Projets novateurs réalisés

L2M – Commercialization of Fluroine-free Ultra Water-repellent Coating Technology

The main applicant is leading a research-based venture spun out of Simon Fraser University that has developed a patented, PFAS-free ultra water-repellent (superhydrophobic) nanocoating technology. Our technology offers a more environmentally-friendly alternative to incumbent water-repellent coatings. The current goal of the venture is to explore the best technology-market match to commercialize the technology. Key operation activities include lab-scale R&D (material synthesis & characterization) and pilot trials with collaborators from the construction and textile industry.

L2M – ThermaSwitch: Passive Films for Adaptive Heat Management

This project aims to develop and validate a low-cost, energy-efficient film made from common or recycled plastics that can passively manage heat without using electricity or fuel. By microstructuring the surface of materials like polyethylene and polymethylpentene (TPX), we create thin, flexible films that can either retain or release heat depending on their arrangement. When two films are pressed together, they allow thermal radiation to pass through; when separated, they scatter it, creating a switchable system that mimics smart windows—but without electronics or coatings.

The research will focus on refining the production process using scalable mold-based manufacturing techniques and testing the films in real-world environments such as buildings or greenhouses. The system can reduce heating and cooling needs by passively managing thermal energy, helping reduce operational costs and carbon emissions. It offers a competitive advantage over existing insulation and reflective materials by being adaptable, affordable, and easy to integrate into current construction or agricultural practices.

The partner organization will benefit by gaining early access to a versatile and sustainable technology that aligns with energy efficiency goals and carbon reduction targets. This innovation supports their role in advancing clean technologies and can lead to new product lines, pilot projects, and potential commercialization. By participating in the development phase, the partner helps shape a product with real market relevance, strengthening their position in Alberta’s and Canada’s growing clean-tech economy.

Reliable, Continuous, and Safe Updates of Digital Twin Services

The intern Mr. Andersen will travel from Aarhus University, Denmark to Polytechnique Montréal, Canada, to research the engineering of Digital Twins (DTs). DTs are virtual representations of physical systems, like the concrete mixing machine which is the focus of this project. Mr. Andersen will work with Prof. Bentley Oakes on the research problem of ensuring that DTs and their services can be reliably deployed and continually updated in operation. The aim is to develop techniques such that DTs are trustworthy, up-to-date throughout their lifecycle, tested during development, and deployed afterwards without downtime. The project will create a technical architecture which creates mathematical models of the DT and system behaviour, and integrates these models with other formalized knowledge of DT and the service creation process. The approach will be validated on an industrial case study of a concrete mixing machine, developed at a Danish company. This research is part of Mr. Andersen’s PhD dissertation. Thus, he brings his expertise of DT engineering to the lab of Prof. Oakes to transfer this knowledge with the members of the lab. Likewise, Mr. Andersen will be trained in the research process of Prof. Oakes, bringing these new perspectives and experiences back to Aarhus University.

L2M – AI-Based Alarm Management System

Over the four-month Lab2Market internship, discussions with plant operators, maintenance teams, and technology vendors will determine how the existing AI alarm-diagnosis system should be fine-tuned for real-world deployment. Targeted interviews, and rapid feedback loops will uncover the highest-value use cases, reveal functionality gaps, and establish clear upgrade priorities. These market-discovery insights will provide us with a data-driven roadmap for refining features, ensuring seamless integration with control systems, setting pricing strategies, and identifying early adopters. Consequently, costly guesswork will be avoided, product-market fit accelerated, and a pipeline of potential pilot sites secured.

L2M – Optimizing Rheology for 3D-Printed Geopolymer Concrete

This project explores how sustainable concrete alternatives can be adapted for 3D printing in construction. Traditional cement-based concrete is a major source of global carbon emissions. Geopolymer concrete—made from industrial by-products like fly ash—offers a greener alternative, but challenges remain in making it printable at scale. This project will develop and validate a new testing method that simulates real-world extrusion behavior, helping ensure these materials can flow consistently through 3D printing systems. In parallel, stakeholder interviews will help assess market needs and potential applications. The goal is to bring environmentally friendly, printable building materials one step closer to real-world adoption.

Analyse comparative de l’impact carbone et systémique d’un spectacle d’art vivant au Québec

Ce projet a pour but de comparer les impacts environnementaux d’une production culturelle au Québec réalisée de façon traditionnelle avec ceux d’une production conçue selon des principes d’écoconception. Au-delà des émissions de gaz à effet de serre, l’étude prendra en compte d’autres effets sur l’environnement, comme la gestion des déchets ou l’utilisation des matériaux.
Le projet est réalisé en collaboration avec Écoscéno, un organisme montréalais qui aide les équipes de création à adopter des pratiques plus écoresponsables et à réutiliser les matériaux de décor. L’objectif est de produire des données claires et fiables qui permettront à Écoscéno de démontrer les avantages concrets de l’écoconception dans le milieu culturel. Ces résultats contribueront à faire reconnaître la valeur de ces pratiques auprès des partenaires du secteur, tout en renforçant l’expertise d’Écoscéno.

