Special Issue "Gas Sensors - 2013"
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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".
Deadline for manuscript submissions: 30 November 2013
Special Issue Editor
Special Issue Information
Dear Colleagues,
Gas sensors continue to be a dynamic and fast-emerging field of research for both scientists and developers. Modern advances in the synthesis of functional materials and in the fabrication of smart devices open up new opportunities to create novel sensors with enhanced properties. Fundamental research, at the same time, allows us to understand the underlying principles and sensing mechanisms in greater detail. This special issue on ‘gas sensors' aims to cover all various aspects, such as (but not limited to) the preparation of functional materials and micro-fabricated systems for gas sensing, new insights in gas-sensing mechanisms, and the large number of different types of gas-sensing principles (resistive, mechanical, capacitive, etc.).
Prof. Dr. Michael Tiemann
Guest Editor
Submission
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed Open Access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs).
Keywords
- functional sensor materials
- sensing mechanisms
- metal oxide gas sensors
- thin films
- porous materials / nanostructures
- resistive gas sensors
- electrochemical gas sensors
- optical gas sensors
- thermometric gas sensors / pellistors
- acoustic wave / crystal microbalance gas sensors
- cantilever gas sensors
- capacitive humidity sensors
- field-effect gas sensors
- microfabrication / MEMS
Published Papers (10 papers)
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Received: 2 August 2012; in revised form: 28 August 2012 / Accepted: 31 August 2012 / Published: 10 September 2012
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Abstract: A novel and promising method to measure low levels of NOx utilizes the accumulating sensor principle. During an integration cycle, incoming NOx molecules are stored in a sensitive layer based on an automotive lean NOx trap (LNT) material that changes its electrical resistivity proportional to the amount of stored NOx, making the sensor suitable for long-term detection of low levels of NOx. In this study, the influence of the thickness of the sensitive layer, prepared by multiple screen-printing, is investigated. All samples show good accumulating sensing properties for both NO and NO2. In accordance to a simplified model, the base resistance of the sensitive layer and the sensitivity to NOx decrease with increasing thickness. Contrarily, the sensor response time increases. The linear measurement range of all samples ends at a sensor response of about 30% resulting in an increase of the linearly detectable amount with the thickness. Hence, the variation of the thickness of the sensitive layer is a powerful tool to adapt the linear measurement range (proportional to the thickness) as well as the sensitivity (proportional to the inverse thickness) to the application requirements. Calculations combining the sensor model with the measurement results indicate that for operation in the linear range, about 3% of the LNT material is converted to nitrate.
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Received: 2 August 2012; in revised form: 2 October 2012 / Accepted: 5 October 2012 / Published: 11 October 2012
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Abstract: Metal Oxide Semiconductor (MOX) gas transducers are one of the preferable technologies to build electronic noses because of their high sensitivity and low price. In this paper we present an approach to overcome to a certain extent one of their major disadvantages: their slow recovery time (tens of seconds), which limits their suitability to applications where the sensor is exposed to rapid changes of the gas concentration. Our proposal consists of exploiting a double first-order model of the MOX-based sensor from which a steady-state output is anticipated in real time given measurements of the transient state signal. This approach assumes that the nature of the volatile is known and requires a precalibration of the system time constants for each substance, an issue that is also described in the paper. The applicability of the proposed approach is validated with several experiments in real, uncontrolled scenarios with a mobile robot bearing an e-nose.
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Received: 7 November 2012; in revised form: 5 January 2013 / Accepted: 9 January 2013 / Published: 11 January 2013
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Abstract: In this study, electron beam lithography, rather than the most popular method, chemical synthesis, is used to construct periodical TiO2 nanowires for a gas sensor with both robust and rapid performance. The effects of temperature on the sensing response and reaction time are analyzed at various operation temperatures ranging from 200 to 350 °C. At the optimized temperature of 300 °C, the proposed sensor repeatedly obtained a rise/recovery time (ΔR: 0.9 R0 to 0.1 R0) of 3.2/17.5 s and a corresponding sensor response (ΔR/R0) of 21.7% at an ethanol injection mass quantity of 0.2 μg.
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Received: 7 January 2013; in revised form: 31 January 2013 / Accepted: 21 February 2013 / Published: 1 March 2013
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Abstract: This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.
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Received: 24 January 2013; in revised form: 25 February 2013 / Accepted: 28 February 2013 / Published: 8 March 2013
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Abstract: Various types of CO sensors based on cerium oxide (ceria) have been reported recently. It has also been reported that the response speed of CO sensors fabricated from porous ceria thick films comprising nanoparticles is extremely high. However, the response value of such sensors is not suitably high. In this study, we investigated methods of improving the response values of CO sensors based on ceria and prepared gas sensors from core-shell ceria polymer hybrid nanoparticles. These hybrid nanoparticles have been reported to have a unique structure: The core consists of a cluster of ceria crystallites several nanometers in size. We compared the characteristics of the sensors based on thick films prepared from core-shell nanoparticles with those of sensors based on thick films prepared from conventionally used precipitated nanoparticles. The sensors prepared from the core-shell nanoparticles exhibited a resistance that was ten times greater than that of the sensors prepared from the precipitated nanoparticles. The response values of the gas sensors based on the core-shell nanoparticles also was higher than that of the sensors based on the precipitated nanoparticles. Finally, improvements in sensor response were also noticed after the addition of Au nanoparticles to the thick films used to fabricate the two types of sensors.
