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High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine
Samira Sajed1, Mohammadreza Kolahdouz1, Mohammad Amin Sadeghi1
1School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
ACS Omega
|November 2, 2020
Summary
A new smartphone sensor accurately detects lead ions (Pb2+) in water using a machine learning algorithm and color changes in gold nanoparticles. This cost-effective method offers rapid and precise lead detection for environmental monitoring.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Nanotechnology
Background:
- Traditional lead ion detection methods are expensive and slow.
- There is a need for rapid, accurate, and cost-effective water quality monitoring tools.
Purpose of the Study:
- To develop a smartphone-based colorimetric sensor for detecting lead ions (Pb2+) in water.
- To utilize a novel machine learning algorithm for accurate lead concentration estimation.
Main Methods:
- Functionalized gold nanoparticles were used, exhibiting a color change (red to purple) proportional to Pb2+ concentration.
- A smartphone camera captured color changes, processed by an artificial intelligence protocol.
- Nonlinear regression and a new feature extraction algorithm were employed for concentration estimation.
Main Results:
- The sensor demonstrated good linearity for Pb2+ detection in the range of 0.5-2000 ppb.
- A low detection limit of 0.5 ppb was achieved.
- Average absolute error and root-mean-square error were 0.094 and 0.124, respectively.
Conclusions:
- The developed smartphone-based sensor provides an accurate, rapid, and cost-effective solution for lead ion detection in water.
- Optimization of pH, temperature, oligonucleotide concentration, and reaction time enhanced sensor performance.
- This novel approach offers a promising tool for environmental monitoring and water safety.
