McGill Alert / Alerte de McGill

Updated: Mon, 07/15/2024 - 16:07

Gradual reopening continues on downtown campus. See Campus Public Safety website for details.

La réouverture graduelle du campus du centre-ville se poursuit. Complément d'information : Direction de la protection et de la prévention.

NSERC/FRQNT NOVA program for early career researchers

The NOVA program for early career researchers is a grant program to support research projects led by early career researcher from Quebec. The program is offered jointly by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the This link will take you to another Web site Fonds de recherche du Québec Nature et technologies (FRQNT). The funding provided by this program will enable research teams to generate new knowledge in the natural sciences and engineering and to combine complementary expertise to carry out highly competitive research projects that can generate social, environmental, technological or economic benefits or have an impact on policies concerning issues of importance to the community.

Faculty Professors were successful in the NSERC/FRQNT NOVA joint funding program competition. Assistant Professor Elizabeth Zimmermann was awarded a grant for her project ‘Mechanical behavior and sustainability of green concrete made from green cement reinforced with functionalized recycled plastic’early career researchers. The award is $225,000 over three years plus $50,000 in equipment. Professor Marc McKee was a co-applicant and received $248,400 over three years for the project titled ‘Upsampling of low-resolution/large-volume 3D tomographic images using generative adversarial neural networks applied to biological anthropology, medical imaging and evolutionary biology’. Dr. Didem Dagdeviren is also a collaborator in this project. 

 

Mechanical behavior of green concrete made from green cement reinforced with functionalized recycled plastic:

PI: Elizabeth Zimmermann

Co-PI: Marta Cerruti, McGill University, Hamid Afshari, Dalhousie University

Collaborator: Faleh Tamimi

Summary: The overarching objective is to develop concrete with greater sustainability and lower environmental impact: green concrete. We bring together researchers from materials science and bioengineering for a fresh perspective to this materials design problem, combined with a PI from industrial engineering to assess the design’s social, economic, and environmental impact. Here, lessons learned from the natural design of biological materials are applied to concrete. The components and structure of biomaterials and tissues parallel concrete: Concrete is made with calcium silicate cement (Portland cement), whereas many biological materials and tissues contain biominerals, such as calcium phosphates (bone, enamel, fish scales), calcium carbonates (nacre, coral, eggshells) and calcium silicates (radiolaria). Whereas concrete in structural components is reinforced with steel, biological materials and tissues have an organic component, such as collagen, providing toughness and ductility. This organic component can take many forms from the Bouligand structure in armorlike fish scales to organics within and between calcium carbonate grains in seashells. Similarly, both concrete and biological materials combine these biominerals and reinforcements to produce a composite material with better mechanical properties than their constituents alone.

Upsampling of low-resolution/large-volume 3D tomographic images using generative adversarial neural networks applied to biological anthropology, medical imaging and evolutionary biology

PI: Natalie Reznikov

Co-PI: Marc McKee and Nelson, A.

Collaborator: Didem Dagdeviren

X-ray based 3D imaging methods are important in the clinical fields of medicine and dentistry, and they are a cornerstone research method in biomineralization, skeletal biology, biological anthropology, and medical and dental radiology, amongst others. This project aims to revolutionize the field of bioimaging by using deep learning neural network-based tools to develop a new upsampling algorithm to significantly enrich information available from X-ray-based computed tomography including cone-beam CT scans. This will be implemented by augmenting the resolution of large-volume data with simultaneous 3D image segmentation, the latter being a crucial prerequisite for quantitative bioimaging research.

 

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