Search Results (144)

Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications. Full article

Ammonia (NH₃) detection in liquids and biological fluids is essential for monitoring environmental contamination and industrial processes, ensuring food safety, and diagnosing health conditions. Existing detection techniques are often unsuitable for point-of-care (POC) use due to limitations including complex sample handling, lack of portability, and poor compatibility with miniaturized systems. This study introduces a proof-of-concept for a compact, portable device tailored for POC detection of gaseous ammonia released from liquid samples. The device combines a polyaniline (PANI)-based chemoresistive sensor with interdigitated electrodes and a resistance readout circuit, enclosed in a gas-permeable hydrophobic membrane that permits ammonia in the vapor phase only to reach the sensing layer, ensuring selectivity and protection from liquid interference. The ink formulation was optimized. PANI nanoparticle suspension exhibited a monomodal, narrow particle size distribution with an average size of 120 nm and no evidence of larger aggregates. A key advancement of this device is its ability to limit the impact of water vapor, a known source of interference in PANI-based sensors, while maintaining a simple sensor design. A tailored signal processing strategy was implemented, extracting the slope of resistance variation over time as a robust metric for ammonia quantification. The sensor demonstrated reliable performance across a concentration range of 1.7 to 170 ppm with strong logarithmic correlation (R² = 0.99), and very good linear correlations in low (R² = 0.96) and high (R² = 0.97) subranges. These findings validate the feasibility of this POC platform for sensitive, selective, and practical ammonia detection in clinical and environmental applications. Full article

Polyaniline (PANI) is an important conductive-polymer gas-sensing material with working temperature and mechanical flexibilities superior to those of conventional metal oxide sensing materials. However, its applicability is limited by its low sensitivity, high detection limits, and long response/recovery times. In this study, we prepared PANI/WS₂ composites via chemical oxidative polymerization and mechanical blending. A multilayer sensor structure—sequentially printed silver-paste heating electrodes, fluorene polyester insulating layer, silver interdigitated electrodes, and sensing material layer—was fabricated on a polyimide substrate via flexible microelectronic printing and systematically characterized using scanning electron microscopy, X-ray diffraction, and Fourier-transform infrared spectroscopy. The optimized 5 wt% WS₂ composite showed enhanced gas-sensing performance, with 219.1% sensitivity to 100 ppm ammonia (2.4-fold higher than that of pure PANI) and reduced response and recovery times of 24 and 91 s, respectively (compared to 81 and 436 s for pure PANI, respectively). Notably, the PANI/WS₂ sensor detected an ultralow ammonia concentration (100 ppb) with 0.104% sensitivity. The structural characterization and performance analysis results were used to deduce a mechanism for the enhanced sensing capability. These findings highlight the application potential of PANI/WS₂ composites in flexible gas sensors and provide fundamental insights for PANI-based sensing materials research. Full article

Norovirus, a foodborne pathogen, causes a significant economic and health burden globally. Although detection methods exist, they are expensive and non-field deployable. A flow-based dielectrophoretic biosensor was designed for the detection of foodborne pathogenic viruses and was tested using bacteriophage MS2 as a norovirus surrogate. The flow-based MS2 sensor comprises a concentrator and a detector. The concentrator is an interdigitated electrode array designed to impart dielectrophoretic effects to manipulate viral particles toward the detector in a fluidic channel. The detector is made of a silver electrode conjugated with anti-MS2 IgG to allow for antibody–antigen biorecognition events and is supplied with the electrical current for the purpose of measurement. Serially diluted MS2 suspensions were continuously injected into the fluidic channel at 0.1 mL/min. A cyclic voltammogram indicated that current measurements from single-walled carbon nanotube (SWCNT)-coated electrodes increased compared to uncoated electrodes. Additionally, a drop in the current measurements after antibody immobilization and MS2 capture was observed with the developed electrodes. Antibody immobilization at the biorecognition site provided greater current changes with the antibody-MS2 complexes vs. the assays without antibodies. The electric field applied to the fluidic channel at 10 V_pp and 1 MHz contributed to an increase in current changes in response to MS2 bound on the detector and was dependent on the MS2 concentrations in the sample. The developed biosensor was able to detect MS2 with a sensitivity of 10² PFU/mL within 15 min. Overall, this work demonstrates a proof of concept for a rapid and field-deployable strategy to detect foodborne pathogens. Full article

