Currently, low power Metal Oxide Gas Sensors (MOXs) are widely employed in gas detection because of its benefits, such as high sensitivity and low cost. However, MOX presents several problems, as well as lack of selectivity and environment effect. Semiconducting Zinc Oxide was used for sensing methane, where alloys of noble metals, mostly pure or binary alloys, were used to increase the sensor parameters of the device. Experimental results of such noble metals or their binary alloys were used to develop the corresponding artificial neural network model describing the three pivotal attributes of the sensor device, viz. response magnitude, response time and recovery time. The models were used in a novel approach to design ternary alloys with superior performance using multi-objective optimization technique, where the Pareto front thus developed was used for designing ternary catalysts. The present scheme of prescriptive data analytics seems to provide some definite clue for experimental study aiming the pre-determined set of sensor parameters.

Gas Sensor parameters, Ternary Alloy Design, Artificial Neural Network, Multi-Objective Optimization

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