Innovate materials solutions for solving current energy and environmental challenges
Computational Energy Materials Design Infrastructure
a unique infrastructure dedicated to revolutionize materials research discovery for commercializing sustainable energy technologies and preventing climate change.
Learn More arrow_forward_iosarrow_forward_iosInfrastructural collaboration within Canada & globally Industry-Academic partnership to bring theory to practice
Research teams based in Varennes, Quebec, Canada and beyond
Facilities arrow_forward_iosarrow_forward_iosAdvanced Computational
Funded by Governments of Canada and Quebec
Themes arrow_forward_iosarrow_forward_iosEnergy Material
via advanced computational and numerical methods
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Andreas Peter Ruediger
Nanophotonics and nanoelectronics
Molecular and device physics
Numerical & DFT Modelling
computational optical imaging, image processing
Structure and Dynamics of Energy Materials , Ultrafast Microscopy
Canada Research Chair Tier 2
Classic & Quantum Simulations
Theoretical physics & machine learning
PI: Prof. Chaker
PI: Prof. Sun & Prof. Vidal
PI: Prof Liang
PI: Prof. Vidal
PI: Prof. Sun & Prof. Vidal
PI: Prof. Ghuman & Prof. Tavares
PI: Prof. Taveres
Jose Madrid Madrid
PI: Prof Ghuman
PI: Prof. Orgiu
PI: Prof. Ghuman
PI: Prof. Ghuman & Prof. O'Brien
PI: Prof. Liang
PI: Prof. Liand
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Menunavigate_nextnavigate_nextDesigning Materials for Energy and Environmental Applications via Advanced Computational Techniques The major hurdle in achieving the economic success from the existing sustainable technologies is that the materials used at their core are extremely expensive, and the materials that are cheap and abundant are not active enough to be used commercially. Improving the performance of inexpensive and abundant materials is a challenge because of the poor understanding of their fundamental properties arising from their complex structures. At Prof. Ghuman’s lab these complex materials are understood and improved using the advanced computational techniques.
Specifically, her laboratory is dedicated to
develop realistic computational models to predict the structural-property relationship in low-cost materials;
develop a high fidelity multiscale theoretical framework to connect the physics of crystalline and amorphous materials at different length scales and operating conditions; and
provide optimized design principles and deeper understanding of materials and chemical processes resulting in new directions to use inexpensive and abundant materials for photo- and electro- catalysis to produce sustainable fuels and chemicals, CO2 photo capture, and fuel cell applications. Designing Next-Generation Materials by Numerical Simulations
In recent years, Prof. Vidal's team has tackled various materials science subjects through numerical simulations in support of the experimental work carried out at INRS-EMT. These topics, which are still part of the research objectives of Prof. Vidal's team, mainly include:
Developing non-noble metal catalysts for hydrogen fuel cells. Fuel cells are part of the solution to exit the fossil fuel era, but they require efficient and inexpensive catalysts that have yet to be developed. Numerical simulations guides the experiments by predicting the catalytic properties of different combinations and atomic arrangements of common chemical elements in carbon matrices.
Predicting the generation of metal-organic frameworks (MOFs) by mechanochemistry. MOFs have applications in many technological fields. Although mechanochemistry allows to produce them at a relatively low cost, the process requires a better understanding in order to control the final product. Numerical simulations are used to predict the thermal and mechanical stability of MOFs and their formation sequence.
Understanding the properties of multiferroic materials. Multiferroic materials have the rare characteristic of possessing both electrical and magnetic properties. They are considered for various technological applications such as ferroelectric memories with non-destructive magnetic readout. Numerical simulations give insight into the physical origin of their properties and allow to predict those of materials not yet synthesized in the laboratory.
Rationalizing the doping of materials. Doping of materials is commonly used to improve some of their useful properties, such as photovoltaic properties, but the methodology often consists of trial and error. Numerical simulations provide a better understanding of how dopant atoms are distributed in materials and help provide a rational framework to guide future research.
