Dr. Aamir Farooq

On January 12, 2023, Dr. Aamir Farooq from King Abdullah University of Science and Technology (KAUST) will be presenting on his research findings. This event will be held in the Food Processing Technology Building auditorium on the NARA campus. 

Professor Aamir Farooq is a member of the Physical Sciences and Engineering Division in the Clean Combustion Research Center (CCRC) at KAUST. Dr. Farooq received his Ph.D. in Mechanical Engineering from Stanford University in 2010, and then joined KAUST. He was promoted to the rank of full Professor in 2022. He is the principal investigator of the Chemical Kinetics and Laser Sensors Laboratory in the CCRC at KAUST. His research interests are in the areas of energy, chemical kinetics, spectroscopy, and laser-based sensors. He has authored over 180 refereed journal articles and has given invited talks at a number of international conferences. At KAUST, he won the Distinguished Teaching Award, given to the best instructor over a period of two years. In 2019, Dr. Farooq was awarded the prestigious Hiroshi Tsuji Early Career Research Award by Elsevier and the Combustion Institute. In 2020, he received Research Excellence award by the Combustion Institute. He became a fellow of the Royal Society of Chemistry in 2022.

The presentation details are as follows:

Title: Mid-IR Laser Sensors for Chemical Kinetics and Environment


Sensors based on laser absorption spectroscopy (LAS) have achieved widespread usage in research and practical applications due to their simple architecture, ease of implementation, and field deployment. Recent years have witnessed strong emphasis on the mid-IR wavelength region of the electromagnetic spectrum due to the presence of strong fundamental vibrational bands of many species of interest, availability of laser systems and the opportunities to do sensitive as well as selective detection. This talk will briefly describe some of the recent mid-IR sensing work being carried out at KAUST. This includes the measurements of a family of aromatic molecules, BTEX, for environmental monitoring by exploiting cavity-enhanced absorption and deep-neural networks. This work was then extended to multi-species measurements in shock tube chemical kinetic studies. Machine-learning methods were exploited to overcome non-linear blending in liquid fuel samples and to do multi-species quantification from noisy spectroscopic data. The access to the far-wavelength mid-IR was enabled recently in the PI’s lab by the design and development of a difference-frequency-generation laser system, and this laser was used for the study of benzene and HCN kinetics.