The Maker Space self-directed group at KAUST promotes DIY culture and provides training on using machines, tools, and materials. In March 2017, Maker Space launched the "Design for KAUST" workshop in collaboration with the University’s Residential Maintenance Department. The winning teams in the workshop received sponsorship, including a total of SAR 10,000 in prizes, a Local Impact Award and an opportunity to test the prototypes in the field. Why it matters: This initiative fosters innovation and problem-solving within the KAUST community, addressing practical challenges in daily life through technology and promoting local impact.
KAUST researchers have fabricated and tested high-efficiency perovskite-silicon tandem solar cells optimized for hot climates. The tandem device is more stable than conventional perovskite cells and optimized for industry use. Outdoor testing at KAUST confirmed performance improvements, indicating bromide-lean perovskite top cells with narrower bandgaps are ideal. Why it matters: The research demonstrates the viability of tandem silicon-perovskite cells in harsh environments, paving the way for more efficient solar technology in the region and globally.
Researchers have developed a scalable pre-screening framework that integrates climate and remote sensing data to identify cost-efficient sites for sustainable dryland restoration, using Saudi Arabia as a case study. The framework employs machine learning models to derive a Climate Suitability Score (CSS), which captures climatic dependencies on vegetation persistence. National-scale prediction maps were generated using multi-year ERA5-Land data for Saudi Arabia, leading to the identification of thirteen priority locations with an estimated potential for a 2.5-fold increase in vegetation coverage. Why it matters: This approach significantly reduces the search space and costs associated with restoration efforts, supporting more resilient and sustainable ecosystem recovery planning in water-limited regions of the Middle East.
KAUST researchers developed a tandem solar cell with 32.5% conversion efficiency by optimizing the silicon-perovskite connection. Another team combined spectroscopy and reactor technologies to reveal details on catalyst function and reaction mechanisms. A KAUST team also developed a mathematical framework improving data rates by 30% and optimizing terrestrial network speeds. Why it matters: These advances highlight KAUST's contributions to sustainable energy, industrial processes, and network optimization, addressing key challenges in the region and globally.
A KAUST-led team developed a superabsorbent polyacrylate film for passive cooling, combining radiative and evaporative techniques without extra energy. The film uses sodium polyacrylate to absorb moisture and form a reflective film, reducing solar heating. Experiments showed the film lowered temperatures by five degrees Celsius, with simulations indicating a 3.3 percent reduction in total energy consumption. Why it matters: This innovation offers a sustainable alternative to traditional cooling systems, reducing carbon emissions and strain on energy grids in hot climates.
KAUST's Imaging and Characterization Core Lab (IAC) co-hosted a materials science optical microscopy workshop with Leica Microsystems. The workshop included hands-on training led by IAC staff scientist Ebtihaj Bukhari and Leica specialist Philippe Vignal. Researchers from KAUST, King Abdulaziz University (KAU), and Obeikan participated in the event. Why it matters: Such workshops contribute to developing local expertise in advanced materials science techniques, crucial for Saudi Arabia's industrial and research sectors.
MBZUAI is hosting a short course on developing open-source machine learning packages. The course will be led by Chih-Jen Lin, an affiliated professor at MBZUAI and distinguished professor at National Taiwan University, who has developed widely used ML packages like LIBSVM and LibMultiLabel. The course will cover topics such as starting a project, choosing functionalities, and identifying research problems from user feedback. Why it matters: This course can help improve the quality and usability of open-source machine learning tools coming from the region's research institutions.