Nanoplasmonics for Photocatalysis
Plasmonic effects can be exploited to build photocatalysts (i.e., photo-activated substances increasing the rate of a chemical reaction) that are active in the visible range. A typical arrangement combines TiO2 nanospheres (transparent in the visible) and gold (plasmonic) nanoparticles. However, plasmonic resonances cover the visible spectrum only partially and are endowed with weak light absorption. Prof. Razzari’s group employs electromagnetic design to improve the performance of plasmonic photocatalyst. The target is to obtain a reinforced plasmonic absorption and an enhanced local electric field at the metal-dielectric interface. These properties promote the generation of highly energetic electrons inside the plasmonic particle and their transfer towards the dielectric, where they become available for the reaction. For instance, "whispering gallery modes" (WGMs) have been employed, i.e., resonant modes that develop in a TiO2 sphere of proper size. WGM-assisted plasmonic photocatalysts have been exploited to produce hydrogen from water solutions, showing a significantly enhanced activity under visible illumination. Other explored designs comprise plasmonic photocatalysts incorporating carbon layers, hollow and porous spheres of TiO2, as well as different plasmonic units with distinct functionalities. Collaborations are in place for the chemical synthesis of the designed photocatalysts.
Computational imaging, image reconstruction & image processing for material characterization
Accurate characterization of materials is crucial in analyzing their optical, topological, and physical properties. However, existing instrumentations have limitations in imaging speed, detection sensitivities, and sample preparation. To overcome these limitations, we develop novel computational imaging modalities that consist of hardware-based image acquisition and software-based image reconstruction. We are currently working on applying compressed sensing paradigms, convex optimization algorithms, and image denoising modules in both system design, image reconstruction, and image processing. Modeling Laser-induced Phase Transformations We use multiscale simulations to investigate the interaction of high-intensity short pulse laser light with materials. We are interested in understanding the full process: from how the initial excitation transfers energy into the material to following the flow of heat and how the material structure responds as it undergoes a corresponding phase transformation. These models are developed to imitate real material and device processing conditions, and used to explain in situ measurements made with a Dynamic Transmission Electron Microscope in our lab. The ultimate goal is to use computational tools to predict the materials response and discover new routes to fabricate novel or hard to manufacture materials. Understanding the doping of organic semiconductors to design better materials for thermoelectrics Our work focuses on the understanding of the fundamentals of doping in organic semiconductors (OSCs). In particular, we are interested in coming up with novel design rules for dopant engineering. A major role for molecular doping of OSC is played by the various forms of disorder (structural, energetic), and by the role of intermolecular interactions (coulomb interactions, including those of higher order, intermolecular hybridization, etc.), all of which these impact how the charge transfer occurs.
We are also studying the doping of OSCs by alternative dopants such as Lewis acids, which are able to dope OSCs despite having an electron affinity well below what is typically needed. It was proposed recently that the doping can be mediated by a proton introduced by extraneous water. We are expanding on this idea by modelling interactions between Lewis acids and organic semiconductors in their pristine form and with water. Understanding better the fundamental aspects of doping and alternative doping mechanisms will lead to potentially less toxic dopants, and to greater lifetime in better-performing devices, which will greatly contribute to expanding the applicability of organic materials for thermoelectrics in the close future. Menu navigate_nextnavigate_next
Development of compressed ultrafast transmission electron microscopy for studying transient events in materials under Prof. Liang.
Designing efficient and affordable energy materials via computational modeling under supervision of Prof. Ghuman
Modelling Light-matter Interaction in Advanced Functional Material Devices.
ID-27337 Under Dr. Kenneth Beyerlein
Nano-Photonics for 2D Materials under Dr Luca Razzari
High-speed computational optical imaging for biomedical applications under supervision of Prof. Liang
Designing novel materials for catalysis and fuel cell applications via computational modelling ID-27920 under Dr. Kulbir Ghuman
Presented by students, postdocs, professors in Canada and global collaborators
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