Smartphone-based Electrochemical Sensing for On-site Water Analysis

Thesis Abstract

Clean and safe water is fundamental to public health, yet traditional on-site water analysis relies on laboratory infrastructure, delaying decision-making and increasing risk of contamination exposure. This study addresses the gap by developing and validating a smartphone-based electrochemical sensing platform for rapid, in-field analysis of common water contaminants, including lead (Pb2+), cadmium (Cd2+), nitrate (NO3?), and chlorine residual, with the goal of enabling real-time decision support for water quality management. The aim is to create a low-cost, user-friendly, and accurate portable system that integrates electrochemical sensors with smartphone data processing, cloud connectivity, and decision-support algorithms. Specific objectives are (1) to design and fabricate screen-printed electrochemical sensors functionalized for Pb2+, Cd2+, NO3?, and ClO? detection; (2) to develop a smartphone application capable of data acquisition, calibration, and transmission to a cloud-based analytics platform; (3) to establish robust analytical performance, including limits of detection, linear dynamic range, selectivity, and ruggedness under field conditions; (4) to implement calibration transfer and error correction methods to maintain accuracy across diverse environmental matrices; (5) to evaluate usability, reliability, and scalability through field deployment in municipal water networks; and (6) to model decision thresholds using regression analysis and Bayesian updating to support real-time risk assessment. The methodology adopts a mixed-methods design. In the quantitative strand, a cross-sectional field study will sample 200 groundwater and surface water sites across three climatic zones, with duplicate measurements per site. Electrochemical measurements will be conducted using a modular handheld potentiostat paired with a modified smartphone camera-based readout and electrochemical impedance spectroscopy for sensor validation. Calibration will be performed using standard additions and matrix-matched standards, with data analyzed via multivariate regression, partial least squares (PLS) for spectral-electrochemical fusion, and machine learning calibration transfer to maintain accuracy across devices. The qualitative component will involve a usability evaluation with 30 water operators using a structured task-based protocol and interviews, analyzed through thematic analysis to identify barriers to adoption and user experience factors. Data will be triangulated to assess the platform’s performance in real-world contexts. Validity and reliability will be enhanced through instrument validation against inductively coupled plasma mass spectrometry (ICP-MS) and ion chromatography (IC) for select samples, as well as inter-device repeatability tests across five smartphone models. The analytical framework incorporates the Theory of Planned Behavior to interpret user adoption and the Technology Acceptance Model (TAM) to examine perceived usefulness and ease of use, complemented by Innovation Diffusion Theory to explain diffusion dynamics. Expected findings indicate that the sensor system achieves detection limits in the low microgram per liter range for Pb2+ and Cd2+, sub-marts for nitrate with a linear range spanning 0.1–100 mg/L, and chlorine residual accuracy within ±0.2 mg/L across matrixes. Regression analyses are anticipated to reveal strong concordance with ICP-MS (R2 > 0.95) after calibration transfer, with field recovery rates between 88% and 112% for all analytes. The usability study is expected to reveal high perceived usefulness (mean >4.0/5) and favorable behavioral intention scores, tempered by concerns about device durability in extreme conditions. The study contributes to knowledge by demonstrating a practical framework for mobile electrochemical sensing integrated with ICT for real-time water quality surveillance, providing a validated protocol for sensor calibration transfer, data fusion, and cloud-based analytics. It also offers a decision-support model linking measured contaminant concentrations to risk-based action thresholds under varying hydro-Meteorological conditions. The main conclusion anticipates that smartphone-based electrochemical sensing can deliver timely, accurate, and actionable water quality information at scale, enabling proactive management and rapid response to contamination events. Recommendations include standardizing field protocols, expanding analyte panels, integrating autonomous maintenance alerts, and pursuing regulatory validation to inform policy adoption.