Application of metal oxide semiconductor for detection of ammonia emissions from agricultural sources

Abstract

Agricultural emissions of ammonia (NH3) reduce air quality and biodiversity. Measuring the effectiveness of mitigations measures requires rapid monitoring tools, however, conventional methods are labour intensive and costly. This study evaluated the performance of a prototype metal oxide semiconductor (MOS) gas sensor for monitoring NH3. Conventional methods were used to calibrate sensor conductance. The metal oxide semiconductor (MOS) gas sensor was calibrated against NH3 released from a 0.1 M phosphate buffer spiked with ammonium chloride and NH3 released from recently spread cattle slurry. Field measurements using the MOS sensor were compared with values measuring a Bruker Open Path Air Monitoring System. Sensor conductance and NH3 concentration were described using single site Langmuir adsorption model. Field calibrations suggest a higher detection limit above 0.1 ppm and coefficients of determination were 0.93 and 0.89 for sensors 1 and 2, respectively. For prototypes deployed under field conditions, sensitivities of 2.2 and 2.4 with nonlinearity constants of 0.53 and 0.51, were found for sensor 1 and 3 respectively. Average R2 values were 0.88 for sensor 1 and 0.92 for sensor 3. The calibrations were used to calculate NH3 concentrations from slurry emissions using MOS sensor conductance. NH3 concentrations between 0.2 and 1 ppm, were measured with standard deviation of 20% of verified concentrations. The MOS sensor is sensitive enough to detect NH3 emission from agricultural sources with concentrations above 0.2 ppm. Low power and cost of MOS sensors are an advantage over existing techniques.

Description

Publication history: Accepted - 17 November 2022; Published online - 21 November 2022.

Keywords

Ammonia, Agriculture, Emission, Sensor, Metal-oxide semiconductor, Calibration

Citation

Molleman, B., Alessi, E., Krol, D., Morton, P.A. and Daly, K. (2022) ‘Application of metal oxide semiconductor for detection of ammonia emissions from agricultural sources’, Sensing and Bio-Sensing Research. Elsevier BV. Available at: https://doi.org/10.1016/j.sbsr.2022.100541.

DOI

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