KAUST Ph.D. students Sandra Medina and Luisa Javier created WAYAKIT, a compact, organic, and portable multi-cleaner and odor remover for travelers. Their biotechnology-based startup, WAYAK Group, aims to transform the laundry industry with affordable, low-resource solutions. WAYAKIT uses biotechnology to degrade odor-causing molecules and solubilize stains. Why it matters: This showcases KAUST's entrepreneurial environment and the potential for scientific research to address practical, everyday challenges with sustainable solutions.
KAUST researchers have developed a green synthetic biology approach using engineered algae to replicate the complex fragrances of agarwood, also known as oudh. They catalogued the chemical diversity of sesquiterpenes (STPs) in 58 agarwood samples and reproduced some of the chemical complexity of agarwood STPs in algae using synthetic biology. The team used the green alga Chlamydomonas reinhardtii to produce nine distinct STP chemical products widely found in agarwood, offering a sustainable alternative to harvesting endangered trees. Why it matters: This research provides a sustainable route for producing sought-after fragrances, reducing pressure on endangered agarwood tree populations and promoting green chemistry in the region.
KAUST startup uODS signed an MoU with Saudi Aramco Base Oil Company (Luberef) to develop and deploy technology removing sulfur from hydrocarbons. The uODS process, based on KAUST's sonochemistry research, reduces sulfur in marine fuels to meet IMO 2020 regulations. Luberef aims to reduce its environmental footprint by piloting the uODS technology at its Jeddah refineries, with uODS set to produce 10 tons per day of desulfurized fuel for testing. Why it matters: The partnership demonstrates KAUST's role in addressing Saudi Arabia's environmental goals and showcases the potential of university spin-offs to contribute to a more sustainable oil industry in the region.
KAUST and Saudi Aramco collaborated to develop a laser-based sensor for detecting trace amounts of gas leaks in petrochemical plants. The sensor uses machine learning to identify specific gases, differentiating it from previous sensors that only detect large leaks. The technology can differentiate between closely related industrial gases like benzene, toluene, ethyl benzene and xylene (BTEX). Why it matters: This innovation enables proactive monitoring and rapid pinpointing of leaks, enhancing safety, environmental protection, and operational efficiency in the petrochemical industry.