 1987 to 2012. The first chapter of this dissertation gives the basic description of the Mediterranean Sea circulation, the MFS model implementation, and the air-sea interaction physics. In the second chapter, the problem of Water Mass Formation in the Mediterranean Sea is approached, also performing ad-hoc numerical simulations to study heat balance components. The third chapter considers the study of Mediterranean Coastal Upwelling in some particular areas (Sicily, Gulf of Lion, Aegean Sea) of the Mediterranean Basin, together with the introduction of a new Upwelling Index to characterize and predict upwelling features using only surface estimates of air-sea fluxes. Our conclusions are that latent heat flux is the driving air-sea heat balance component in the Water Mass Formation phenomenon, while sensible heat exchanges are fundamental in Coastal Upwelling process. It is shown that our upwelling index is capable to reproduce the vertical velocity patterns in Coastal Upwelling areas. Nondimensional Marshall numbers evaluations for the open-ocean convection process in the Gulf of Lion show that it is a fully turbulent, three-dimensional phenomenon.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.