An IoT-Enabled Portable Water Quality Monitoring System With MWCNT/PDMS Multifunctional Sensor for Agricultural Applications

The need to develop a low-power, low-cost nitrate, phosphate, and pH sensor and sensing system is essential for monitoring water quality in real time. A novel interdigital sensor has been fabricated and characterized for temperature, nitrate, phosphate, and pH detection in water. The sensor is fabricated using the 3-D printing technique, where the electrodes are formed using multiwalled carbon nanotubes, and the substrate is developed using polydimethylsiloxane. The sensor is characterized by electrochemical impedance spectroscopy to determine various temperatures, pH levels, nitrate, and phosphate concentrations. Experimental outcomes prove that the developed sensor can distinguish nitrate and phosphate concentrations ranging from 0.1 to 30 ppm, pH values from 1.71 to 12.59, temperature from 0 to 45 degrees C. The sensitivity for temperature, nitrate, phosphate, and pH level of the sensor are 1.1974 Omega/degrees C, 1.9396 Omega/ppm, 0.8839 Omega/ppm, and 1.0295 Omega, respectively. A location-independent portable smart sensing system with LoRa connectivity is also developed to surveil water quality and get feedback from the experts. A machine learning algorithm trains the Arduino-based system and determines temperature, nitrate and phosphate concentrations, and pH level in real water samples. All the outcomes are compared with the standard method for validation. The sensor and the sensing system's performances are highly stable, reliable, and repeatable to be a part of a smart sensing network for continuous water quality monitoring.

Automated summary

A low-power, low-cost nitrate, phosphate, and pH sensor and sensing system have been developed for real-time water quality monitoring. The sensor is fabricated using 3D printing with carbon nanotube electrodes and polydimethylsiloxane substrate. Experimental results show that the sensor has high sensitivity to temperature, nitrate, phosphate, and pH levels, and the sensing system can accurately monitor and analyze water quality using a machine learning algorithm.