In an electrochemical sensor, the sensing performance is mainly dependent on the mass transport of the analyte towards the working electrode-electrolyte interface and working electrode properties. Carbon nanomaterials like carbon nanotubes are widely employed to modify the working electrode properties for sensitive detection. A simulation model is formulated to investigate the effects of modifying a planar bare electrode with carbon nanotubes on electrochemical detection of fenitrothion (FT, an organophosphate). The model revealed that porous electrodes caused the change in mass transport regime and influenced FT’s electrochemical response. The results aided in understanding the influence of the porous electrode on analyte detection and thus assisted in the fabrication of an ultrasensitive electrochemical sensor.
Simulation supported synthesis of a highly sensitive ink to produce highly porous and electrocatalytic electrodes. Activated carbon (AC) possesses high porosity and surface area, but they suffer from lower electrical conductivity. To enhance their conductivity, AC was co-doped with nitrogen and sulfur. Multiwalled carbon nanotubes were incorporated to further improve their porosity and electrocatalytic properties. The synthesized nitrogen-sulfur co-doped activated carbon coated multiwalled carbon nanotube (NS-AC-MWCNT) ink produced highly porous electrocatalytic electrodes. The sensor revealed a 4.9 nM limit of detection (LOD) under optimized conditions. However, it failed to overcome the enzymatic sensors’ performances. The ultrasensitive performance was achieved by incorporating a detecting agent in the ink that instilled analyte capture ability. Metal oxides like ZrO2, MnO2, and MgO possessed affinity towards organophosphate (fenitrothion), heavy-metal ion (lead), and nutrient (nitrite). Metal oxides were modified with 3,4-dihydroxylbenzaldehyde (DHBA) – Chitosan (CHIT) to produce well dispersed and uniformly coated stable electrodes. The ZrO2-DHBA-CHIT/NS-AC-MWCNT sensor achieved a remarkable limit of detection of 1.69 nM for FT. The sensor's performance exceeded the enzymatic-based sensors. The commonly found chemical interferents had negligible interference. The sensor produced reliable and satisfactory performance in lake and tap water. The MnO2-DHBA-CHIT/NS-AC-MWCNT/GCE and MgO-DHBA-CHIT/NS-AC-MWCNT/GCE sensors produced an enormous improvement in the sensor performance compared to unmodified electrodes for lead and nitrite detection. The preliminary results on detecting other pollutants like lead and nitrite showed the importance of the methodology in providing a platform for a new class of metal oxide-based sensors. / Thesis / Doctor of Philosophy (PhD) / The growing population and rapid industrial development are affecting the water quality worldwide. The major water pollutants are organophosphates, heavy metal ions, and nutrients. These water pollutants are harmful, and their bioaccumulation poses a major health concern. In the USA alone, water quality issues are predicted to cost $210 billion annually. Therefore, sensors to detect water pollutants are developed to monitor their environmental footprints. Electrochemical sensors are popularly used to detect water pollutants owing to their low-cost and high sensitivity.
The objective of this dissertation was to fabricate highly sensitive and specific electrochemical sensors to detect organophosphate (e.g., fenitrothion, FT), heavy metal ion (e.g., lead), and nutrient (e.g., nitrite). The sensors were fabricated with ink based on nanomaterials like carbon nanotubes and detecting agents like metal oxides. The fabricated sensors achieved very high sensitivity and specificity and can detect water pollutants in lake and tap water.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27764 |
| Date | January 2022 |
| Creators | Jangid, Krishna |
| Contributors | Puri, Ishwar, Zhitomirsky, Igor, Engineering Physics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0019 seconds