Sustainability

Sustainability is a broad term that cuts across different disciplines with the unifying theme of improving the state and availability of desirable materials over the long term and being accountable to the environment. This research focuses on efforts to migrate from fossil fuel use for industrial heating toward more sustainable and eco-friendly methods. This endeavor opens the door to the following opportunities:

Research on electrification techniques such as Joule heating, induction and microwave heating, for large-scale industrial heating processes.
Development of innovative methods for effective catalytic reactions where the catalysts are embedded within conductor material to improve selectivity.

Creation of sustainable methods for dehydrogenation (e.g. methane, propane dehydrogenation reactions, etc.,).
Development of efficient electricity-driven catalytic reactors which facilitate the direct supply of energy to catalytic sites.

Fagbemi et al. (under prep.)

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Sustainable Material Development

There is a need for alternative materials that can rival non-sustainable materials such as plastics while offering similar or better performance. We carry out the following to achieve this aim:

Coating of bio-based polymers on porous substrates to improve their mechanical, thermal, antibacterial, and barrier properties.
Development of smart materials that are sensitive to stress and environmental conditions such as humidity, temperature, etc.
AI-enabled nanoparticle syntheses, process parameter optimization, and green manufacturing.

Aspects of Sustainability

Fagbemi et al. (under review)

Manufacturing Methods

Improved scalable manufacturing techniques such as slot die or blade coating running at lab scales for easier pilot-scale transition. Such innovative methods can be used to:

Accelerate the development of efficient protocols in process-structure-property relationship cycles.
Enable multiple-layer simultaneous coating or co-deposition for high-fidelity and faster coating processes.

Process Optimization

Manufacturing processes need to be optimized to minimize material wastage and processing times. We achieve this in our research by:

Improving processing parameters to reduce waste and increase productivity.
Optimizing process parameters using digital twins with the application of CFD modeling.
Tuning process parameters using AI models. For example, we use AI to optimize the coating process to ensure certain controlled characteristics, such as coating thickness, porosity, and pore-size distribution in the final product.
Applying AI for nanoparticle (NP) syntheses to ensure desired nanoparticle characteristics are obtained