Next Article in Journal
Acoustic Emission and Echo Signal Compensation Techniques Applied to an Ultrasonic Logging-While-Drilling Caliper
Next Article in Special Issue
CeO2 Enhanced Ethanol Sensing Performance in a CdS Gas Sensor
Previous Article in Journal
Development and Validation of a New Near-Infrared Sensor to Measure Polyethylene Glycol (PEG) Concentration in Water
Previous Article in Special Issue
A Study of the CO Sensing Responses of Cu-, Pt- and Pd-Activated SnO2 Sensors: Effect of Precipitation Agents, Dopants and Doping Methods
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(6), 1352; doi:10.3390/s17061352

Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design

1
Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100 Siena, Italy
2
Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, Viale Morgagni 59, 50134 Florence, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Giovanni Neri
Received: 8 April 2017 / Revised: 1 June 2017 / Accepted: 4 June 2017 / Published: 10 June 2017
(This article belongs to the Collection Gas Sensors)
View Full-Text   |   Download PDF [783 KB, uploaded 13 June 2017]   |  

Abstract

Eight different types of nanostructured perovskites based on YCoO 3 with different chemical compositions are prepared as gas sensor materials, and they are studied with two target gases NO 2 and CO. Moreover, a statistical approach is adopted to optimize their performance. The innovative contribution is carried out through a split-plot design planning and modeling, also involving random effects, for studying Metal Oxide Semiconductors (MOX) sensors in a robust design context. The statistical results prove the validity of the proposed approach; in fact, for each material type, the variation of the electrical resistance achieves a satisfactory optimized value conditional to the working temperature and by controlling for the gas concentration variability. Just to mention some results, the sensing material YCo 0 . 9 Pd 0 . 1 O 3 (Mt1) achieved excellent solutions during the optimization procedure. In particular, Mt1 resulted in being useful and feasible for the detection of both gases, with optimal response equal to +10.23% and working temperature at 312 C for CO (284 ppm, from design) and response equal to −14.17% at 185 C for NO 2 (16 ppm, from design). Analogously, for NO 2 (16 ppm, from design), the material type YCo 0 . 9 O 2 . 85 + 1 % Pd (Mt8) allows for optimizing the response value at 15 . 39 % with a working temperature at 181 . 0 C, whereas for YCo 0 . 95 Pd 0 . 05 O 3 (Mt3), the best response value is achieved at 15 . 40 % with the temperature equal to 204 C. View Full-Text
Keywords: gas sensing; carbon monoxide; electronic nose; nitrogen dioxide; split-plot design; robust process optimization gas sensing; carbon monoxide; electronic nose; nitrogen dioxide; split-plot design; robust process optimization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bertocci, F.; Fort, A.; Vignoli, V.; Mugnaini, M.; Berni, R. Optimization of Perovskite Gas Sensor Performance: Characterization, Measurement and Experimental Design. Sensors 2017, 17, 1352.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top