L2M Validate – Pi16 Therapeutics

The mission of PI16 Therapeutics is to improve skin health for the ~4 million Canadian’s suffering from long-term skin diseases, including clinically dry skin, chronic wounds, and severe burns. PI16 Therapeutics aims to produce skin solutions for these individuals by genetically engineering naturally occurring stem-cell like fibroblasts that express the gene Peptidase Inhibitor 16 (Pi16) with regenerative properties and packaging these cells in a clinical-grade hydrogel formulation. The resulting PI16+ fibroblast containing hydrogel will be directly applied to painful skin at hospitals and dermatology clinics to bring permanent relief by improving skin vitality, skin healing, and skin immunity.

L2M_BSI Proposal_L2M Health Validate Helixir

Our academic lab has developed technology that could have potential benefits in the Canadian healthcare system. This technology is highly specialized that combines MRI and interventional cardiac treatment. The intern will determine if our technology can satisfy the needs dictated by various stakeholders in the industry, such that a startup is warranted. This objective will be accomplished through conducting stakeholder interviews and analysing the data. Our supervisor and partner organization will provide tools and mentorship to conduct these stakeholder interviews and to verify if the final analysis is reasonable. This will accomplish the goal of the partner organization, which is to train Canadians to be able to spin out academic technologies into potential Canadian startups.

L2M – Market Validation and Human Factors Strategy for an Edge AI-Based Care Platform

This project focuses on validating the real-world use and integration of an advanced, AI-enabled platform designed to improve prenatal care through early detection of conditions such as gestational diabetes and preeclampsia. Rather than simply testing technical performance, the project places strong emphasis on human factors engineering and understanding how healthcare professionals interact with new technology in busy clinical settings. By working directly with obstetricians, nurses, and clinic staff, the project will assess usability, workflow compatibility, and overall clinician experience. This user-centered approach will identify potential barriers to adoption, streamline training and implementation strategies, and ensure that the platform supports rather than disrupts existing care routines. Insights gained will help create evidence-based recommendations for effective integration into prenatal care and potentially other healthcare areas in the future. Ultimately, this work aims to empower clinicians with tools that are intuitive, reliable, and seamlessly fit into their daily practices, supporting better patient outcomes and more efficient care delivery.

L2M – Advancing the Commercial Potential of a Novel Simulation Tool for Thoracic Surgery Training

This project will support the commercialization of a new medical training tool designed to help doctors learn how to perform technically complex procedures for cancer staging and diagnosis. The partner organization, University Health Network, has developed a realistic 3D-printed simulation model that enables trainees to safely practice this procedure outside the operating room. Unlike current training tools, this model is affordable, portable, and highly customizable to different learning levels. With the prototype already developed and tested, the next step is to determine how to implement this innovation in hospitals and training programs. Through this internship, the intern will conduct market research, speak with potential users, and develop a strategic roadmap to help launch the product. This work will provide the partner organization with the information it needs to grow the business, scale manufacturing, expand its impact, and make advanced surgical training more accessible worldwide.

L2M Validation / Qc Automne 2025 / Production de composites thermoplastiques avancés par pultrusion

Typiquement, les matériaux utilisés dans les composites ne sont pas recyclables. Lorsqu’une pièce est brisée ou qu’elle atteint sa fin de vie, elle peut être brulée afin de récupérer les fibres de renforts, mais plus souvent les pièces se dirigent directement vers un site d’enfouissement. Nous travaillons depuis environ 10 ans sur une méthode de production et des matériaux alternatifs plus résistants, pouvant être retravaillés et même recyclés. Cette technologie est la pultrusion de composite thermoplastique, elle permet la production de pièces en composite haute qualité de manière automatisée.
Les pièces créées par notre technologie peuvent être reformées en les chauffant, elles peuvent être utilisées comme renfort en surmoulage et peuvent même être soudées ensemble. Cette capacité de notre matière à être retravaillé nous permet aussi de la réparer dans le cas d’un bris mineur ou de la recycler entièrement lorsque la pièce atteint sa fin de vie.
Notre technologie permet de remplacer des procédés existant par une nouvelle alternative évitant les sites d’enfouissement comme solution de fin de vie, mais elle ouvre aussi de nouvelles portes. De tout nouveaux produits peuvent être élaborés avec les options de mise en forme uniques à notre technologie.

L2M – Developing Machine Learning Models to Accelerate our In-Vivo Creatinine and Potassium Biosensor Development

Many people with heart failure are not prescribed the full set of guideline-directed medical therapies (GDMT), which can shorten their lifespan and reduce quality of life. One major reason is the difficulty and inconvenience of regularly checking potassium and creatinine levels, which are needed to safely adjust these medications. To address this, we are developing the first on-demand system to measure potassium and creatinine, making it possible to optimize GDMT in weeks instead of years.

A key challenge we encountered is interference in electrochemical sensors: when other compounds in biological fluids create signals that interfere with accurate detection of the target molecule. Traditionally, solving this requires slow and labor-intensive trial-and-error testing of electrode materials in the lab. This delays development and limits how quickly new sensors can be brought to use.

Our solution is to use existing data and electrochemical knowledge to train machine learning models that can predict potential interferents and recommend better sensor designs before lab testing. This approach transforms biosensor development into a faster, data-driven process and could significantly accelerate the creation of reliable, clinically useful sensors.