No description available.

No description available.

Gas sensors are devices that can convert the concentration of an analyte gas into an electronic signal. Zinc oxide (ZnO) is an important n-type metal oxide semiconductor which has been utilized as sensor for several decades. In recent years, there have been extensive investigations of nanoscale semiconductor gas sensors. The size reduction of ZnO sensors to nanometer scale provides a good opportunity to dramatically increase their sensing properties in comparison with their macroscale counterparts.<p> In this work, two kinds of ZnO nanostructures (nanowires and nanorods) were studied. ZnO nanowires were synthesized by electrodeposition while porous anodic aluminum oxide served as a growth template. Three types of ZnO nanowires with different diameters were obtained. Meanwhile, ZnO nanorods were prepared by a hydrothermal route from ZnO nanoparticle seeds. However, the aspect ratio (length/width) of nanorods was significantly smaller than that of nanowires. Both nanowires and nanorods were characterized by optical microscopy, scanning electron microscopy, powder X-ray diffraction, energy dispersive X-ray spectroscopy and energy dispersive spectroscopy.<p> The sensing performance of the synthetic ZnO nanostructures were investigated by three gases: saturated water vapour in air, saturated ethanol vapour in air, and carbon monoxide in air. Both ZnO nanostructures showed good sensitivity and selectivity to ethanol vapour. At high temperature, the ZnO nanosensors were up to seven times more responsive to ethanol vapour than water vapour and over 200 times more responsive to ethanol vapour than CO. Due to the size dependence, ZnO nanowires with the smallest diameter is considered the best sensor candidate among ZnO nanowires.<p> On the basis of previous work, Au/ZnO/Au multimetallic nanobarcodes were also synthesized by electrodeposition, and their sensing characteristics are to be investigated in the future.

This report presents machine learning models that can accurately classify gases by analyzing data from an array of 16 sensors. More specifically, the report presents basic decision tree models and advanced ensemble versions. The contribution of this report is to show that basic decision trees perform reasonably well on the gas sensor data, however their accuracy can be drastically improved by employing ensemble decision tree classifiers. The report presents bagged trees, Adaboost trees and Random Forest models in addition to basic entropy and Gini based trees. It is shown that ensemble classifiers achieve a very high degree of accuracy of 99% in classifying gases even when the sensor data is drift ridden. Finally, the report compares the accuracy of all the models developed. / text

No description available.

No description available.

TiO2 is a fascinating material proving its usefulness in a wide range of applications. In photocatalytic and gas sensitization applications, it has been utilized as a stable semiconductor for the organic matter of waste water cleaning and impetus on environmental consciousness the need for highly efficient combustion engines, controlled industrial processes and monitoring the hazardous gas level in the environment. Nanocrystalline TiO2 modified with Nb incorporation has been produced by LPD technique that deposited at 40 oC with (NH4)2TiF6 for 0.2 M, and 0.6 M for boric acid and a few mount of Nb solution. The deposition rate can be controlled perfectly about 40-50 nm/h. The addition of Nb stabilizes the anatase phase and retards the grain growth during high temperature annealing. XRD and RAMAN spectra of undoped and Nb doped samples show that the undoped one has converted to rutile at 900 oC, meanwhile the doped one remains anatase phase. Nanocrystalline films stabilize at 800 oC with grain size of about 7 nm have successfully been synthesized by the additives of Nb, which appear to be an adequate dopant to improve the photocatalytic and gas sensor performances. The ability of niobium introduction is of retarding the anatase to rutile transformation and hindering its grain growth.

Electrostatic Assisted Vapor Deposition method was adopted to deposit SnO2 thin films in this work using either SnCl4 or DBTDC (C12H24O4Sn) as precursors. Appropriate deposition parameters were identified for deposition of porous and dense films . A post-deposition calcination of 600¢XC/2h yielded well crystalline rutile phase. Electrical resistance measurement indicated that the most porous films ,derived from the precursor solution of ethanol solvent, were not continuous. Instead, films derived from precursor solution of mixed ethanol-carbitol solvent were less porous allowing stable resistance values to be measured. A detection sensitivity of 2.55 for 100ppm CO gas was obtained from films derived from a 30% ethanol-70% carbitol solution. A higher sensitivity of 6.55 was obtained from films derived from solutions containing Di-n-butyltin diacetate (DBTDC) as precursor.

This study is dedicated to the implementing of Electron-Beam-Induced Current (EBIC) microscopy to study the behavior of metal oxide semiconducting (MOS) nanowire (NW) gas sensor in situ under exposure to different environment. First, we reported the development of a single nanowire gas sensor compatible with an environmental cell. The major component of the device we use in this study is a single SnO2 nanowire attached to an electron transparent SiN membrane (50-100 nm thick), which was used for mounting nanowire working electrodes and surface imaging of NW. First the NW's conductivity is investigated in different temperatures. Higher temperature is proved to cause higher conductivity of NW. We also found that often the Schottky barrier is formed at the nanowire's contacts with Au and Au/Cr electrodes. Then NW's responses to gas and electron beam (from SEM) are analyzed quantitatively by current measurement. Electron-Beam-Induced Current technique was introduced for the first time to characterize the conductivity behavior of the nanowire during the gas sensing process. Resistive contrast was observed in the EBIC image.

A preliminary gas detection study was conducted on as-grown multi-walled carbon nanotubes and anodized aluminum oxide (MWNTs/AAO) template. The material demonstrated room temperature gas sensitivity and p-type semiconductor characteristics. Plasma-etched MWNTs/AAO templates were employed to construct capacitive gas sensors. The capacitances of the sensors were sensitive to both reducing and oxidizing gases at room temperature. Single-walled carbon nanotubes (SWNTs) dispersed in binder andamp;aacute;-terpineol were applied on sensor platforms to form resistive gas sensors. The sensors demonstrated excellent sensitivity to low concentrations of reducing and oxidizing gases at room temperature, which suggests the p-type semiconducting behavior of SWNTs. The sensor recovery was found to be incomplete at room temperature in flow of nitrogen and air, thus possible solutions were investigated to enhance sensor performance. The sensor operating principles and suggestions for possible future work are discussed. The room temperature and air background functionality of the sensor suggest that SWNT is a promising gas sensing material for application in ambient conditions.