"The power to question is the basis of all human progress."   

Indira Gandhi    

Microfluidics

Methodology and Technology for Microfluidic Systems Control

Nowadays there is an increasing interest in the study of microfluidic (uF) two-phase flow systems where two immiscible fluids travel in microchannels as droplets or bubbles, respectively if in liquid or gaseous phase. In particular droplet/bubble microfluidics is growing importance for numerous applications ranging from emulsion generation to reaction within droplets (microreactors), high-throughput screening and even non-electronic-coding and computing functions. Current Lab on a Chip devices (LOC) use bulky external pumps and valves to control the fluids.

The main drawback in microfluidics is that the increasing complexity of the geometry brings the necessity of a precise knowledge of bubble/droplet dynamics. Up to now the mechanisms that drives the input flows to lead a specific bubble temporization and dynamical patterns and, the stability of these dynamics due to slight changes in the input conditions or channel geometry are not fully understood.

The nonlinear model of the two-phase flow can be described through a set of partial differential equation, different examples are presented in the field of Computational Fluid Dynamics (CFD), but due to its complexity the numerical implementation requires an high level of computation and time resources. Being a wide range of technologies available for the manufacturing of microfluidics device and in some case entailing a fast-design process and low cost in the production; the difficulties related to the numerical study become an important bottleneck for the development of microfluidics-based technology.

The combination of optics and microfluidics offers unprecedented level of integration for building a new generation of optically actuated microdevices. These aspects combined with the advance in the microfabrication of optical detection methods based on the polydimethylsiloxane (PDMS) open the way for the design of an complete PDMS micro-optic-fluidic system in which microfluidic processed are embedded with point-wise actuation and detection and interact among each other.

 

 
 

IN COLLABORATION WITH 

MSc THESIS PROJECTS IN MICROFLUIDICS 

  • PDMS MICRO-OPTICS COMPONENTS DESIGN AND REALIZATION BY 3D-PRINTING
  • PMMA MICRO-OPTICS COMPONENTS DESIGN BY LASER CUTTING
  • DESIGN AND REALIZATION OF MICRO-OPTO-FLUIDICS DEVICES
  • FLUID/FLUID TWO-PHASE FLOW MODELLING & CONTROL BY SIGNAL PROCESSING
  • FLUID/micro-PARTICLES  TWO-PHASE FLOW MODELLING & CONTROL BY IMAGE PROCESSING
  • OPTICAL TWO-PHASE FLOW METERS
  • DESIGN OF SINGLE-PHASE FLUX REGULATOR IN MICROFLUIDCS BY 3D-PRINTING
  • TWO-PHASE FLOW MODELLING BY COMPUTATIONAL FLUID DYNAMICS

 

Computational Neuroscience

 Strategies for the Analysis and Identification of Brain Dynamics 

Nowadays the possibility of different recording modalities of brain activity (as in the case of EEG, MEG and fMRI) allows a continuous monitoring of brain activities with high temporal and spatial resolution. Whereas MEG/EEG has high temporal resolution of below 100ms and therefore allow to explore the timing of basic neural processes at the level of cell assemblies, other methods such as fMRI have a high spatial resolution, typically in the order of 2-3 mm, and can record signals from all regions of the brain, unlike EEG/MEG that are biased towards the cortical surface.
At the same time there is a proliferation of mathematical methods and many case studies on the analysis of these brain data, but no general consensus on the most accurate and efficient way to analyze brain activity. This research is focused on the establishment of a general framework based on data-driven identification methods coupled with both graph analysis and statistics for studying the nature of interactions between brain areas. By integrating different analysis approaches and procedures and define a standard protocol for the data organization  we aim to provide an automatic analysis within the same platform.

 


 

 

IN COLLABORATION WITH 

MSc THESIS PROJECTS IN COMPUTATIONAL NEUROSCIENCE 

  • PLATFORM FOR BRAIN CONNECTIVITY ANALYSIS IN fMRI DATA
  • PLATFORM FOR EEG SIGNAL PRE-PROCESSING
  • NON-LINEAR ANALYSIS OF MEG DATA FOR BRAIN DISORDER DIAGNOSIS

 

 

Additional Areas: 

Portable Device for Real-Time Diagnosis and Control of Heart Dynamics, in collaboration with:

 

Decision Supporting System for Production Planning Control, in collaboration with: