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Dielectric particles in a non-uniform electric field are subject to a force caused by a phenomenon called dielectrophoresis (DEP). DEP is a commonly used technique in microfluidics for particle or cell separation. In comparison with other separation methods, DEP has the unique advantage of being label-free, fast, and accurate. It has been widely applied in microfluidics for bio-molecular diagnostics and medical and polymer research. This review introduces the basic theory of DEP, its advantages compared with other separation methods, and its applications in recent years, in particular, focusing on the different electrode types integrated into microfluidic chips, fabrication techniques, and operation principles.

Body hydration is considered one of the most important physiological parameters to measure and one of the most challenging. Current methods to assess hydration are invasive and require costly clinical settings. The bio-impedance analysis offers a noninvasive and inexpensive tool to assess hydration, and it can be designed to be used in wearable health devices. The use of wearable electronics in healthcare applications has received increased attention over the last decade. New, emerging medical devices feature continuous patient monitoring and data collection to provide suitable treatment and preventive actions. In this paper, a model of human skin is developed and simulated to be used as a guide to designing a dehydration monitoring system based on a bio-impedance analysis technique. The study investigates the effect of applying different frequencies on the dielectric parameters of the skin and the resulting measured impedance. Two different interdigitated electrode designs are presented, and a comparison of the measurements is presented. The rectangular IDE is printed and tested on subjects to validate the bio-impedance method and study the interpretation of its results. The proposed design offers a classification criterion that can be used to assess dehydration without the need for a complex mathematical model. Further clinical testing and data are needed to refine and finalize the criteria.

Flexible and biodegradable sensors are advantageous for their versatility in a range of areas from smart packaging to agriculture. In this work, we characterize and compare the performance of interdigitated electrode (IDE) humidity sensors printed on different biodegradable substrates. In these IDE capacitive devices, the substrate acts as the sensing layer. The dielectric constant of the substrate increases as the material absorbs water from the atmosphere. Consequently, the capacitance across the electrodes is a function of environmental relative humidity. Here, the performance of polylactide (PLA), glossy paper, and potato starch as a sensing layer is compared to that of nonbiodegradable polyethylene terephthalate (PET). The capacitance across inkjet-printed silver electrodes is measured in environmental conditions ranging from 15 to 90% relative humidity. The sensitivity, response time, hysteresis, and temperature dependency are compared for the sensors. The relationship between humidity and capacitance across the sensors can be modeled by exponential growth with an R2 value of 0.99, with paper and starch sensors having the highest overall sensitivity. The PET and PLA sensors have response and recovery times under 5 min and limited hysteresis. However, the paper and starch sensors have response and recovery times closer to 20 min, with significant hysteresis around 100%. The PET and starch sensors are temperature independent, while the PLA and paper sensors display thermal drift that increases with temperature.

Cortisol is a key biomarker for stress detection, and its levels can be monitored using point-of-care devices with sensors such as nanoparticles and interdigitated array electrodes (IDEs). This study developed an IDE platform using barium titanate (BaTiO3) particles synthesized via colloidal precipitation with titanium tetraisopropoxide, barium chloride, and Pluronic® P123. The calcination temperatures varied between 160 °C and 340 °C, with optimal results observed at 160 °C. Scanning electron microscopy revealed particles with an average size of 26 nm, and Fourier transform infrared spectroscopy confirmed the molecular composition after the removal of P123. X-ray diffraction analysis revealed anatase and brookite phases. Brunauer-Emmett-Teller analysis indicated changes in pore morphology, with samples treated at 160 °C exhibiting a type IV(a) mesoporous structure, a surface area of 163 m2/g, and an average pore diameter of 5.24 nm. Higher temperatures led to transitions to type IV(b) at 260 °C and type V at 340 °C, with reduced pore size. Electrochemical impedance spectroscopy was employed to evaluate the performance of the IDE sensor integrated with BaTiO3 nanoparticles and albumin across cortisol concentrations ranging from 5.0 to 20 ng/mL. Impedance measurements revealed a significant decrease in impedance (Z′) with increasing cortisol concentrations, indicating increased conductivity. Specifically, Nyquist plots for a saliva sample containing 5 ng/mL cortisol—within the typical physiological range—exhibited a marked increase in charge-transfer resistance (Rct), confirming the sensor’s ability to detect low hormone levels in biological fluids. These findings underscore the potential of BaTiO3-based IDE platforms at 160 °C for stress biomarker monitoring.

In this study, a neural network was developed for the detection of acetone, ethanol, chloroform, and air pollutant NO2 gases using an Interdigitated Electrode (IDE) sensor-based e-nose system. A bioimpedance spectroscopy (BIS)-based interface circuit was used to measure sensor responses in the e-nose system. The sensor was fed with a sinusoidal voltage at 10 MHz frequency and 0.707 V amplitude. Sensor responses were sampled at 100 Hz frequency and converted to digital data with 16-bit resolution. The highest change in impedance magnitude obtained in the e-nose system against chloroform gas was recorded as 24.86 Ω over a concentration range of 0–11,720 ppm. The highest gas detection sensitivity of the e-nose system was calculated as 0.7825 Ω/ppm against 6.7 ppm NO2 gas. Before training with the neural network, data were filtered from noise using Kalman filtering. Principal Component Analysis (PCA) was applied to the improved signal data for dimensionality reduction, separating them from noise and outliers with low variance and non-informative characteristics. The neural network model created is multi-layered and employs the backpropagation algorithm. The Xavier initialization method was used for determining the initial weights of neurons. The neural network successfully classified NO2 (6.7 ppm), acetone (1820 ppm), ethanol (1820 ppm), and chloroform (1465 ppm) gases with a test accuracy of 87.16%. The neural network achieved this test accuracy in a training time of 239.54 milliseconds. As sensor sensitivity increases, the detection capability of the neural network also improves.