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Received: 19 December 2012; in revised form: 19 February 2013 / Accepted: 13 March 2013 / Published: 15 March 2013
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Abstract: The study presents an ammonia microsensor integrated with a readout circuit on-a-chip fabricated using the commercial 0.18 μm complementary metal oxide semiconductor (CMOS) process. The integrated sensor chip consists of a heater, an ammonia sensor and a readout circuit. The ammonia sensor is constructed by a sensitive film and the interdigitated electrodes. The sensitive film is zirconium dioxide that is coated on the interdigitated electrodes. The heater is used to provide a working temperature to the sensitive film. A post-process is employed to remove the sacrificial layer and to coat zirconium dioxide on the sensor. When the sensitive film adsorbs or desorbs ammonia gas, the sensor produces a change in resistance. The readout circuit converts the resistance variation of the sensor into the output voltage. The experiments show that the integrated ammonia sensor has a sensitivity of 4.1 mV/ppm.
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Received: 29 January 2013; in revised form: 4 March 2013 / Accepted: 15 March 2013 / Published: 19 March 2013
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Abstract: The effects of the SnO2 pore size and metal oxide promoters on the sensing properties of SnO2-based thick film gas sensors were investigated to improve the detection of very low H2S concentrations (<1 ppm). SnO2 sensors and SnO2-based thick-film gas sensors promoted with NiO, ZnO, MoO3, CuO or Fe2O3 were prepared, and their sensing properties were examined in a flow system. The SnO2 materials were prepared by calcining SnO2 at 600, 800, 1,000 and 1,200 °C to give materials identified as SnO2(600), SnO2(800), SnO2(1000), and SnO2(1200), respectively. The Sn(12)Mo5Ni3 sensor, which was prepared by physically mixing 5 wt% MoO3 (Mo5), 3 wt% NiO (Ni3) and SnO2(1200) with a large pore size of 312 nm, exhibited a high sensor response of approximately 75% for the detection of 1 ppm H2S at 350 °C with excellent recovery properties. Unlike the SnO2 sensors, its response was maintained during multiple cycles without deactivation. This was attributed to the promoter effect of MoO3. In particular, the Sn(12)Mo5Ni3 sensor developed in this study showed twice the response of the Sn(6)Mo5Ni3 sensor, which was prepared by SnO2(600) with the smaller pore size than SnO2(1200). The excellent sensor response and recovery properties of Sn(12)Mo5Ni3 are believed to be due to the combined promoter effects of MoO3 and NiO and the diffusion effect of H2S as a result of the large pore size of SnO2.
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Received: 27 February 2013; in revised form: 25 March 2013 / Accepted: 26 March 2013 / Published: 2 April 2013
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Abstract: Many methods based on spectrophotometric, fluorometric, piezoresistive, amperometric or conductive measurements have been proposed for detecting the concentration of formaldehyde in air. However, conventional formaldehyde measurement systems are bulky and expensive and require the services of highly-trained operators. Accordingly, the emergence of sophisticated technologies in recent years has prompted the development of many microscale gaseous formaldehyde detection systems. Besides their compact size, such devices have many other advantages over their macroscale counterparts, including a real-time response, a more straightforward operation, lower power consumption, and the potential for low-cost batch production. This paper commences by providing a high level overview of the formaldehyde gas sensing field and then describes some of the more significant real-time sensors presented in the literature over the past 10 years or so.
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Received: 19 March 2013; in revised form: 1 April 2013 / Accepted: 8 April 2013 / Published: 10 April 2013
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Abstract: Half-cell potentials of the electrochemical cell Au, VWT | YSZ | Au are analyzed in dependence on oxygen and ammonia concentration at 550 °C. One of the gold electrodes is covered with a porous SCR catalyst, vanadia-tungstenia-titania (VWT). The cell is utilized as a potentiometric ammonia gas sensor and provides a semi-logarithmic characteristic curve with a high NH3 sensitivity and selectivity. The analyses of the Au | YSZ and Au, VWT | YSZ half-cells are conducted to describe the non-equilibrium behavior of the sensor device in light of mixed potential theory. Both electrode potentials provide a dependency on the NH3 concentration, whereby VWT, Au | YSZ shows a stronger effect which increases with increasing VWT coverage. The potential shifts in the anodic direction confirm the formation of mixed potentials at both electrodes resulting from electrochemical reactions of O2 and NH3 at the three-phase boundary. Polarization curves indicate Butler-Volmer-type kinetics. Modified polarization curves of the VWT covered electrode show an enhanced anodic reaction and an almost unaltered cathodic reaction. The NH3 dependency is dominated by the VWT coverage and it turns out that the catalytic properties of the VWT thick film are responsible for the electrode potential shift
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Received: 31 January 2013; in revised form: 18 April 2013 / Accepted: 19 April 2013 / Published: 25 April 2013
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Abstract: This article explains the development of a prototype of a portable and a very low-cost electronic nose based on an mbed microcontroller. Mbeds are a series of ARM microcontroller development boards designed for fast, flexible and rapid prototyping. The electronic nose is comprised of an mbed, an LCD display, two small pumps, two electro-valves and a sensor chamber with four TGS Figaro gas sensors. The performance of the electronic nose has been tested by measuring the ethanol content of wine synthetic matrices and special attention has been paid to the reproducibility and repeatability of the measurements taken on different days. Results show that the electronic nose with a neural network classifier is able to discriminate wine samples with 10, 12 and 14% V/V alcohol content with a classification error of less than 1%.
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Last update: 10 May 2013
