Impedance Sensor for Real-Time Ammonium Detection Based on MWCNT/ZnO Nanocomposites.

This work reports on the development of an impedance sensor-based real-time-field specific system to monitor aqueous Ammonium (NH4+). The sensing element was fabricated by modifying screen-printed interdigitated electrodes (IDEs) with a hybrid nanocomposite of Multi-Wall Carbon Nanotube (MWCNT) with Zinc Oxide (ZnO) nanocrystals. The NH4+ of the water was monitored, and it exhibited a sensitivity of 67.13 Ω /mM with average correlation coefficients of 0.80. The impedance magnitude ( Ω ) of the NH4+ sensor was unaffected by the presence of Fe2+, Ni2+, K+ and P+ interfering cations. The developed sensor was interfaced with an IoT-enabled NodeMCU microcontroller, enabling a direct method for continuous monitoring of NH4+ concentrations. This integrated system is interconnected to the field-deployed sensor nodes, which provide real-time NH4+ levels to the remote user through web applications.

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.

An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices.

Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors.

We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH4+ ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH4+ ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH4+ concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH4+ ion levels. The proposed NH4+ sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.

Deep-Learning Based Estimation of Dielectrophoretic Force.

The ability to accurately quantify dielectrophoretic (DEP) force is critical in the development of high-efficiency microfluidic systems. This is the first reported work that combines a textile electrode-based DEP sensing system with deep learning in order to estimate the DEP forces invoked on microparticles. We demonstrate how our deep learning model can process micrographs of pearl chains of polystyrene (PS) microbeads to estimate the DEP forces experienced. Numerous images obtained from our experiments at varying input voltages were preprocessed and used to train three deep convolutional neural networks, namely AlexNet, MobileNetV2, and VGG19. The performances of all the models was tested for their validation accuracies. Models were also tested with adversarial images to evaluate performance in terms of classification accuracy and resilience as a result of noise, image blur, and contrast changes. The results indicated that our method is robust under unfavorable real-world settings, demonstrating that it can be used for the direct estimation of dielectrophoretic force in point-of-care settings.

Real Time Sensing of Soil Potassium Levels Using Zinc Oxide-Multiwall Carbon Nanotube-Based Sensors.

There is a significant interest in the detection and monitoring of nutrient levels in agriculture farms. In this article, we report the fabrication of Zinc oxide (ZnO) modified multi-walled Carbon nanotube (F-MWCNT) sensor specifically developed for soil nutrient sensing. A thin layer of Valinomycin membrane was grown on the top of the F-MWCNT/ZnO nanocomposite active layer. The resulting composite structure Al/F-MWCNT/ZnO/Valinomycin was found to have a proportional impedance change with soil Potassium (K+) levels. The performance of the sensor was investigated in the 1- 5 kHz range. The impedance magnitude was found to linearly decrease from 2.5± 0.23 to [Formula: see text] range for K+ concentrations from 5 to 25 mM displaying a sensitivity of [Formula: see text]/mM with a correlation coefficient (R2) of 0.95744.

Disposable aptamer-sensor aided by magnetic nanoparticle enrichment for detection of salivary cortisol variations in obstructive sleep apnea patients.

We report a disposable point-of-care sensing platform specific to salivary cortisol detection. The sensor is inkjet printed on a paper substrate with a metalloporphyrin based macrocyclic catalyst ink that can electrochemically reduce cortisol, captured by aptamer functionalized magnetic nanoparticles. The sensor consists of a thin magnet disc, aligned at the back of the electrode, in order to populate the magnetic nanoparticle bound cortisol at the sensing electrode area. Proof of concept studies were performed to detect salivary cortisol levels in human subjects with high and low risks for obstructive sleep apnea (OSA). High selectivity was observed to salivary cortisol against a background of closely related steroids.

Platinum black electrodeposited thread based electrodes for dielectrophoretic assembly of microparticles.

