CYANanobot: Miniaturized Boat-Assisted Data Acquisition for Automated Cyanide Monitoring in Wastewater Using Optical Nano-Sensors
Received: 11 May 2022 | Revised: 4 June 2022 | Accepted: 6 June 2022 | Online: 7 August 2022
Corresponding author: J. S. Loquero
Abstract
Cyanide contamination in water and wastewater is ubiquitous, particularly in gold mining industries, where cyanide is commonly used to extract gold. It is constantly being monitored by collecting samples which are analyzed in the laboratory using traditional cyanide analysis, which requires complicated instrumentation, skilled analysts, and expensive equipment. Using the gold nanoparticle (AuNP)-decorated paper-based sensor employing Whatman Filter Paper (WFP) as a substrate, an automated process for cyanide monitoring with the aid of an assembled and improvised remotely controlled miniature boat was developed. The technology is equipped with a filtration system with automated water sample collection and preparation with an automatic paper sensor dispenser. Images of the collected wastewater samples are taken at different time intervals and are analyzed on their respective color spaces based on 8 mathematical models, each predicting the cyanide level of the water sample. The predictions are compared to the actual Ion-Selective Electrode (ISE) measurement, and Root Mean Square Error (RMSE) values were calculated. The predictions at 165s using the Hue, Saturation, Value (HSV) color space exhibited the highest R2 of 0.85 and the lowest RMSE of 3.80 parts per million (ppm) with an average error of 3.40ppm. The predictions are sent to a database using Global System for Mobile Communications (GSM). The results suggest that the CYANanobot technology facilitates fast analysis time, circumvents the frequent instrument calibration, reduces operating costs, minimizes exposure to toxic cyanide-containing samples, and reduces person-to-person interaction.
Keywords:
Cyanide, Gold nanoparticle (AuNPs)-decorated paper-based sensor, Remote-controlled boat, Automated Cyanide Monitoring, Image Processing, GSMDownloads
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Copyright (c) 2022 J. S. Loquero, A. T. Demetillo, I. B. Pongcol, J. M. Sakuddin, R. N. Mendoza, D. J. C. Estose, R. J. U. Candare, Y. P. C. Amarga, R. Y. Capangpangan
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