This project develops correlations for ignition delay times and laminar flame speeds of fuel blends.

Oxygen-enriched combustion boosts efficiency and eliminates NOx, but requires validated chemical models for real-world methane combustion prediction.

This study validates the AramcoMech and UoS sCO2 mechanisms for hydrogen, methane, and syngas under high CO2 dilution, identifying key reactions and suggesting model improvements.

This project enhances combustion science by improving fuel oxidation understanding.

Our UV absorption diagnostics offer unparalleled sensitivity and in-situ, non-intrusive measurements, enabling high-fidelity kinetic experiments of radicals and molecules, improving our understanding of conventional and biofuel combustion.

This project uses the Design of Experiments (DOE) approach to screen metallic combinations and tailor support properties, aiming to optimize catalytic activity for hydrocarbon combustion and improve resistance to poisoning and deactivation.

This project focuses on the decomposition of hydrogen sulfide (H₂S) to produce hydrogen, using ultraviolet (UV) light.

We use AI and deep learning with laser absorption spectroscopy to enable simultaneous multi-species detection using a single laser, enhancing robustness, accuracy, and reducing complexity for real-time applications.

We developed a fast dual-comb spectrometer in the 7.5–12 µm MIR region for simultaneous multi-gas detection with high temporal resolution, ideal for combustion studies and environmental monitoring.

This project developed a calibration-free laser sensor using NIR absorption spectroscopy for real-time, high-precision measurement of water content in oil-water mixtures (0%–100%), ideal for the oil and gas industry.

We developed a laser-based sensor utilizing a DFB interband cascade laser (DFB-ICL) and convolutional neural networks (CNNs) for the selective and simultaneous detection of C1-C5 alkanes (methane, ethane, propane, n-butane, and n-pentane) in natural gas samples.

This project developed a selective laser-based sensor using DFB-ICL and DNNs for real-time BTEX detection, ideal for air quality monitoring in petrochemical industries.

Our project develops a laser-based system combining open-path optical communication and H2S gas sensing with an 8 µm QCL, enabling real-time, long-distance monitoring in industrial settings.

This project investigates how exhaust gas recirculation (EGR) and varying levels of nitric oxide (NOx) affect the autoignition behavior of Euro 6 oxygenated gasoline in advanced combustion systems.

Our work looks at novel applications of shock tubes for material synthesis/modification and biomedical applications like drug delivery.

Combustion characteristics and property prediction of sustainable aviation fuel (SAFs) blends.

This project investigates the shock velocity profiles in double-diaphragm shock tubes.

This project uses dual-camera high-speed imaging to study ignition behavior in shock tubes, focusing on ethanol, methanol, and n-hexane.

This project explores the development and application of diaphragmless shock tubes for interdisciplinary research.