Rising demands on fossil fuel power systems to lower pollution levels and increase efficiency introduces a need to accurately detect and quantify emission gasses. Novel sensors capable of monitoring gasses throughout a fossil fuel power system will improve regulatory monitoring capabilities and enable energy-saving practices. Micro-Electro-Mechanical-System (MEMS) based gas sensors have shown promise in measuring gas concentrations in various environments, but several barriers still remain to the implementing MEMS gas sensors in high temperature power generation systems: (1) fast degradation of materials under harsh environments at temperatures & gt; 500C, (2) insufficient reliability of detected signals, (3) lack of selectivity for sensing specific gasses in mixed-gas environments, and (4) difficulty in scaling to produce advanced multi-sensor platforms capable of sensing multiple gasses simultaneously. The goal of this work is to introduce an advanced titania-based multivariate MEMS gas sensor that overcomes the debilitating factors previously mentioned. Our approach is to use oxidation-resistant substrates such as (Ti,Al,Cr) alloys to produce arrays of doped nano-structured TiO2 MEMS gas sensors and combine these on a single platform. A single-platform, multi-sensor system enables us to accurately detect multiple gasses simultaneously including O2, H2, CO2 and NOx. The sensor is designed to be effective in harsh, multi-gas environments at temperatures and pressures ranging from 20C to 1200C and 15 to 1000 psi. The proposed sensor is based on several subsystems, each of them dedicated to the detection of a specific gas, and constituted of 64 MEMS sensors. Contrary to previous approaches based on solo sensors, all 64 redundant sensors of each subsystem are dedicated to detecting a single target. Nanostructured TiO2 (NST) layers will be used as the sensing material, and will be doped with elements such as Pt, Al or Cr to increase the MEMS sensor sensitivity to a specific targeted gas. The detecting signals are collected from each individual sensor and processed using multi-sensor data fusion technology. Data fusion will be utilized to efficiently compare and combine the collected data from multiple sensors to provide reliable and accurate signals not achievable with a single sensor. The primary application of the proposed sensor will be to monitor emitted combustion gasses for coal power generation plants, gas turbines, and gasifiers. The value incentive for end users is to accurately monitor gas concentrations to improve system efficiency, minimize ware on equipment, and produce accurate emissions reports for regulatory purposes. Leveraging existing PiMEMS patents and the extensive experience of project members position PiMEMS to successfully develop and commercialize the proposed sensor.