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. Full article

Copper-thiolate nanostructures, formed through the self-assembly of cysteine (Cys) and glutathione (GSH) with copper ions, offer a versatile platform for redox-active applications due to their structural stability and chemical functionality. In this study, Cu-Cys-GSH nanoparticles were synthesized and employed to develop a capacitive biosensor for the ultralow concentration detection of hydrogen peroxide (H₂O₂). The detection mechanism leverages a Fenton-like reaction, where H₂O₂ interacts with Cu-Cys-GSH nanoparticles to generate hydroxyl radicals (·OH) through redox cycling between Cu²⁺ and Cu⁺ ions. These redox processes induce changes in the sensor’s surface charge and dielectric properties, enabling highly sensitive capacitive sensing at gold interdigitated electrodes (IDEs). The influence of chirality on sensing performance was investigated by synthesizing nanoparticles with both L- and D-cysteine enantiomers. Comparative analysis revealed that the stereochemistry of cysteine impacts the catalytic activity and sensor response, with Cu-L-Cys-GSH nanoparticles exhibiting superior performance. Specifically, the biosensor achieved a linear detection range from 1.0 fM to 1.0 pM and demonstrated an ultra-sensitive detection limit of 21.8 aM, outperforming many existing methods for H₂O₂ detection. The sensor’s practical performance was further validated using milk and saliva samples, yielding high recovery rates and confirming its robustness and accuracy for real-world applications. This study offers a disposable, low-cost sensing platform compatible with sustainable healthcare practices and facilitates easy integration into point-of-care diagnostic systems. Full article

This paper reports several preliminary investigations concerning the relative humidity (RH) detection response of a chemiresistive sensor that uses a novel sensing layer based on pristine carbon nano-onions (CNOs) and polyvinyl alcohol (PVA) at a 1/1 and 2/1 w/w ratio. The sensing device, including a Si/SiO₂ substrate and gold electrodes, is obtained by depositing the CNOs–PVA aqueous suspension on the sensing structure by drop casting. The composition and morphology of the sensing film are explored by means of scanning electron microscopy, Raman spectroscopy, atomic force microscopy, and X-ray diffraction. The manufactured sensor’s room temperature RH detection performance is examined by applying a continuous flow of the electric current between the interdigitated electrodes and measuring the voltage as the RH varies from 5% to 95%. For RH below 82% (sensing layer based on CNOs–PVA at 1/1 w/w ratio) or below 50.5% (sensing layer based on CNOs–PVA at 2/1 w/w ratio), the resistance varies linearly with RH, with a moderate slope. The newly developed sensor, using CNOs–PVA at a 1:1 ratio (w/w), responded as well as or better than the reference sensor. At the same time, the recorded recovery time was about 30 s, which is half the recovery time of the reference sensor. Additionally, the changes in resistance (ΔR/ΔRH) for different humidity levels showed that the CNOs–PVA layer at 1:1 was more sensitive at humidity levels above 80%. The main RH sensing mechanisms considered and discussed are the decrease in the hole concentration in the CNOs during the interaction with an electron donor molecule, such as water, and the swelling of the hydrophilic PVA. The experimental RH detection data are analyzed and compared with the RH sensing results reported in previously published work on RH detectors employing sensing layers based on oxidized carbon nanohorns–polyvinylpirrolidone (PVP), oxidized carbon nanohorns–PVA and CNOs–polyvinylpyrrolidone. Full article

Micro-Electro-Mechanical System (MEMS) technology is employed to fabricate surface acoustic wave (SAW)-type and resistive-type ozone sensors on quartz glass (SiO₂) substrates. The fabrication process commences by using a photolithography technique to define interdigitated electrodes (IDEs) on the substrates. Electron-beam evaporation (EBE) followed by radio frequency (RF) magnetron sputtering is then used to deposit platinum (Pt) and chromium (Cr) electrode layers as well as a zinc oxide (ZnO) sensing layer, respectively. Finally, annealing is performed to improve the crystallinity and sensing performance of the ZnO films. The experimental results reveal that the ZnO thin films provide an excellent ozone-concentration sensing capability in both sensors. The SAW-type sensor demonstrates a peak sensitivity at a frequency of 200 kHz, with a rapid response time of just 35 s. Thus, it is suitable for applications requiring a quick response and high sensitivity, such as real-time monitoring and high-precision environmental detection. The resistive-type sensor shows optimal sensitivity at a relatively low operating temperature of 180 °C, but has a longer response time of approximately 103 s. Therefore, it is better suited for low-cost and large-scale applications such as industrial-gas-concentration monitoring. Full article