We report dielectrophoretic (DEP) assembly of biological cells and microparticles using platinum-black electrodeposited conductive textile fiber. The three-dimensional conductive structures with high aspect ratios were found to facilitate high electric field regions, as revealed by scanning electron microscope characterization. The effective conducting area (Aeff) and its stability of thread electrodes were estimated using electrochemical methods. Potential of platinum black electrodeposited thread as 3-D electrodes for creating high gradient electrical field for dielectrophoretic assembly of microspheres and Saccharomyces cerevisiae (yeast cells) into 1D and two-dimensional structures over long ranges under the application of low voltages (4-10 Vpp) has been demonstrated. The formation of highly ordered pearl chains of microparticles using thread electrodes when subjected to dielectrophoresis (DEP) has been discussed in detail.

Flexible Bioimpedance Sensor for Label-Free Detection of Cell Viability and Biomass.

We introduce a flexible microfluidic bioimpedance sensor that is capable of detecting biomass and cell viability variations in a cell suspension. The sensor is developed on indium tin oxide (ITO) coated polyethylene terephthalate (PET) substrate and is devoid of gold, silicon, PDMS, or glass. In conjugation with a custom built PCB read-out module, the impedance characteristics of a cell suspension can be measured within one minute of sample introduction using liquid volumes less than 5 µL. The portable sensor system occupies very little bench space and has the potential to be developed as a disposable electrical bioimpedance probe for rapid detection of dielectric variations in a biological suspension. The sensor is designed to generate a differential impedance spectra exclusive to a cell suspension with a dual-electrode-pair system. The potential of the sensor to discriminate between live and heat treated Saccharomyces cerevisiae is demonstrated in this study. The disposable sensor along with the distance variation technique is touted to be an inexpensive alternative to some of the existing online disposable biomass detection probes and electrochemical sensors.

Direct imaging of DNA motif sequences with encoded nanoparticles.

We present a method for encoded tagging and imaging of short nucleic acid motif chains (oligomotifs) using selective hybridization of heterogeneous Au nanoparticles (Au-NP). The resulting encoded NP string is thus representative of the underlying motif sequence. As the NPs are much more massive than the motifs, the motif chain order can be directly observed using scanning electron microscopy. Using this technique we demonstrate direct sequencing of oligomotifs in single DNA molecules consisting of four 100-nt motif chains tagged with four different types of NPs. The method outlined is a precursor for a high density direct sequencing technology.

Miniaturization of EISCAP sensor for triglyceride detection.

In this paper we discuss the fabrication and characterization of miniaturized triglyceride biosensors on crystalline silicon and porous silicon (PS) substrates. The sensors are miniaturized Electrolyte Insulator Semiconductor Capacitors (mini-EISCAPs), which primarily sense the pH variation of the electrolyte used. The lipase enzyme, which catalyses the hydrolysis of triglycerides, was immobilized on the sensor surface. Triglyceride solutions introduced into the enzyme immobilized sensor produced butyric acid which causes the change in pH of the electrolyte. Miniaturized EISCAP sensors were fabricated using bulk micromachining technique and have silicon nitride as the pH sensitive dielectric layer. The sensors are cubical pits of dimensions 1,500 microm x 1,500 microm x 100 microm which can hold an electrolyte volume of 0.1 microl. The pH changes in the solution can be sensed through the EISCAP sensors by monitoring the flatband voltage shift in the Capacitance-Voltage (C-V) characteristics taken during the course of the reaction. The reaction rate is found to be quite high in the miniature cells when compared to the sensors of bigger dimensions.

Comparison of a potentiometric and a micromechanical triglyceride biosensor.

Sensitive biosensors for detection of triglyceride concentration are important. In this paper we report on two types of silicon based triglyceride sensors: an electrolyte-insulator-semiconductor capacitor (EISCAP) which is a potentiometric device and a polysilicon microcantilever. The detection principle for both sensors is based on the enzymatic hydrolysis of triglyceride though the sensing mechanisms are different: electronic for the EISCAP and mechanical for the microcantilever. The characteristics and performances of the two sensors are critically compared. The EISCAP sensor necessitates the presence of a buffer for stable measurements which limits the sensitivity of the sensor at low concentrations of the bioanalyte to 1mM. The cantilever sensor works without a buffer which improves the lower level of sensitivity to 10 microm. Both sensors are found to give reproducible and reliable results.