http://hdl.handle.net/11375/27630

The monitoring of pharmaceuticals, heavy metal, pH and free chlorine concentration in drinking water is important for public health and the environment. However, conventional laboratory-based analytical methods are labor-intensive, expensive, and time consuming. This thesis focuses on developing an integrated, highly sensitive, easy-to-use, and low-cost pharmaceuticals, heavy metal, pH and free chlorine sensing system for drinking water quality monitoring. A low-temperature, solution-processed modification of multi-walled carbon nanotubes (MWCNT) with β-cyclodextrin (βCD) on glassy carbon electrode is developed for detecting low levels of acetaminophen. The adsorption properties of βCD are combined with the high surface area of carbon nanotubes towards enhanced electrochemical sensing of acetaminophen with a limit of detection of 11 nM and linear range from 0.05-300 μM. Also, a systematic investigation is carried out using four types of modified MWCNT-βCD. A novel, one-step approach called Steglich esterification modified MWCNT-βCD results in large effective surface area, and fast electron transfer towards sensitive detection of acetaminophen and 17β-estradiol (E2, primary female sex hormone) in the range of 0.005–20 and 0.01–15 μM, with low detection limits of 3.3 and 2.5 nM, respectively. The similar MWCNT-βCD modified electrodes can also detect heavy metal ion (lead, Pb2+) with a limit of detection of <10 ppb. Low frequency noise behavior of these sensors are studied. A spin-coated Pd/PdO based pH sensor, and amine-modified carbon electrode-based free chlorine sensor are fabricated on a common substrate together with the pharmaceuticals and heavy metal sensors. A Wheatstone-bridge temperature sensor is fabricated based on silicon and PEDOT:PSS on another substrate. All the sensors are connected to an Arduino microcontroller based data acquisition system with a smartphone application interface. The integrated sensing system is easy-to-use, low-cost, and can provide accurate monitoring data with real drinking water samples.

http://hdl.handle.net/11375/27630