In this study, an in situ electrochemical modulation method based on an ultramicro interdigitated array electrode (UIAE) sensor chip was developed for the detection of ammonia nitrogen (NH₃-N) in neutral aqueous solutions. One comb of the UIAE was used as the working electrode for both the modulating and sensing functions, while the other comb was used as the counter electrode. Utilizing its enhanced mass transfer and proximity effects, the feasibility of in situ modulation of the solution environment near the UIAE chip to generate an electrochemical response for NH₃-N was investigated using electrochemical methods. The proposed method enhances the concentration of hydroxide ions and active chloride in the local solution near the sensor chip. These reactive species play a key role in improving the sensor’s electrocatalytic oxidation capability toward ammonia nitrogen, facilitating the sensitive detection of ammonia nitrogen in neutral environments. A linear relationship was displayed, ranging from 0.15–2.0 mg/L (as nitrogen) with a sensitivity of 3.7936 µA·L·mg⁻¹ (0.0664 µA µM⁻¹ mm⁻²), which was 2.45 times that in strong alkaline conditions without modulation. Additionally, the relative standard deviation of the measurement remained below 2.9% over five days of repeated experiments, indicating excellent stability. Full article

Interleukin-8 (IL-8) is a critical biomarker associated with inflammation and disability in both adults and newborns. Conventional detection methods are often labor-intensive, time-consuming, and require highly trained personnel. Non-Faradaic impedimetric biosensors offer a label-free, rapid, and direct approach for IL-8 detection. While previous studies have primarily focused on capacitance and phase changes, the potential of other impedimetric parameters remains underexplored. In this study, a gold interdigitated electrode (Au-IDE)-based non-Faradaic biosensor was developed for IL-8 detection, evaluating multiple impedimetric parameters, including capacitance, impedance magnitude (Z_mod), real impedance (Z_real), and imaginary impedance (Z_imag). Among these, Z_imag exhibited the lowest limit of detection (LoD) at 90 pg/mL, followed by Z_mod at 120 pg/mL, and capacitance at 140 pg/mL, all significantly below the clinical threshold of 600 pg/mL. In contrast, Z_real displayed the highest LoD at 1.3 ng/mL. Sensitivity analysis revealed that Z_imag provided the highest sensitivity at 13.1 kΩ/log (ng/mL), making it the most effective parameter for detecting IL-8 at low concentrations. The sensitivity of Z_mod and Z_real was lower, while capacitance sensitivity was measured at 20 nF/log (ng/mL). These findings highlight the importance of investigating alternative impedimetric parameters beyond capacitance to optimize biosensor performance for biomarker detection. This study demonstrates that non-Faradaic biosensors, despite their capacitive-based nature, can achieve enhanced sensitivity and detection limits by leveraging additional impedimetric parameters, offering a promising approach for rapid and effective IL-8 detection. Full article

The study presents the ethanol vapor sensing performance of a resistive sensor that utilizes a quaternary nanohybrid sensing layer composed of holey carbon nanohorns (CNHox), graphene oxide (GO), SnO₂, and polyvinylpyrrolidone (PVP) in an equal mass ratio of 1:1:1:1 (w/w/w/w). The sensing device includes a flexible polyimide substrate and interdigital transducer (IDT)-like electrodes. The sensing film is deposited by drop-casting on the sensing structure. The morphology and composition of the sensitive film are analyzed using scanning electron microscopy (SEM), Energy Dispersive X-ray (EDX) Spectroscopy, and Raman spectroscopy. The manufactured resistive device presents good sensitivity to concentrations of alcohol vapors varying in the range of 0.008–0.16 mg/cm³. The resistance of the proposed sensing structure increases over the entire range of measured ethanol concentration. Different types of sensing mechanisms are recognized. The decrease in the hole concentration in CNHox, GO, and CNHox due to the interaction with ethanol vapors, which act as electron donors, and the swelling of the PVP are plausible and seem to be the prevalent sensing pathway. The hard–soft acid-base (HSAB) principle strengthens our analysis. Full article

