Ultra Sensitive Sensors based on Nanotubes Porous Networks

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Project Description

This project will focus on design, development and implementation of a family of ultra sensitive sensors taking advantage of tunnelling effects in carbon nanotube porous networks. The intertube junctions will be customized by using different materials depending of the sensing needs (moisture, special gas, deformations..) The student will be in charge of defining a simple phenomenological model for each sensing mechanism, of choosing the best material and microstructure configuration for each sensing need and to manufacture, test and validate the resulting prototype. This study is relevant for applications in structural health monitoring and for continuous tracking of biological systems. The student is expected to participate in writing journal publications and presenting research at conferences. ​​​​
Program - Mechanical Engineering
Division - Physical Sciences and Engineering
Field of Study - ​Mechanical Engineering

About the
Researcher

Gilles Lubineau

Professor, Mechanical Engineering<br/>Associate Dean Faculty, Physical Science and Engineering

Gilles Lubineau
Professor Lubineau’s research simultaneously involves computational, modeling and experimental developments to tackle complex problems related to composite engineering and more generally to heterogeneous materials. 

Lubineau’s research is focused on four key areas:
  • Integrity of composite materials and structures. Isotropic and anisotropic damage theories; fracture mechanics; homogenization techniques bridging micro-mechanical models to meso/macro-scale models; multi-scale modeling; and damage mechanisms in nano-reinforced multiscale composites.
  • Durability of composite materials and structures; modeling of aging for polymer-based CFRPs under various environments (moisture, temperature, radiation, oxidation, mechanical fatigue); aging of steel pipes in sour environments; and the development of multiphysics-related models (experimental, modeling and computational work).
  • Inverse problems for the identification of constitutive parameters; digital image correlation-based identification techniques; identification techniques for interfaces in joints and laminates; and identification techniques based on 2-D (optical pictures) and 3-D (tomography) image correlation.
  • Multiscale coupling techniques; coupling between non-local continuum and local continuum models; and upscaling strategies for handling localized effects in large-scale simulations.

Desired Project Deliverables

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