Abstract
This paper reports on a set of experiments designed to develop a workable gas sensor prototype using an electronic system with methane. The current is found to be sensitive to the presence of methane gas, which is a conduit for a variety of gas sensors. The sensitivity is shown to depend on pointed or broad electrode configurations. Scanning electron microscopy images show the area of conductance that determines the quality of the electrodes in three configurations. Data processing is performed with a support vector regression algorithm in conjunction with statistical analysis for error and quality control. The reported results can be adapted to a broad range of industrial applications for enhanced productivity, safety, innovation, data processing, and overall total quality management.
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Akbari ES, Ahmadi M, Yusof R, Ghadiry M, Saeidmanesh M (2013) Gas concentration effect on channel capacitance in graphene based sensors. J Comput Theor Nanosci 10:2449–2452
Anthony M (1997) Computational learning theory. Cambridge University Press, Cambridge
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
Davidson EA, Ishida FY, Nepstad DC (2004) Effects of an experimental drought on soil emissions of carbon dioxide, methane, nitrous oxide, and nitric oxide in a moist tropical forest. Glob Change Biol 10:718–730
Gunn SR (1998) Support vector machines for classification and regression. ISIS technical report 14
Huttunen JT, Alm J, Liikanen A, Juutinen S, Larmola T, Hammar T, Silvola J, Martikainen PJ (2003) Fluxes of methane, carbon dioxide and nitrous oxide in boreal lakes and potential anthropogenic effects on the aquatic greenhouse gas emissions. Chemosphere 52:609–621
Krcma F, Klohnova K, Polachova L, Horvath G (2010) Optical emission spectroscopy of abnormal glow gischarge in nitrogen-methane mixtures at atmospheric pressure. Publ Obs Astron Beograd 89:371–374
Lee EK, Lee SY, Han GY, Lee BK, Lee T-J, Jun JH, Yoon KJ (2004) Catalytic decomposition of methane over carbon blacks for CO2-free hydrogen production. Carbon 42:2641–2648
Moon YK, Lee J, Lee JK, Kim TK, Kim SH (2009) Synthesis of length-controlled aerosol carbon nanotubes and their dispersion stability in aqueous solution. Langmuir 25:1739–1743
Müller K-R, Smola AJ, Rätsch G, Schölkopf B, Kohlmorgen J, Vapnik V (1997) Predicting time series with support vector machines. Artificial Neural Networks—ICANN’97. Springer, New York, pp 999–1004
Patacsil C, Malapit G, Ramos H (2006) Optical emission spectroscopy of low temperature CVD diamond. J Plasma Fusion Res Ser 7:145–149
Patel RF, Bazzanella N, Miotello A (2011) Enhanced hydrogen production by hydrolysis of NaBH4 using “Co-B nanoparticles supported on carbon film” catalyst synthesized by pulsed laser deposition. Elsevier 170:20–26
Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222
Stahlbock R, Lessmann S (2004) Potential von support Vektor Maschinen im analytischen customer relationship management. Universität Hamburg, Hamburg
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999
Welling M (2004) Support vector regression. Department of Computer Science, University of Toronto, Toronto
Zhang J, Jin L, Li Y, Si H, Qiu B, Hu H (2013) Hierarchical porous carbon catalyst for simultaneous preparation of hydrogen and fibrous carbon by catalytic methane decomposition. Intl J Hydro Energy 38:8732–8740
Acknowledgments
The authors would like to thank Ministry of Higher Education (MOHE), Malaysia (Grant Vot. No. 4F382) and the Universiti Teknologi Malaysia (Grants Vot. No. 03H86 and Post-doc Grant No. 02E11) for the financial support received during the investigation.
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Akbari, E., Buntat, Z., Afroozeh, A. et al. Analytical investigations of gas-sensor using methane decomposition system. Environ Earth Sci 75, 420 (2016). https://doi.org/10.1007/s12665-015-4943-0
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DOI: https://doi.org/10.1007/s12665-015-4943-0