Electrochemical Impedance Sensors for Monitoring Trace Amounts of NO3 in Selected Growing Media

Real-Time Nitrate Ion Monitoring with Poly(3,4-ethylenedioxythiophene) (PEDOT) Materials

Nitrate (NO3) pollution in groundwater, caused by various factors both natural and synthetic, contributes to the decline of human health and well-being. Current techniques used for nitrate detection include spectroscopic, electrochemical, chromatography, and capillary electrophoresis. It is highly desired to develop a simple cost-effective alternative to these complex methods for nitrate detection. Therefore, a real-time poly (3,4-ethylenedioxythiophene) (PEDOT)-based sensor for nitrate ion detection via electrical property change is introduced in this study. Vapor phase polymerization (VPP) is used to create a polymer thin film. Variations in specific parameters during the process are tested and compared to develop new insights into PEDOT sensitivity towards nitrate ions. Through this study, the optimal fabrication parameters that produce a sensor with the highest sensitivity toward nitrate ions are determined. With the optimized parameters, the electrical resistance response of the sensor to 1000 ppm nitrate solution is 41.79%. Furthermore, the sensors can detect nitrate ranging from 1 ppm to 1000 ppm. The proposed sensor demonstrates excellent potential to detect the overabundance of nitrate ions in aqueous solutions in real time.

The EcoChip 2: An Autonomous Sensor Platform for Multimodal Bio-environmental Monitoring of the Northern Habitat

This paper presents the EcoChip 2, an autonomous multimodal bio-environmental sensor platform for the monitoring of microorganisms in the northern habitat. The EcoChip 2 prototype includes an array of 96-wells for the continuous monitoring of microbiological growth through a multichannel electrochemical impedance analyzer circuit. In addition, the platform includes luminosity, humidity, temperature sensors and monitoring. The developed electronic board uses an ultra-low-power microcontroller unit, a custom power management unit, a low-power wireless ISM-2.45 GHz transceiver, and a flash memory to accumulate and store the sensor data over extended monitoring periods. When a wireless base station is placed within the transmission range of the EcoChip 2, an embedded low-power wireless transceiver transmits the 96-wells impedance data and the other sensor data stored in the flash memory to the user interface. We present the measured performance of the prototype, along with laboratory test results of bacterial growth measurements inside the 96 wells in parallel. We show that the EcoChip 2 can successfully measure the impedances associated with bacterial growth over several hours using an excitation frequency of 2 kHz with power consumption of 114.6 mW under operating mode.

The EcoChip: A Wireless Multi-Sensor Platform for Comprehensive Environmental Monitoring

This paper presents the EcoChip, a new system based on the state-of-the-art electro-chemical impedance (EIS) technologies allowing the growth of single strain organisms isolated from northern habitats. This portable system is a complete and autonomous wireless platform designed to monitor and cultivate microorganisms directly sampled from their natural environment, particularly from harsh northern environments. Using 96-well plates, the EcoChip can be used in the field for real-time monitoring of bacterial growth. Manufactured with high-quality electronic components, this new EIS monitoring system is designed to function at a low excitation voltage signal to avoid damaging the cultured cells. The high-precision calibration network leads to high-precision results, even in the most limiting contexts. Luminosity, humidity, and temperature can also be monitored with the addition of appropriate sensors. Access to robust data storage systems and power supplies is an obvious limitation for northern research. That is why the EcoChip is equipped with a flash memory that can store data over long periods of time. To resolve the power issue, a low-power micro-controller and a power management unit control and supply all electronic building blocks. Data stored in the EcoChip's flash memory can be transmitted through a transceiver whenever a receiver is located within the functional transmission range. In this paper, we present the measured performance of the system, along with results from laboratory tests in vitro and from two field tests. The EcoChip has been utilized to collect bio-environemental data in the field from the northern soils and ecosystems of Kuujjuarapik and Puvirnituq, during two expeditions, in 2017 and 2018, respectively. We show that the EcoChip can effectively carry out EIS analyses over an excitation frequency ranging from 750 Hz to 10 kHz with an accuracy of 2.35%. The overall power consumption of the system was 140.4 mW in normal operating mode and 81 μW in sleep mode. The proper development of the isolated bacteria was confirmed through deoxyribonucleic acid sequencing, indicating that bacteria thrive in the EcoChip's culture wells while the growing conditions are successfully gathered and stored.

A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities

In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM_2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM_2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM_2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM_2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM_2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM_2.5 concentration. In the future, this study can also be applied to the prevention and control of PM_2.5.

Practical Application of Electrochemical Nitrate Sensor under Laboratory and Forest Nursery Conditions

The reduction of nitrate leaching to ensure greater protection of groundwater quality has become a global issue. The development of new technologies for more accurate dosing of nitrates helps optimize fertilization programs. This paper presents the practical application of a newly developed electrochemical sensor designed for in situ quantification of nitrate. To our knowledge, this paper is the first to report the use of electrochemical impedance to determine nitrate concentrations in growing media under forest nursery conditions. Using impedance measurements, the sensor has been tested in laboratory and compared to colorimetric measurements of the nitrate. The developed sensor has been used in water-saturated growing medium and showed good correlation to certified methods, even in samples obtained over a multi-ion fertilisation season. A linear and significant relationship was observed between the resistance and the concentration of nitrates (R² = 0.972), for a range of concentrations of nitrates. We also observed stability of the sensor after exposure of one month to the real environmental conditions of the forest nursery.

A Wireless Multi-Sensor Dielectric Impedance Spectroscopy Platform

This paper describes the development of a low-cost, miniaturized, multiplexed, and connected platform for dielectric impedance spectroscopy (DIS), designed for in situ measurements and adapted to wireless network architectures. The platform has been tested and used as a DIS sensor node on ZigBee mesh and was able to interface up to three DIS sensors at the same time and relay the information through the network for data analysis and storage. The system is built from low-cost commercial microelectronics components, performs dielectric spectroscopy ranging from 5 kHz to 100 kHz, and benefits from an on-the-fly calibration system that makes sensor calibration easy. The paper describes the microelectronics design, the Nyquist impedance response, the measurement sensitivity and accuracy, and the testing of the platform for in situ dielectric impedance spectroscopy applications pertaining to fertilizer sensing, water quality sensing, and touch sensing.