Investigation on Dielectrophoretic Assembly of Nanostructures and Its Application on Chemical Sensors

Because of their extraordinary characteristics such as quantum confinement and large surface-to-volume ratio, semiconducting nanostructures such as nanowires or nanotubes hold great potential in sensing chemical vapors. Nanowire or nanotube based gas sensors usually possess appealing advantages such as high sensitivity, high stability, fast recovery time, and electrically controllable properties. To better predict the composition and concentration of target gas, nanostructures made from heterogeneous materials are employed to provide more predictors. In recent years, nanowires and nanotubes can be synthesized routinely through different methods. The techniques of fabricating nanowire or nanotube based sensor arrays, however, encounter obstacles and deserve further investigations. Dielectrophoresis (DEP), which refers to the motion of submicron particles inside a non-uniform electric field, has long been recognized as a non-destructive, easily implementable, and efficient approach to manipulate nanostructures onto electronic circuitries. However, due to our limited understandings, devices fabricated through DEP often end up with unpredictable number of arbitrarily aligned nanostructures. \ud In this study, we first optimize the classical DEP formulas such that it can be applied to a more general case that a nanostructure is subjected to a non-uniform electric field with arbitrary orientation. A comprehensive model is then constructed to investigate the trajectory and alignment of DEP assembled nanostructures, which can be verified by experimental observations. The simulation results assist us to fabricate a gas sensor array with zinc oxide (ZnO) nanowires and carbon nanotubes (CNTs). It is then demonstrated that the device can well sense ammonia (NH3) at room temperature, which circumvents the usually required high temperature condition for nanowire based gas sensor application. An effective approach to recover the device using DC biases to locally heat up the nanostructures is then proposed and implemented to accelerate the recovery process of the device without the requirement of heating up the whole device. As the sensors are characterized under different NH3 concentrations, the outputs are analyzed using regression methods to estimate the concentration of NH3. The quadratic model with the lasso is demonstrated to provide best performance for the collected data