Dhanekar, Saakshi

A sensitive and rapid electrochemical sensor has been developed to detect Escherichia coli (E. coli) bacteria that is crucial for ensuring safe drinking water. E. coli significantly contributes to waterborne contamination, driven by overexploitation and insufficient cleanliness around water bodies. This work incorporates synthesis and characterization of graphene oxide (GO), sensor preparation, and sensor testing in the presence of varying E. coli dilutions via H2O2 decomposition. GO is used as a sensing layer and drop casted on the working electrode of screen printed electrode (SPE). The sensor is exposed to different bacterial solutions using varied bacterial concentrations and fixed percent solution of H2O2. The interface of GO and bacterial solution results in a change in potential. The cross-sensitivity tests have also been performed in the presence of chemical compounds found in wastewater samples, Pseudomonas aeruginosa and Citrobacter youngae. The sensor demonstrates a detection limit of 2.8 CFU/mL, with a sensitivity of 5.1 mV-mL/CFU, fast response time and excellent repeatability when tested with E. coli. Its performance has also been assessed by comparing the sensing results for regular tap water, reverse osmosis (RO) water, and deionized water samples. This work paves the way for developing a chip-based system to detect E. coli in water.

Recognizing the need for a hand-held device capable of quantitatively measuring the concentration of bacteria in water, this paper describes a label-free method for rapid detection of Escherichia coli (E. coli) in water via H2O2 decomposition using screen-printed electrodes (SPE) modified with nanostructured metal oxide layers. The study encompasses sensor preparation, bacteria culture, synthesis and characterization of nanostructures, and development of a readout circuitry for lab prototyping. During sensing measurements, the bacteria are first made to interact with H2O2 and subsequently, the H2O2 solution is exposed on the sensing surface. The electrochemical sensors are fabricated by modifying the working electrode of SPE with nanostructured metal oxide layers of MnO2 and TiO2 as these play a crucial role in the detection of E. coli in water. The sensing experiments of MnO2-modified SPE show a significant response to bacteria with a sensitivity of 0.82 mV.mL/log CFU and a limit of detection (LOD) of 1.8 CFU/mL, while the TiO2-modified SPE exhibits a linear response over a wide range of bacterial concentrations with a sensitivity of 1.12 mV·mL/log CFU and a limit of detection of 2.23 CFU/mL. Both sensors demonstrate a rapid response, stability, repeatability, and a recovery time of 70 ms. Additionally, selectivity with respect to other bacteria, wastewater components such as glucose, ammonium sulfate, and sodium carbonate, and testing with RO, DI, and tap water samples are conducted to evaluate the sensors’ performance. A detailed sensing mechanism has been developed to comprehend the results, including chemical and biological reactions, metal oxide interfaces, morphology, and other surface studies of the sensing surface. A prototype comprising a sensor chip, an Arduino board, and other necessary circuit components is tested with various bacterial solutions. This enables its use for on-field rapid detection of bacteria in water using smaller volumes and a portable system.

Electronic nose (E-Nose) comprises a sensor array combined with scientific computations to derive intelligent analysis. This work presents an E-Nose consisting of four nanosensors and performs smart analysis with machine learning (ML) models for gas type classification and concentration prediction. The sensors were tested with ethanol, acetone, isopropyl alcohol (IPA), xylene, benzene, and toluene gases, and results showed higher selectivity to ethanol and acetone. The combination of principal component analysis (PCA) and support vector classifier (SVC) having radial basis function (RBF) as kernel classified ethanol, acetone, and other tested gases with 98% accuracy. Furthermore, the regression studies performed with AdaBoost algorithm yielded R2 scores of 80% and 83% for ethanol and acetone concentration prediction, respectively.

Electronic nose (e-nose) systems are well known in breath analysis because they combine breath printing with advanced and intelligent machine learning (ML) algorithms. This work demonstrates development of an e-nose system comprising gas sensors exposed to six different volatile organic compounds (VOCs). The change in the voltage of the sensors was recorded and analyzed through ML algorithms to achieve selectivity and predict the VOCs. In this work, a novel approach to automatic learning technology that systematically categorizes and implements standard algorithms for use on gas sensors’ data set is presented. Different algorithms were compared based on F1 score, accuracy, and testing time. Performance testing of these methods is also conducted on both a Google Colab and a single-board computer, simulating their application in portable Internet of Things (IoT) sensor systems. Post validation, a simple IoT-enabled prototype was prepared that was tested in the presence of normal breath, alcohol (simulated breath), mint, mouthwash, and cardamom. The model system could classify a simulated breath alcohol sample and other breath samples with an accuracy of 0.96 obtained from the Extra Trees model. This work can be scaled up to a system wherein further breath print analysis can be used for breath diagnostic applications to detect diseases or a person’s physiological condition.

An efficient electrochemical sensor based on Cr-Au Inter Digitated Electrodes (IDE)-porous silicon has been developed to rapidly assess Escherichia coli (E. coli) bacteria in water. Coliform bacteria, particularly E. coli, contribute significantly to waterborne contamination, driven by overuse and insufficient cleanliness around water bodies. This work incorporates the fabrication of porous silicon, characterization, synthesis of bacterial dilutions and testing of the sensor in the presence of varying E. coli dilutions. The dilutions are prepared from the stock solution of bacterial concentrations and H2O2. The interaction of porous silicon with bacteria incubated in H2O2 leads to a change in potential across the electrodes in real time. The limit of detection and sensitivity for the sensor are 0.187 CFU/mL and 113mV.mL/CFU respectively. The response and recovery time of the sensor is 80 milliseconds and 90 milliseconds, respectively. Additionally, analysis such as repeatability and testing in tap water, Pseudomonas and Citrobacter are conducted. For a user-friendly output, the sensor has been interfaced with a signal conditioning circuit and a display. This prototype offers a quick and precise way to identify the quality of drinking water, making it a potential solution to the growing problems caused by water pollution.

Electronic nose (e-nose) systems are well known in breath analysis because they combine breath printing with advanced and intelligent machine learning (ML) algorithms. This work demonstrates development of an e-nose system comprising gas sensors exposed to six different volatile organic compounds (VOCs). The change in the voltage of the sensors was recorded and analyzed through ML algorithms to achieve selectivity and predict the VOCs. In this work, a novel approach to automatic learning technology that systematically categorizes and implements standard algorithms for use on gas sensors’ data set is presented. Different algorithms were compared based on F1 score, accuracy, and testing time. Performance testing of these methods is also conducted on both a Google Colab and a single-board computer, simulating their application in portable Internet of Things (IoT) sensor systems. Post validation, a simple IoT-enabled prototype was prepared that was tested in the presence of normal breath, alcohol (simulated breath), mint, mouthwash, and cardamom. The model system could classify a simulated breath alcohol sample and other breath samples with an accuracy of 0.96 obtained from the Extra Trees model. This work can be scaled up to a system wherein further breath print analysis can be used for breath diagnostic applications to detect diseases or a person’s physiological condition. © 2024 American Chemical Society.