Sensors for chemical contaminants in water

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Barry, Fiona
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The Boolean, University College Cork
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Long-term exposure to chemical contaminants in our drinking water can have adverse effects on human health. Such exposure has been linked to cancers, neurological diseases, and cardiovascular diseases. Examples of chemical contaminants found in our water systems include nitrates, heavy metals such as lead, and disinfectant by-products. These contaminants are not visible to the human eye; therefore, it is essential to test the water sample to determine their presence. This work presents an electrochemical sensing platform to detect chemical contaminants in water. This sensing platform has advantages over traditional lab-based methods as it eliminates the need to transport samples to a lab by giving at-source, real-time analysis. The sensing component used consisted of two combs of electrodes that were sandwiched together with ultra-small gaps between the opposing combs. This configuration notably enabled the reduction of the sensor footprint by ∼6,000 times compared to previous designs. The significance of this reduction in size means these sensors are lower in cost to produce and require less power to operate. Another benefit of interdigitated electrodes investigated was the ability to use them for in-situ pH control. In situ pH control was successfully demonstrated using lead. Lead requires acidic conditions for analysis and therefore pH adjusting buffers are typically required for the analysis of lead in tap water which is normally a neutral pH between pH 6 to 8. The ability to electrochemically control the pH of the water sample allowed detection of lead as low as 10 ppb without the need for any additional buffers.
Drinking water , Chemical contaminants , Electrochemical sensor , Electrodes , Interdigitated electrode array , Generator-collector , Electrochemical pH control
Barry, F. (2022) 'Sensors for chemical contaminants in water', The Boolean: Snapshots of Doctoral Research at University College Cork, 6, pp. 40-45. doi: 10.33178/boolean.2022.1.7