A prototypical metal-organic framework (MOF), a 2D periodic porous structure based on the assembly of copper ions and benzene dicarboxylate (bdc) ligands (Cu(bdc)·xH2O), was grown successfully as a thin film on interdigitated electrodes (IDEs). IDEs have been used for achieving planar CMOS-compatible low-cost capacitive sensing structures for the detection of humidity and volatile organic compounds (VOCs). Accordingly, the resultant IDEs coated with the Cu(bdc)·xH2O thin film was evaluated, for the first time, as a capacitive sensor for gas sensing applications. A fully automated setup, using LabVIEW interfaces to experiment conduction and data acquisition, was developed in order to measure the associated gas sensing performance.

For rapid real-time detection of veterinary drug residues in paddy field water, we developed a novel sensor system using interdigitated electrodes as detection probes and the STM32F405RGT6 microcontroller as the core processing unit. The hardware architecture integrates multiple functional modules including excitation signal generation, signal detection, signal processing, LoRa coupled with 4G wireless communication, voltage regulation, and lithium battery charging. The system acquires three types of measurement data (amplitude ratio, phase difference, and their combination) from water samples containing sulfamethazine, ofloxacin, doxycycline hydrochloride and tetracycline hydrochloride across a broad frequency spectrum from 200 Hz to 100 MHz. Through Competitive Adaptive Reweighted Sampling (CARS) for feature selection and artificial neural network modeling, we established a multi-input multi-output concentration prediction model. Comparative analysis demonstrated superior performance when using phase difference data as model input, achieving prediction coefficients of determination (R 2 ) between 0.7831 and 0.8713 with root mean square errors of prediction (RMSEP) ranging from 22.0759 to 28.1526 mg/L. Studies showed that this sensor device could effectively detect the contents of four veterinary drugs, namely sulfamethazine, doxycycline hydrochloride, ofloxacin, and tetracycline hydrochloride, in paddy field water, thus realizing the rapid and real-time monitoring of veterinary drugs in paddy field water.

In this paper, a new method for the wireless detection of liquid level is proposed by integrating a capacitive IDC-sensing element with a passive three-port RFID-sensing architecture. The sensing element transduces changes in the liquid level to corresponding fringe-capacitance variations, which alters the phase of the RFID backscattered signal. Variation in capacitance also changes the resonance magnitude of the sensing element, which is associated with a high phase transition. This change in the reactive phase is used as a sensing parameter by the RFID architecture for liquid-level detection. Practical measurements were conducted in a real-world scenario by placing the sensor at a distance of approximately 2 m (with a maximum range of about 7 m) from the RFID reader. The results show that the sensor node offers a high sensitivity of 2.15°/mm to the liquid-level variation. Additionally, the sensor can be used within or outside the container for the accurate measurement of conductive- or non-conductive-type liquids due to the use of polyethylene coating on the sensitive element. The proposed sensor increases the reliability of the current level sensors by eliminating the internal power source as well as complex signal-processing circuits, and it offers real-time response, linearity, high sensitivity, and excellent repeatability, which are suitable for widespread deployment of sensor node applications.

Irrigation water is a primary source of fresh produce contamination by bacteria during the preharvest, particularly in hydroponic systems where the control of pests and pathogens is a major challenge. In this work, we demonstrate the development of a Listeria biosensor using platinum interdigitated microelectrodes (Pt-IME). The sensor is incorporated into a particle/sediment trap for the real-time analysis of irrigation water in a hydroponic lettuce system. We demonstrate the application of this system using a smartphone-based potentiostat for rapid on-site analysis of water quality. A detailed characterization of the electrochemical behavior was conducted in the presence/absence of DNA and Listeria spp., which was followed by calibration in various solutions with and without flow. In flow conditions (100 mL samples), the aptasensor had a sensitivity of 3.37 ± 0.21 kΩ log-CFU−1 mL, and the LOD was 48 ± 12 CFU mL−1 with a linear range of 102 to 104 CFU mL−1. In stagnant solution with no flow, the aptasensor performance was significantly improved in buffer, vegetable broth, and hydroponic media. Sensor hysteresis ranged from 2 to 16% after rinsing in a strong basic solution (direct reuse) and was insignificant after removing the aptamer via washing in Piranha solution (reuse after adsorption with fresh aptamer). This is the first demonstration of an aptasensor used to monitor microbial water quality for hydroponic lettuce in real time using a smartphone-based acquisition system for volumes that conform with the regulatory standards. The aptasensor demonstrated a recovery of 90% and may be reused a limited number of times with minor washing steps.

Interdigitated electrodes (IDEs) are commonly employed in biological cellular characterization techniques such as electrical cell-substrate impedance sensing (ECIS). Because of its simple production technique and low cost, interdigitated electrode sensor design is critical for practical impedance spectroscopy in the medical and pharmaceutical domains. The equivalent circuit of an IDE was modeled in this paper, it consisted of three primary components: double layer capacitance, Cdl, solution capacitance, CSol, and solution resistance, RSol. One of the challenging optimization challenges is the geometric optimization of the interdigital electrode structure of a sensor. We employ metaheuristic techniques to identify the best answer to problems of this kind. multi-objective optimization of the IDE using multi-objective particle swarm optimization (MOPSO) was achieved to maximize the sensitivity of the electrode and minimize the Cut-off frequency. The optimal geometrical parameters determined during optimization are used to build the electrical equivalent circuit. The amplitude and phase of the impedance versus frequency analysis were calculated using EC-LAB® software, and the corresponding conductivity was determined.