No abstract

Traditional methods for detection of lead ions in water samples are costly and time-consuming. In this work, an accurate smartphone-based colorimetric sensor was developed utilizing a novel machine learning algorithm. In the presence of Pb 2+ ions in the solution of specifically functionalized gold nanoparticles, the color of solution turns from red to purple. Indeed, the color variation of the solution is proportional to Pb 2+ concentration. The smartphone camera captures the corresponding color change, and the image is processed by an efficient artificial intelligence protocol. The nonlinear regression approach was used for concentration estimation, in which the parameters of the proposed model are obtained using a new feature extraction algorithm. In prediction of Pb 2+ concentration, the average absolute error and root-mean-square error were 0.094 and 0.124, respectively. The influence of pH of the medium, temperature, oligonucleotide concentration, and reaction time on the performance of the proposed sensor was carefully investigated and understood to achieve the best sensor response. This novel sensor exhibited good linearity for the detection of Pb 2+ in the concentration range of 0.5–2000 ppb with a detection limit of 0.5 ppb.

Mercury is one of the most toxic heavy metals in the environment that can seriously damage the human health. Therefore, the identification of mercury in water sources such as rivers, lakes, and bays is very crucial. Many traditional methods are used for the detection of mercury (II) ions (Hg 2þ ), but they suffer from dependence on expensive and complicated instruments and need time consuming operating process. Herein, a fast, low cost, and accurate lab-on-a-phone device has been introduced for on-site monitoring of Hg 2þ in ppb level. It detects Hg 2þ based on localized surface plasmon resonance property of gold nanoparticles. The apparatus consists of lightweight opto-mechanical attachment, wirelessly connected to a smart phone. This method presents a sensitive detection of Hg 2þ in water with a detection limit of 3 nM (%0.8 ppb). Detection limit of the proposed sensor is well below the maximum allowed containment level of Hg 2þ for drinking water (6 ppb) by the World Health Organization.

Arsenic is perhaps the most harmful components and may be found in drinking water. In this work, a convenient and lowcost gadget is designed and connected to smartphone to predict arsenic concentration. The smartphone captures the images of color change and analyzes data in RGB color space. The color change is the result of functionalized gold nanoparticle aggregations in the presence of arsenic ions. By implementing a multi-variable regression model, the captured data is converted into the arsenic concentration values. Under optimum analytical conditions, the proposed algorithm provides better detection limit and linearity as compared to other studies. The nanoprobe has high sensitivity to arsenic, with detection limit of 0.45 ppb at the linear range of 1-7500 ppb and a mean squared error of 0.24. The quantitative results agreed with those obtained by the reference ICP-MS method at a 94 % confidence level and can be used for on-site detection of low-content arsenic ions.