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Sensors 2018, 18(11), 3858; https://doi.org/10.3390/s18113858

Optimization of Gas Sensors Based on Advanced Nanomaterials through Split-Plot Designs and GLMMs

1
Department of Statistics Computer Science Applications “Giuseppe Parenti”, University of Florence, Viale Morgagni 59, 50134 Florence, Italy
2
Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy
*
Author to whom correspondence should be addressed.
Received: 3 October 2018 / Revised: 31 October 2018 / Accepted: 31 October 2018 / Published: 9 November 2018
(This article belongs to the Special Issue Nanostructured Surfaces in Sensing Systems)
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Abstract

This paper deals with the planning and modeling of a split-plot experiment to improve novel gas sensing materials based on Perovskite, a nano-structured, semi-conductor material that is sensitive to changes in the concentration of hazardous gas in the ambient air. The study addresses both applied and theoretical issues. More precisely, it focuses on (i) the detection of harmful gases, e.g., NO 2 and CO, which have a great impact on industrial applications as well as a significantly harmful impact on human health; (ii) the planning and modeling of a split-plot design for the two target gases by applying a dual-response modeling approach in which two models, e.g., location and dispersion models, are estimated; and (iii) a robust process optimization conducted in the final modeling step for each target gas and for each gas sensing material, conditioned to the minimization of the working temperature. The dual-response modeling allows us to achieve satisfactory estimates for the process variables and, at the same time, good diagnostic valuations. Optimal solutions are obtained for each gas sensing material while also improving the results achieved from previous studies. View Full-Text
Keywords: experimental design; Generalized Linear Mixed Models; metal oxide semiconductors; resistive gas sensors; robust process optimization experimental design; Generalized Linear Mixed Models; metal oxide semiconductors; resistive gas sensors; robust process optimization
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Berni, R.; Bertocci, F. Optimization of Gas Sensors Based on Advanced Nanomaterials through Split-Plot Designs and GLMMs. Sensors 2018, 18, 3858.

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