L2M- Innovative Metal Powder Solution- Transforming metal Waste into Sustainable Additive Manufacturing Materials

NexaPowder Inc. aims to revolutionize metal 3D printing by recycling scraps and waste into affordable, eco-friendly metal powders. This will slash costs by half and reduce carbon emissions by 80%, making metal 3D printing more accessible and sustainable. The Lab2Market Launch program offers NexaPowder tailored support to develop vital business skills and secure funding, helping […]

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L2M- Remotely Controlled Bio-Inspired Additive Manufacturing Robot (RemoraAM Bot)

Inspired by the Remora fish we have developed a remotely operated vehicle (ROV), specifically designed for underwater maintenance using metal additive manufacturing principles. Targeting offshore structures and Navy vessels as our main clientele, our ROV will not only facilitate repairs conducted by professional divers but also undertake the rebuilding of damaged parts without dry docking. […]

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L2M – Electroless nickel-phosphorus composite coating for molding application

The proposed project aims to develop an electroless nickel-phosphorus composite coating for tire molds to address common issues such as product sticking and damage during demolding. By applying this coating, we aim to provide semi-permanent protection and anti-sticking properties to the molds, reducing the need for frequent application of releasing agents. This innovation is expected […]

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Design, synthesis and characterization of conjugated organic materials for printed electronics applications.

During this dual internship, I will be involved in the PIONEER project (PhotoactIve OrgaNic polymErs for watER treatment). It has been demonstrated that through light irradiation, BTI (benzothioxanthene imide) in water generates hydroxyl radicals (OH•), with their recombination product being hydrogen peroxide (H2O2). Hydrogen peroxide serves an antifouling function due to its compatibility with living […]

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“Improving Flexural Strength Predictions in Composite Materials using Image Processing and Machine Learning”

This project aims to improve the way we assess the strength of short fiber reinforced composites, focusing on sustainability. By exploiting the distinct visibility traits of PEEK and carbon fibers in CT scans, the study will utilize non-destructive testing and computer algorithms to analyze and measure factors critical to the material’s strength directly from scan […]

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Integrating graph-based data management into materials acceleration platforms

This research project aims to significantly improve the way data are managed in a specific self-driving laboratory in the AUTODIAL group of Prof. Hattrick-Simpers at the University of Toronto, focusing on discovering new materials that are resistant to corrosion. This class of labs, known as Self-driving labs (SDL) or Materials Acceleration Platforms (MAPs), use advanced […]

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Asymmetric Semiconducting Polymers; Electronic and Mechanical Properties in Organic Electronics

Semiconducting polymers (SPs) are at the forefront of the next generation of organic electronics. They enable seamless integration into various biological and industrial applications. SPs can be engineered to be mechanically compliant and soft, giving them an advantage over silicon-based electronics. Their electronic and solid-state properties also make them promising candidates for emerging organic electronics. […]

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Investigating the Impact of SALD Materials on CVD 2D Tungsten Disulfide

This project aims at improving LED technology through the utilization of advanced atomically thin materials. It seeks to create a scalable method for fabricating high-performance, energy-efficient, and flexible LEDs, addressing challenges in scalability and reproducibility. By combining CVD-grown transition-metal dichalcogenides (TMDCs) with spatial atomic layer deposition (SALD)-grown semiconducting oxides, the project promises to advance research […]

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Self Supervised Learning of Embeddings for Semi-Supervised Track Classification

Total::Insight™ is a geospatial and time distribution Decision Support System (DSS) that includes a correlator capable of consuming many sporadic time domain signals and converting them into feature rich tracks. The problem here is how to create an AI/ML embedding that is domain relevant from the tracks.The project objective is to develop, refine and industrialize […]

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A generalizable bilevel reinforcement learning model to solve large-scale unrelated parallel machine scheduling problem with sequence-dependent setups in real-time

Our research addresses the challenges in solving large-scale parallel machine scheduling, an important combinatorial optimization problem in computer science and operations research. With applications ranging from manufacturing to healthcare and supercomputing, our goal is to provide a real-time solution for instances exceeding 1,000 jobs. In this research, we explore the application of parallel machine scheduling […]

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Development of 3D printable inks for wound dressing applications

it is planned to devise 3D printable PLA-PEG gels hosting st EOs (called emulgels). The primary objective of this research is to develop innovative methods to manufacture multifunctional 3D-printed hydrogels that can be applied to wound treatments. The challenges include stabilization of EO droplet in gel media along with engineering the rheological properties of emulgels […]

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Using Multi-Material 3D Printing to Strengthen Biocomposites with Superhydrophobic Plating

This project will focus on reinforcing biocomposite materials with a superhydrophobic plating to help improve the mechanical properties and industrial feasibility. Biocomposites are currently being widely researched because of their numerous advantages over materials traditionally used in manufacturing such as being easily recyclable, being made from materials which are found abundantly in nature, and having […]

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