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(University of New Brunswick, 2025) nason
Design, manufacturing, and functionality assessment of an engineered nanocomposite filter with hydrophilic and microbial resistant properties using electrospinning method for air ventilation systems
(University of New Brunswick, 2024-08) Ireland, Kelton; Saha, Gobinda C.; Afzal, Muhmmad T.
Air quality is a growing concern around the world due to the increased presence of pollution and microorganisms. Small particles, both organic and inorganic, suspended in the air have been linked to significant health problems such as respiratory and cardiovascular issues along with infections from bacteria and viruses. The most common way to improve air quality inside residential or industrial buildings is by using fibrous filter materials with heating, ventilation, and air conditioning (HVAC) systems. Hospitals require high efficiency filters to reduce air transmission of microorganisms to ensure a safe environment for recovering patients. Commercial fibrous filters can obtain high filtration efficiencies however, bacteria and viruses still pose a risk if they survive on the fibers for extended periods of time. The present study focuses on the development and investigation of a nanofiber nanocomposite filter with hydrophilic and microbial resistant properties for the potential use in the medical sector.
The filter was developed based on thermoplastic polyurethane (TPU), with the incorporation of graphene oxide (GO), and cellulose nanocrystal (CNC) as nanofiller reinforcements. Electrospun nanofibers with diameters less than 350 nm were successfully deposited as both a standalone membrane and nanofiber coating. The nanocomposite nanofibers were characterized by experimental and analytical methods and the electrospinning parameters were investigated utilizing modelling techniques. The techniques employed gave insight into the tensile, viscoelastic, hydrophilic, antibacterial, and filtration properties of the nanocomposite and how the properties were affected by the nanofillers and process parameters.
The results indicated that at a loading of 3 wt.% of GO, CNC, and a combined GO/CNC the tensile properties remained unaffected. It was observed that the base TPU nanofibers showed good bacterial resistance against E. coli. The nanomaterials were able to delay the glass transition temperature and made the filter more hydrophilic. With regards to the filtration capabilities the presence of CNC increased the filtration efficiency from 84.24% to 91.83% while GO did not affect the filter properties on its own.
Overall, the presented work provided valuable insight into the interactions of GO and CNC in a three-constituent nanocomposite produced via electrospinning and its feasibility towards antibacterial/industrial air filtration.
Conflict, hope, and mathematics education storylines: Pivoting away from a pathology-based orientation.
(North American Study Group on Ethnomathematics, 2023-06) Gerbrandt, Julianne; Wagner, David
In this paper, we play with the ideas of conflict and hope in reported storylines from subaltern contexts of mathematics learning. The concept of storyline comes from positioning theory, which suggests that people make choices about communication acts according to known or familiar storylines. By drawing attention to aspects of conflict and hope within storylines, we identify pivot points that permit reorientation. By deconstructing several storylines from the Mathematics Education in Indigenous and Migrational contexts project, we noticed how storylines that feature conflict offer more opportunities to pivot than do storylines that feature appeals to hope. This process of reorientation resists the dominance of pathology-based storylines about mathematics
education for students from minoritized groups and draws attention to the impact of orientation on storylines.
Challenges, opportunities, and key questions in research for Mathematics in Indigenous and Migrational (MIM) contexts through a language-focused lens.
(North American Study Group on Ethnomathematics, 2023) Culligan, Karla; DeWolfe, Sacha; Simensen, Anita Movik
This paper presents some challenges, opportunities, key questions, and ways forward for research in mathematics in Indigenous and Migrational (MIM) contexts as discussed by the two featured panelists and mediated by the moderator in the closing symposium of the MIM Conference in Alta, Norway in November 2022. Punctuated with quotations, photos and images, the paper begins by introducing the three researchers, their contexts, and their respective research interests. Next, the paper unfolds as a discussion organized around the four main points (challenges, opportunities, key questions, ways forward). The moderator invited the panelists to examine these discussion points with a view towards the role of language in their respective contexts and research, therefore the theme of language features throughout. The paper concludes with a synthesis of common threads that emerged through the discussion and a focus on action moving forward.
Machine learning-driven improvements for efficient routing in advanced metering infrastructure for Smart Grids
(University of New Brunswick, 2024-08) Duenas Santos, Carlos Lester; Meng, Julian; Mohamad Mezher, Ahmad
Smart Grids envisioned as the next-generation power grid, aim to revolutionize the way electricity is generated, distributed, and consumed. They promise enhanced efficiency, reliability, and greater integration of renewable energy sources. However, the achievement of these ambitious objectives is deeply dependent on the robustness and sophistication of Advanced Metering Infrastructure (AMI) systems.
AMI is a critical component of Smart Grids, providing essential real-time data and two-way communication capabilities that are fundamental to the smart management of energy resources. Through AMI, Smart Grids gain the ability to dynamically respond to changing energy demands, implement efficient demand-response strategies, and offer consumers greater control over their energy usage.
AMI systems rely heavily on robust and reliable communication networks. However, several critical challenges and limitations persist in the current communication networks used within AMIs, necessitating further research and innovation in this domain. One of those challenges is the way the data packets are routed so that they reach their intended destinations.
Thus, this PhD thesis focuses on addressing the inherent limitation of the current routing protocols employed in AMI deployments. In pursuing this goal, three routing algorithms are proposed, RPL+, ML-RPL, and Q-RPL. They take as a base the Routing Protocol for Low Power and Lossy Networks (RPL) and on top of this implement Machine Learning-driven mechanisms to improve the routing process.
The developed routing algorithms are validated through extensive simulations and experiments in representative AMI deployments. Evaluation criteria include typical metrics to measure the performance of communication networks such as packet losses, and latency.
When the proposed routing algorithms are compared to the standard RPL implementations and other benchmark algorithms found in the literature, it is observed a significant improvement in network performance, which underscores their potential. These advancements represent a more robust, efficient, and reliable communication system within Smart Grids and lay the groundwork for future innovations in communication technologies in this context.