Date of Completion

10-29-2020

Embargo Period

10-29-2021

Keywords

ZnO Nanowires, selective sensing, multiple mode sensing, gas mixture, machine learning

Major Advisor

Pu-Xian Gao

Associate Advisor

Yuanyuan Zhu

Associate Advisor

Luyi Sun

Associate Advisor

Yu Lei

Associate Advisor

Serge Nakhmanson

Field of Study

Materials Science and Engineering

Degree

Doctor of Philosophy

Open Access

Open Access

Abstract

Zinc oxide (ZnO) based nanostructures represent an important class of gas sensor materials, due to their high surface-to-volume ratio, significant surface band structure bending upon gaseous analyte exposures, good mobility of charge carriers, and good structural stability at elevated temperature. Their usually surface-dominant sensing processes entail the important roles played by nanostructure size, defects, morphologies, and surface absorbate energetics and dynamics. In this dissertation, based on ZnO nanowire array as a gas sensing platform, rational decoration of electronic sensitizers, such as Au nanoparticles, and semiconducting oxide, such as Fe2O3 nanoparticles, has been employed to boost its electrical sensing performance. Using NO2 as the probe gaseous analyte, both catalytic spill-over and hetero-interface charge transfer regulation have been found effective strategies for significantly improving the sensor performance. The structure-function correlations of these nanowire array sensors have been established for understanding the surface catalysis and associated surface structural evolution in the recoverable sensing dynamics. Specifically, formation of hetero-interfaces of Au-ZnO and Au-Fe2O3-ZnO interfaces endows the pristine ZnO with enhanced sensitivity and improved detection limit. The intermediate nitrate formations were identified in the Au-ZnO hetero-interface region during the sensor exposure to the NO2 probe molecules. The surface local crater formation was revealed on ZnO nanowire surfaces due to the nitrate formation associated with the Au nanoparticles. Such an observation is related to both homogeneous gas phase etching and Au-catalyzed heterogeneous nitrate formation that involve catalytic spillover, strong metal-support interaction, and transient nitrate formation that leads to interfacial material loss or migration under NO2 atmosphere. To improve the thermal instability of Au nanoparticle and allow a higher operation temperature range, the Fe2O3 was introduced as the support for Au nanoparticles. Such an Au-Fe2O3 hybrid nanoparticle decoration has significantly increased the NO2 gas sensor response by 42 times as compared to the Au decorated ZnO nanowires, as high as 74500. Meanwhile, the operating temperature of nanowire sensors was successfully extended to 600 oC. Finally, to address the crosstalk among various gaseous analytes in the nanosensor, a single bimodular sensor has been designed and demonstrated for smart differentation of multiple oxidative analytes by relating the resistance-metric mode to impedance-metric mode. The differentiative and correlated nature between these response signals allows such a single sensor platform to differentiate these oxidative gases accurately and robustly. Thus, the characteristic signature for the target analyte is successfully extracted by incorporating the resistive response and frequency-dependent dielectric response via Electrochemical Impedance Spectroscopy (EIS). The differentiative and correlated nature between these response signals allows such a single sensor platform to differentiate these oxidative gases accurately and robustly. Linear and non-linear decision boundaries were subsequently established over a large gas-concentration range from 2 ppm to 3 % through a combination of principal component analysis and artificial neural network training. In addition to single analyte analysis, the on-board interrogation is achieved towards gas mixture (e.g., NO and NO2) through correlation between the electrical and electrochemical responses. Facilitated by a graphical method, the detailed concentration of component gas could be quantified using the measured responses of NOx mixture in the bimodular sensing on such a single nano-array sensor platform.

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