Nanomaterial-Based Sensors for Hydrogen Sulfide Detection in Petroleum Operations: From Colorimetry to Machine-Learning-Assisted Platforms

Hydrogen sulfide (H₂S) is a toxic, corrosive, and flammable gas generated in petroleum operations, from sour reservoirs to refining and gas processing, and remains a leading cause of occupational fatalities in the industry. Conventional sensors (such as electrochemical, metal-oxide, and infrared) suffer from cross-sensitivity, humidity dependence, and sensor drift, motivating the search for alternative sensing paradigms. Nanomaterial-based sensors (colorimetric, fluorescent, and machine-learning (ML)-assisted arrays) exploit the tunable optical, catalytic, and structural properties of plasmonic nanoparticles, metal-organic frameworks (MOFs), porous organic cages (POCs), and cross-reactive sensor arrays. We examine five major classes of these platforms, namely metal-salt paper sensors, gold and silver plasmonic nanoparticles, MOFs, machine-learning-assisted sensor arrays, and POCs, comparing their sensing mechanisms, detection limits, and selectivity against key interferents including chloride ions, mercaptans, SO₂, and CO₂. While lab performance is excellent, a consistent gap exists between demonstration and validation under realistic conditions (high humidity, temperature, competing sulfur species, and hydrocarbon matrices). ML-integrated arrays and multimodal MOFs are promising near-term strategies to bridge this gap, whereas POCs offer record H₂S capture capacities but lack field-ready devices. We conclude by outlining priority directions (standardized testing and validation in produced water/sour-gas) to translate these platforms from prototypes to deployable H₂S safety tools.

کلیدواژه ها:

Hydrogen sulfide ، Colorimetric sensors ، Nanomaterials ، Metal-organic frameworks ، Plasmonic nanoparticles ، Petroleum industry