A time-dependent electrochemical impedance spectroscopy (EIS) model is presented using the finite element method (FEM) to simulate a 2D interdigitated electrode in an aqueous NaCl electrolyte. Developed in COMSOL Multiphysics, the model incorporates ion transport, electric field distribution, Stern layer effects, and electrode sheet resistance, governed by the Poisson and Nernst–Planck equations. This model can predict the transient current response to an applied excitation voltage, which gives information about the dynamics of the electrochemical system. The simulation results are compared with the experimental data, reproducing key features of the measurements. The transient current response indicates the need for multiple excitation cycles to stabilize the impedance measurement. At low frequencies (<1 kHz), the voltage drop at the Stern layer is significant, while at higher frequencies (>100 kHz), the voltage drop due to sheet resistance dominates. Moreover, the amplitude of the excitation voltage influences the EIS measurement, higher amplitudes (above 0.1 V) lead to non-linear impedance behavior, particularly at low ion concentrations. Discrepancies at low frequencies suggest that Faradaic processes may need to be incorporated for improved accuracy. Overall, this model provides quantitative insights for optimizing EIS sensor design and highlights critical factors for high-frequency and low-concentration conditions, laying the foundation for future biosensing applications with functionalized electrodes. Full article

Despite the direct, redox-free and simple detection non-faradaic impedimetric biosensors offer, considerable optimizations are required to enhance their performance for the detection of various biomarkers. Non-faradaic EIS sensors’ performance depends on the interfacial capacitance between a polarized biosensor surface and the tested sample solution. Careful engineering and design of the interfacial capacitance is encouraged to magnify the redout signal upon bioreceptor–antigen interactions. One of the methods to achieve this goal is by optimizing the self-assembled monolayer concentration, which has not been reported for non-faradaic impedimetric sensors. Here, the impact of alkanethiolate (cysteamine) concentration on the performance of gold (Au) interdigitated electrode (Au-IDE) biosensors is reported. Six sets of biosensors were prepared, each with a different cysteamine concentration: 100 nM, 1 μM, 10 μM, 100 μM, 1 mM, and 10 mM. The biosensors were prepared for the direct detection of LDL cholesterol by attaching LDL antibodies on top of the cysteamine via a glutaraldehyde cross-linker. As the concentration of cysteamine increased from 100 nM to 100 μM, the sensitivity of the biosensor increased from 6.7 to 16.2 nF/ln (ng/mL). As the cysteamine concentration increased from 100 μM to 10 mM, the sensitivity deteriorated. The limit of detection (LoD) of the biosensor improved as the cysteamine increased from 100 nM to 100 μM (i.e., 400 ng/mL to 59 pg/mL). However, the LoD started to increase to 67 pg/mL and 16 ng/mL for 1 mM and 10 mM cysteamine concentrations, respectively. This shows that the cysteamine concentration has a detrimental effect on redox-free biosensors. The cysteamine layer has to be as thin as possible and uniformly cover the electrode surfaces to maximize positive readout signals and reduce negative signals, significantly improving both sensitivity and LoD. Full article

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. Full article

This study investigates the influence of synthesis methods and electrode geometry on the physico-chemical properties of 5%Gd-doped SnO₂. Two distinct synthesis routes, co-precipitation and hydrothermal growth, were employed, resulting in powders denoted as SnO₂: Gd 5%-CP and SnO₂: Gd 5%-HT. Morpho-structural and textural analyses reveal a uniform morphology consisting of quasi-spherical nanoparticles with dimensions of ~6 nm and mesoporosity for CP and a non-uniform morphology with larger nanoparticles of ~42 nm, with irregular shapes and macroporosity for the HT sample, respectively. The powders were deposited onto alumina substrates equipped with platinum interdigital electrodes with alternative gaps of 200 μm and 100 μm. The back-side heater allows for variation in the temperature of the layer. Sensing properties assessed under in-field-like atmospheres simulated by a computer-controlled Gas Mixing System reveal higher sensitivity to methane compared to carbon dioxide. Although the sensor signals did not differ quantitatively, they exhibited distinct saturation tendencies with an increasing methane concentration, attributed to the morpho-structure and porosity induced by the synthesis method. Differentiation was achieved by varying the interdigital gap of the electrodes, highlighting different sensor signals and conduction mechanisms, determined by the specific size of the crystallites. Full article