Synthesis and Integration of Functional Nanomaterials on Optical Fiber Platforms for Points and Distributed Sensing for Energy Applications

As relatively clean and abundant energy sources, large-scale utilization of natural gas and hydrogen gas fuel for automobile and electricity productions have been considered by many countries as key energy strategies to reduce greenhouse emission and to improve air quality. Methane, the major constituent of natural gas, is an extremely potent greenhouse gas, while hydrogen is highly flammable. The extraction, production, storage, transportation, and combustion of both gas fuels requires extensive deployment of low-cost sensors with sufficient sensitivity to monitor emissions of these gas species throughout energy infrastructure. Fiber optical sensors, compared with conventional electrical sensors, has been considered effective sensing tools for energy applications due to their resilience to harsh environment, inert for reactive/flammable gases, and immune to electromagnetic fields. A unique trait for fiber optics sensors is their capability to perform distributed measurements along the entire length of optical fibers across great distance using a single fiber. However, optical fiber based on silica materials are insensitive to changes of ambient gas compositions. This dissertation describes research efforts to synthesize novel functional nanomaterials including rare-earth doped metal oxide nanocomposite, metal organic frameworks doped polymer, nanoscale metal alloy that is sensitive to hydrogen and methane gas from the room temperature to 750 ℃. These functional nanomaterials were integrated with various optical fiber sensors platforms including both telecom fibers and D-shaped micro-structured fibers. Nanostructure-textured optical fiber was utilized to increase the surface-to-volume ratio of the metal alloy sensory film while dimension and density of the nanostructure was specifically chosen to limit the light scattering. Through on-fiber transaction mechanism such as evanescent interaction and gas-absorption induced on-fiber strain, Rayleigh-based distributed fiber sensors based on optical frequency domain reflectometry and intrinsic Fabry–Perot interferometer were used to measure fuel gas concentration from 0.25% to 40% from room temperature to 750 oC with excellent repeatability. Research works documented in this dissertation shows that highly controllable VLSI microfabrication schemes and novel functional nanomaterials can be used to enhance functionality of optical fibers to produce high performance fiber optical chemical sensors for both distributed and point measurements for both fossil energy and renewable energy applications

A New Strategy to Minimize Humidity Influences for Acoustic Wave Ultraviolet Sensor Using ZnO Nanowires Wrapped with Hydrophobic Silica Nanoparticles

Surface acoustic wave (SAW) technology has been widely developed for ultraviolet (UV) detection due to its advantages of miniaturization, portability, potential to be integrated with microelectronics, and passive/wireless capabilities. To enhance UV sensitivities, nanowires (NWs) such as ZnO are often applied to enhance SAW based UV detection, due to their highly porous and interconnected 3D network structures and good UV sensitivity. However, ZnO NWs are normally hydrophilic, and thus changes of environmental parameters such as humidity will significantly influence the detection precision and sensitivity of the SAW based UV sensors. To solve this issue, in this work, we proposed a new strategy using ZnO NWs wrapped with hydrophobic silica nanoparticles as the effective sensing layer. Analysis of distribution and chemical bonds of these hydrophobic silica nanoparticles showed that numerous C-F bonds (which are hydrophobic) were found on the surface of sensitive layer, which effectively block the adsorption of water molecules onto the ZnO NWs. This new sensing layer design endows the ZnO NWs based UV sensor with a minimized humidity interference within the relatively humidity range between 10 and 70. The sensor showed a UV sensitivity of 9.53 ppm (mW/cm2)-1, with a high linearity (R2 value is 0.99904), small hysteresis (less than 1.65) and a good repeatability. This work solves the long-term dilemma for ZnO NWs based sensors which are often sensitive to humidity changes

Machine-learning assisted handwriting recognition using graphene oxide-based hydrogel

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of currently reported handwriting recognition systems are lacked in flexible sensing and machine learning capabilities, both of which are essential for implementations of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor i for confidential nformation. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide and good sensitivity, and allows high-- based hydrogel sensors. It offers fast response precision recognitions of handwritten conten ts from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from 7 participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (~91.30). Our developed handwri has great potentials in advanced humanting recognition system machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality