Sensor Electrode Research Articles

Design methodology for optimal sensor placement for cure monitoring and load detection of sensor-integrated, gentelligent composite parts

AbstractSelecting right positions for composite-integrated sensors for monitoring cure during manufacturing and loads during product use presents challenges for engineering design. Since an optimal sensor placement (OSP) methodology for both phases is not emphasised enough in literature, a new methodology is proposed. This methodology is based on a Genetic Algorithm and strain gauges, temperature sensors and interdigitated electrode sensors for cure monitoring and physics-informed neural network-based load detection. Additionally, it includes sensor node positions optimization in a sensor network.

Development of Ag2O–TeO2-based glass and glass/stainless steel reference electrodes for pH sensors

Glass pH sensors are unsuitable for in vivo biomedical, clinical, or food applications because of the brittleness of glass, the difficulty in measuring small volumes and complicated matrix, and the necessity of calibration. Enamel structures such as glass/stainless steel (SUS) may substitute glass-based pH electrodes. This study developed new reference electrodes for pH sensors using ternary Ag2O–TeO2-MaOb (M = B, Ge, and P) glass and glass/SUS. The effect of the partial replacement of TeO2 in Ag2O–TeO2 glasses with B2O3, GeO2, and P2O5 on the pH sensitivity was investigated. (1) The effect of the partial replacement on the pH 4–9 sensitivity, which is defined as 100% for the ideal working electrode and 0% for the ideal reference electrode estimated from pH potential, became prominent in the order of GeO2, B2O3, and P2O5 in glass electrodes. Ag2O–TeO2–B2O3 glasses showed the lowest pH 7–9 sensitivity of 1% among the present electrodes. Ag2O–TeO2–P2O5 glasses showed the lowest pH 4–9 sensitivities of 24–33% among the present electrodes. (2) 25Ag2O･55TeO2･20P2O5/SUS showed a very low pH 4–9 sensitivity of 5% in glass/SUS electrodes. In this case, AgP2 may be responsible for the low pH 4–9 sensitivity because only AgP2 was detected from XRD measurements, and a small particle on the surface was observed in AFM observations. On the other hand, the crystallization of the glass matrix of 25Ag2O･55TeO2･20P2O5/SUS was suppressed by adding P2O5. Thus, 25Ag2O･55TeO2･20P2O5/SUS is a candidate for a robust KCl-leakless reference electrode for pH sensors.

Factorial experimental design based on multiple factors for sensors and actuators development

Factorial experimental design based on multiple factors for sensors and actuators development

Benchtop Fabricated Nano-Roughened Microstructured Electrodes for Electrochemical and Surface-Enhanced Raman Scattering Sensing.

Tailoring a material's surface with hierarchical structures from the micro- to the nanoscale is key for fabricating highly sensitive detection platforms. To achieve this, the fabrication method should be simple, inexpensive, and yield materials with a high density of surface features. Here, using benchtop fabrication techniques, gold surfaces with hierarchically structured roughness are generated for sensing applications. Hierarchical gold electrodes are prepared on pre-stressed polystyrene substrates via electroless deposition and amperometric pulsing. Electrodes fabricated using 1mm H[AuCl₄] and roughened with 80 pulses revealed the highest electroactive surface area. These electrodes are used for enzyme-free detection of glucose in the presence of bovine serum albumin and achieved a limit of detection of 0.36mm, below glucose concentrations in human blood. The surfaces nanoroughened with 100 pulses also showed excellent surface-enhanced Raman scattering (SERS) response for the detection of rhodamine 6G, with an enhancement factor of ≈2×106 compared to detection in solution, and for the detection of a self-assembled monolayer of thiophenol, with an enhancement factor of ≈30 compared to the response from microstructured gold surfaces. It is envisioned that the simplicity and low fabrication cost of these gold-roughened structures will expedite the development of electrochemical and SERS sensing devices.

Investigation of Electrochemical Properties and Behaviors of NBITEP Molecule; Availability of Sensor Electrode in Determination of Hg2+ with SWAdSV Technique

This Master's Thesis focuses on the synthesis and structural elucidation of 4-(2-((6-nitro-1h-benzo[d]imidazol-2-yl)thio)ethyl)phenol (NBITEP) molecule, along with its electrochemical behaviors. Electrochemical studies were carried out employing a glassy carbon (GC) electrode as the working electrode. The cyclic voltammetry (CV) technique was utilized for modification and characterization processes. Furthermore, the potential of the modified surface as a sensor electrode for Hg2+ ion detection was investigated using square wave adsorptive stripping voltammetry (SWAdSV). Detailed examinations were conducted in a phosphate buffer solution (PBS) medium specifically for Hg2+ ion detection, demonstrating the suitability of the reduced NBITEP modified GC electrode surface as a sensor electrode.

SrMoO4-based mixed-potential gas sensor for NH3 sensing in direct ammonia-fed fuel cells

Direct ammonia-fed fuel cells (DAFCs) are well known for sustainable energy production. However, the issue is a certain amount of unreacted NH3 fuel is obtained at the outlet. Thus, real-time monitoring and quantification are of utmost importance, nevertheless, this is troublesome due to the harsh thermodynamic conditions. This work aims to develop a mixed-potential sensing electrode for the detection of NH3 under reducing conditions (H2 atmosphere) at high temperatures. In this regard, a strontium molybdate (SrMoO4) sensing electrode (SE) is a potential solution due to its physical and chemical stability, along with its sensitivity to NH3 under reducing conditions. Therefore, an SrMoO4-based mixed-potential sensor is fabricated herein using yttria-stabilized zirconia (YSZ) as the electrolyte and platinum (Pt) as the reference electrode (RE) and is operated at 400–650 ℃. The maximum response exhibits about –21.57 mV toward 80 ppm NH3 at 500 ℃. Further, the sensitivity is –17.19 mV/decade towards 10–60 ppm and –45.33 mV/decade towards 60–320 ppm NH3 under 75% H2 balanced by N2 at 500 ℃. The as-fabricated sensor also has good selectivity towards NH3 and good cross-sensitivity towards other interfering gases and is stable in long-term repeated operation. The direct current (DC) polarization curves and electrochemical impedance spectroscopy (EIS) results demonstrate the sensing mechanism in accordance with the mixed-potential theory.

Hierarchical Structure by Self‐sedimentation of Liquid Metal for Flexible Sensor Integrating Pressure Detection and Triboelectric Nanogenerator

AbstractMultifunctional integration is a pivotal aspect in the development of flexible sensors that facilitate augmented interaction with the physical environment. However, contemporary multifunctional flexible sensors face challenges arising from their complex structures and high costs. Therefore, polydimethylsiloxane (PDMS) /liquid metal (LM) films with a hierarchical structure prepared via the gravity‐induced deposition of LM are applied to multifunctional flexible sensors. PDMS/LM films exhibit remarkable flexibility and function as flexible electrodes for pressure sensors as well as a single‐electrode mode triboelectric nanogenerator (TENG). The porous PDMS/carbon nanotube (CNT) dielectric with an array structure is developed for pressure sensing, showing commendable sensitivity (0.671 kPa−1) even under low pressures (0–17 kPa). Compared with solid and porous dielectrics, its sensitivity is improved over the entire pressure range. The sensor achieves a response time of ≈80 ms and demonstrates exceptional stability. Furthermore, when the PDMS/LM film with a hierarchical structure functions as a TENG, it can serve as a self‐powered sensor for measuring dynamic forces and aid in material recognition with the assistance of capacitive pressure sensing. This study provides a new perspective on the simplified design and integration of multifunctional flexible sensors.

The semi-analytical model of capacitive imaging (CI) sensors based on the renormalization group method

Purpose This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated. Design/methodology/approach The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing. Findings The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors. Originality/value The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

Fabrication of mechanically alloyed high entropy alloys (20-Fe-20Cr-20Ni-20Mn-20Ti) modified carbon paste electrode sensor for the cyclic voltammetric determination of methyl orange

We have successfully fabricated high entropy alloys (HEA) of composition 20-Fe-20Cr-20Ni-20Mn-20Ti by ball milling method for 20 h after optimizing the milling parameters. Generally, the HEAs are very popular for their multi-elemental cocktail effect and exhibit excellent high temperature oxidation resistance, high strength, electromagnetic, wear and corrosion resistance properties. But, HEAs also exhibit significant electrocatalytic properties due to their unique morphology, composition, and structure. Therefore, we have used planetary ball milled HEA powders for the electrochemical determination of methyl orange (MO). MO is recalcitrant and very difficult to degrade and can affect the humans as well as the environment. Therefore, we must determine the presence of MO. We have investigated the effect of scan rate, modifier and analyte concentration, and the pH variation on the anodic peak current (Ipa) of MO. The calculated active syrface area of bare carbon paste electrode (BCPE) and HEA-modified carbon paste electrode (HEA-MCPE) were found to be 0.041 cm2 and 0.137 cm2 respectively. We have obtained the maximum oxidation peak current for the 8 mg HEA-MCPE at a pH of 7.2 during the electrochemical oxidation of MO. The increased Ipa in basic medium is due to structural transformation of MO from quinoid form (in acidic medium) to azo structure (in basic medium). We have also calculated the number of electrons and the protons involved in the electrochemical reaction. The calculated values of detection limit (DL) and quantification limit (QL) of 8 mg HEA-MCPE was found to be 0.114 × 10−8 M and 0.38 × 10−8 M respectively.

Target-induced activation of dual DNAzyme recycling amplifications for sensitive electrochemical fat mass and obesity-associated protein assay

The fat mass and obesity-associated protein (FTO) is vital for modulating dynamic reversible process of N6-methyladenosine (m6A) level in the nucleus and the abnormal expression of FTO correlates with generation and development of Alzheimer's disease, obesity and several cancers. The sensitive detection of FTO is therefore of significance for diagnosis of FTO-related diseases. In this work, on the basis of the demethylation enzyme capability of FTO and the dual Mg2+-dependent DNAzyme recycled signal magnification strategy, establishment of a highly sensitive electrochemical FTO biosensor is described. The target FTO molecules catalyze the demethylation of m6A-modified sequences to form effective cleavage substrates for the MboI endonuclease, leading to the release of active DNAzyme sequences to trigger subsequent dual DNAzyme recycling cycles. Many methylene blue-tagged ssDNA sequences can thus be yielded via the DNAzyme amplifications and captured on the sensor electrode to generate highly amplified currents for detecting FTO in range of 0.04 pM to 10 nM with 6.11 fM detection limit. Additionally, the sensor demonstrates high selectivity and is able to be used for detecting cell lysate FTO, showing its potential for the diagnosis and study of FTO-related diseases for various application purposes.

SPOOC (Sensor for Periodic Observation of Choline): An Integrated Lab-on-a-Spoon Platform for At-Home Quantification of Choline in Infant Formula.

Choline is an essential micronutrient for infants' brain development and health. To ensure that infants receive the needed daily dose of choline, the U.S. Food and Drug Administration (FDA) has set requirements for choline levels in commercialized infant formulas. Unfortunately, not all families can access well-regulated formulas, leading to potential inadequacies in choline intake. Economic constraints or difficulties in obtaining formulas, exacerbated by situations like COVID-19, prompt families to stretch formulas. Accurate measurement of choline in infant formulas becomes imperative to guarantee that infants receive the necessary nutritional support. Yet, accessible tools for this purpose are lacking. An innovative integrated sensor for the periodic observation of choline (SPOOC) designed for at-home quantification of choline in infants' formulas and milk powders is reported. This system is composed of a choline potentiometric sensor and ionic-liquid reference electrode developed on laser-induced graphene (LIG) and integrated into a spoon-like device. SPOOC includes a micro-potentiometer that conducts the measurements and transmits results wirelessly to parents' mobile devices. SPOOC demonstrated rapid and accurate assessment of choline levels directly in pre-consuming infant formulas without any sample treatment. This work empowers parents with a user-friendly tool for choline monitoring promoting informed nutritional decision-making in the care of infants.

Decoration of atomic PdxAuy (1 ≤ x + y ≤ 4) clusters on polyaniline in electrochemical sensing of 1-propanol

Atomic PdxAuy (1 ≤ x + y ≤ 4) clusters are decorated on polyaniline to be used as the catalytic electrode in electrochemical sensors. The current responses in the oxidation of 1-propanol are used to reveal the catalytic activity. Effects of the size, decoration sequence, and composition of the atomic metal clusters decorated on the PANI on the catalytic activity are evaluated. Regarding the size, an odd-even size effect is observed in the atomic pure metal clusters, which the even-numbered pure metal clusters show a higher catalytic activity than the odd-numbered pure metal clusters. For the decoration sequence, the catalytic activity is high when the same metal is decorated in the first two cycles of the cyclic atomic electrodeposition process. For the composition effect, the atomic metal clusters show a high catalytic activity in the oxidation, in the region of second oxidation peak, when there are more Au in the atomic metal clusters.

Study of the interdigital electrode sensor at resonance frequency during water transition

This paper uses the co-planar interdigital electrode (IDE) sensor to measure water level. The researchers generally characterize the interdigital electrode sensor as a fringe field capacitor sensor developed on the printed circuit board and utilize the capacitor sensor's properties for liquid-level measurement. The interdigital electrode sensors illustrate more than one resonance at the higher frequencies, and in this study, the first resonance frequency fr -has been utilized for the water level measurement. Three water types are examined here: distilled, tap, and river. The study assesses that with the transition of water, the permittivity between the electrodes is changed and, it leads to a change in capacitance hence, the change in resonance frequency was observed. The proposed sensor can be represented by the lumped element equivalent series RLC circuit. The developed IDE sensor has good repeatability, small variability, and small hysteresis error. The maximum standard error for distilled, tap, and river water are 0.02833, 0.02503, and 0.02618, respectively, and the hysteresis error is less than 1.903% of full-scale output variation. The maximum error for the fr estimation is about ±2 Hz.

Laser‐induced graphene formation on recycled woods for green smart furniture

AbstractIn the pursuit of carbon neutrality policies, the development of eco‐friendly and intelligent furniture commands a significant role. However, the integration of non‐biodegradable electronic components in smart furniture fabrication has led to substantial electronic waste. Here, we report a straightforward approach, the rapid production of Laser‐Induced Graphene (LIG) on medium‐density fiberboard (MDF), a prevalent recycled wood in furniture production. This LIG electrode is crafted with negligible material ablation in ambient air with the aid of femtosecond laser pulses, without requiring any additional materials, showcasing the highest electrical conductivity (2.781 Ω sq−1) among previously reported lignocellulosic materials‐based LIG. The application of this LIG electrode for lighting, heating, and touch sensors displays sufficient performance for smart furniture implementation. For eco‐conscious furniture, LIG‐based human‐machine interfaces are demonstrated on recycled woods for the facile control of smart devices, which will readily enable IoT‐oriented smart sustainable furniture.image

Design and fabrication of electrochemical sensor based on NiO/Ni@C-Fe3O4/CeO2 for the determination of niclosamide

