Overview

MD develops and applies advanced computational methods and materials to support and advance research and development within Energy, Water, Environment, optoelectronics, and advanced manufacturing.

Projects

This project advances thermoelectric (TE) materials for sustainable energy conversion by enhancing efficiency through novel material design. A collaborative team from QEERI, HBKU, QU, and NTU will develop thin-film TE materials via Molecular Beam Epitaxy and bulk materials through solid-state reactions. Advanced characterization and TE measurements will establish efficiency-structure correlations, while nanocomposites for radiative cooling will optimize performance. The project will also develop TE device prototypes for energy applications, aligning with Qatar’s National Development Strategy to promote zero-carbon energy solutions.

This project advances rectenna technology by developing high-efficiency, cost-effective devices that convert electromagnetic waves into DC power. Using an in-house molecular 3D-printing technique, new 2D materials are fabricated through molecular self-assembly, overcoming traditional limitations. The project follows a licensing-based business model, focusing on IP licensing to encourage industry adoption and sustain innovation. By advancing clean energy technologies and electronics, it contributes to a more sustainable and connected future.

This project develops advanced gas sensors for confined spaces in the Oil & Gas industry, enhancing safety and real-time monitoring. Using Molecular Beam Epitaxy, it explores nanomaterials like CZTSSe, 2D materials, and carbon nanostructures to improve sensor sensitivity, response time, and durability. RFID-enabled sensors will enable remote monitoring and IoT integration for real-time data collection. The outcome is a highly sensitive, reliable, and scalable gas detection system, reducing accidents and improving operational efficiency in harsh environments.

This project enhances PV deployment in desert climates by addressing soiling-related energy losses through data-driven solutions. It integrates environmental analysis, field measurements, and AI-driven predictive modeling to understand and forecast soiling dynamics. Key mitigation strategies include optimized cleaning schedules and advanced coatings to reduce dust accumulation. Deliverables include predictive soiling maps, performance models, and cost-benefit analyses to improve PV efficiency and durability. The project supports Qatar’s renewable energy goals and provides scalable solutions for arid regions worldwide.