Synthesis methods, microscopy characterization and device integration of nanoscale metal oxide semiconductors for gas sensing

Hollow WO3/SnO2 Hetero-Nanofibers: Controlled Synthesis and High Efficiency of Acetone Vapor Detection

Metal oxide hetero-nanostructures have widely been used as the core part of chemical gas sensors. To improve the dispersion state of each constituent and the poor stability that exists in heterogeneous gas sensing materials, a uniaxial electro-spinning method combined with calcination was applied to synthesize pure SnO2 and three groups of WO3/SnO2 (WO3 of 0.1, 0.3, 0.9 wt%) hetero-nanofibers (HNFs) in our work. A series of characterizations prove that the products present hollow and fibrous structures composed of even nanoparticles while WO3 is uniformly distributed into the SnO2 matrix. Gas sensing tests display that the WO3/SnO2 (0.3 wt%) sensor not only exhibits the highest response (30.28) and excellent selectivity to acetone vapor at the lower detection temperature (170°C), 6 times higher than that of pure SnO2 (5.2), but still achieves a considerable response (4.7) when the acetone concentration is down to 100 ppb with the corresponding response/recovery times of 50/200 s, respectively. Such structure obviously enhances the gas sensing performance toward acetone which guides the construction of a highly sensitive acetone sensor. Meanwhile, the enhancement mechanism of such a special sensor is also discussed in detail.

Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique

The paper presents two methods of dynamic error correction applied to transducers used for the measurement of gas concentration. One of them is based on a parametric model of the transducer dynamics, and the second one uses the artificial neural network (ANN) technique. This article describes research of the dynamic properties of the gas concentration measuring transducer with a typical sensor based on tin dioxide. Its response time is about 8 min, which may be not acceptable in many applications. On the basis of these studies, a parametric model of the transducer dynamics and an adequate correction algorithm has been developed. The results obtained in the research of the transducer were also used for learning and testing ANN, which were implemented in the dynamic correction task. Despite the simplicity of the used models, both methods allowed a significant reduction of the transducer’s response time. For the algorithm based on the parametric model the response time was shorter by approximately eight-fold (reduced up to 40–80 s, i.e., about 2–4 sample periods), whereas with the use of an ANN the output signal was practically fixed after a time equal to one sampling period, i.e., 20 s. In addition, the use of ANN has allowed reducing the impact of the transducer dynamic non-linearity on the correction effectiveness.

Surface morphology-dependent room-temperature LaFeO₃ nanostructure thin films as selective NO₂ gas sensor prepared by radio frequency magnetron sputtering

In the present work, perovskite LaFeO3 thin films with unique morphology were obtained on silicon substrate using radio frequency magnetron sputtering technique. The effect of thickness and temperature on the morphological and structural properties of LaFeO3 films was systematically studied. The X-ray diffraction pattern explored the highly oriented orthorhombic perovskite phase of the prepared thin films along [121]. Electron micrograph images exposed the network and nanocube surface morphology of LaFeO3 thin films with average sizes of ∼90 and 70 nm, respectively. The developed LaFeO3 thin films not only possess unique morphology, but also influence the gas-sensing performance toward NO2. Among the two morphologies, nanocubes exhibited high sensitivity, good selectivity, fast response-recovery time, and excellent repeatability for 1 ppm level of NO2 gas at room temperature. The response time for nanocubes was 24-11 s with a recovery duration of 35-15 s less than the network structure. The sensitivity toward NO2 detection was found to be in the range 29.60-157.89. The enhancement in gas-sensing properties is attributed to their porous structure, surface morphology, numerous surface active sites, and the oxygen vacancies. The gas-sensing measurements demonstrate that the LaFeO3 sensing material is an outstanding candidate for NO2 detection.

A novel methane sensor based on porous SnO2 nanorods: room temperature to high temperature detection

We report for the first time a novel room temperature methane (CH(4)) sensor fabricated using porous tin oxide (SnO(2)) nanorods as the sensing material. The porous SnO(2) nanorods were synthesized by using multiwall carbon nanotubes (MWCNTs) as templates. Current versus time curves were obtained demonstrating the room temperature sensing capabilities of the sensor system when exposed to 0.25% CH(4) in air. The sensor also exhibited a wide temperature range for different concentrations of CH(4) (25-500 °C), making it useful in harsh environments as well.

One-dimensional oxide nanostructures as gas-sensing materials: review and issues

In this article, we review gas sensor application of one-dimensional (1D) metal-oxide nanostructures with major emphases on the types of device structure and issues for realizing practical sensors. One of the most important steps in fabricating 1D-nanostructure devices is manipulation and making electrical contacts of the nanostructures. Gas sensors based on individual 1D nanostructure, which were usually fabricated using electron-beam lithography, have been a platform technology for fundamental research. Recently, gas sensors with practical applicability were proposed, which were fabricated with an array of 1D nanostructures using scalable micro-fabrication tools. In the second part of the paper, some critical issues are pointed out including long-term stability, gas selectivity, and room-temperature operation of 1D-nanostructure-based metal-oxide gas sensors.