In this study, we aimed to enhance and accelerate the electrochemical properties of a glassy carbon-based voltammetric sensor electrode. This was achieved through the modification of the electrode using a nanocomposite derived from a metal–organic framework, which was embedded onto a substrate consisting of metal oxide nanoparticles. The final product was an electrocatalyst denoted as NiO/Ni@C-Fe3O4/CeO2, tailored for the detection of the drug niclosamide. Several techniques, including FT-IR, XRD, XPS, FE-SEM, TEM, and EDS, were employed to characterize the structure and morphology of this newly formed electroactive catalyst. Subsequently, the efficiency of this electrocatalyst was evaluated using cyclic voltammetry and electrochemical impedance spectroscopy techniques. Differential pulse voltammetry was also utilized to achieve heightened sensitivity and selectivity. A comprehensive exploration of key factors such as the catalyst quantity, optimal instrumental parameters, scan rate influence, and pH effect was undertaken, revealing a well-regulated reaction process. Furthermore, the sensor's analytical performance parameters were determined. This included establishing the linear detection range for the target compound within a specified concentration interval of 2.92 nM to 4.97 μM. The detection limit of 0.91 nM, repeatability of 3.1%, and reproducibility of 4.8% of the sensor were calculated, leading to the observation of favorable stability characteristics. Conclusively, the developed electrochemical sensor was successfully employed for the quantification of niclosamide in urine samples and niclosamide tablets. This application highlighted not only the sensor’s high selectivity but also the satisfactory and accurate outcomes obtained from these measurements.

Inkjet Printed Potentiometric Sensors for Nitrate Detection Directly in Soil enabled by a Hydrophilic Passivation Layer

AbstractAgricultural intensification has increased the use of chemical fertilizers, promoting plant growth and crop yield. Excessive use of nitrogen fertilizers leads to nutrient loss and low nitrogen use efficiency. Management of nitrogen fertilizer input requires close to real‐time information about the soil nitrate concentration. While there is extensive work developing nitrate ion sensing solutions for liquid media, few allow for in‐soil measurements. This study introduces inkjet‐printed potentiometric sensors, containing 2 electrodes, the reference electrode (RE) and the nitrate‐selective film‐encapsulated working electrode (WE). The interaction between the nitrate‐sensitive membrane and soil nitrate ions causes a change in potential across the RE and WE. Additionally, a hydrophilic Polyvinylidene Fluoride (PVDF) layer ensures the long‐term functionality of the sensor in wet soil environments by protecting it from charged soil particles while simultaneously allowing water to flow from the soil toward the sensor electrodes. The sensors are tested in sand and silt loam soil, demonstrating their versatility across soil types. The potential change can be related to the nitrate concentration in soil, with typical sensitivities of 45–55 mV decade−1. Overall, the use of the PVDF layer allows for direct sensing in moist soil environments, which is critical for developing soil nitrate sensors.

Fabrication of Highly Ordered Nanopillar Array Electrode for High-Performance Humidity Sensors

Fabrication of Highly Ordered Nanopillar Array Electrode for High-Performance Humidity Sensors

Development of Machine Learning Models for Ion-Selective Electrode Cation Sensor Design.

Polyvinyl chloride (PVC) membrane-based ion-selective electrode (ISE) sensors are common tools for water assessments, but their development relies on time-consuming and costly experimental investigations. To address this challenge, this study combines machine learning (ML), Morgan fingerprint, and Bayesian optimization technologies with experimental results to develop high-performance PVC-based ISE cation sensors. By using 1745 data sets collected from 20 years of literature, appropriate ML models are trained to enable accurate prediction and a deep understanding of the relationship between ISE components and sensor performance (R 2 = 0.75). Rapid ionophore screening is achieved using the Morgan fingerprint based on atomic groups derived from ML model interpretation. Bayesian optimization is then applied to identify optimal combinations of ISE materials with the potential to deliver desirable ISE sensor performance. Na+, Mg2+, and Al3+ sensors fabricated from Bayesian optimization results exhibit excellent Nernst slopes with less than 8.2% deviation from the ideal value and superb detection limits at 10-7 M level based on experimental validation results. This approach can potentially transform sensor development into a more time-efficient, cost-effective, and rational design process, guided by ML-based techniques.

Design and Optimization of Sensor Electrode Geometry for Hormone Sensing by COMSOL Multiphysics Software

Design and Optimization of Sensor Electrode Geometry for Hormone Sensing by COMSOL Multiphysics Software