Resistive Sensors Research Articles

Research progress of integrating electrical impedance sensors with microfluidic chips in cell detection

Cell culture is a fundamental tool for cell-based assays in biological and preclinical research. The measurements of cell culture, including cell count, viability, and metabolic activity, can reflect the conditions of cells under culture conditions. The conventional cell culture and detection methods have problems such as high consumption of reagents and samples, inability to monitor cell status in real time, and difficulty in spatiotemporally adjusting the cell microenvironment. A cell impedance sensor measures changes in the electrical impedance of cells through alternating current, enabling real-time monitoring of impedance changes caused by cell activities such as attachment, growth, proliferation, and migration. Microfluidic chips are praised for reducing complex biological processes, integrating multiple analysis modes, and achieving high automation in detection. Integrating microfluidic chips with cell impedance sensors greatly improves the capability and efficiency of cell-related analysis. This review outlines the application of microfluidic chip-based impedance sensors in 2D and 3D cell systems and summarizes the research progress in application of such sensors in research on cell growth, proliferation, viability, metabolic activity, and drug screening. Finally, this review prospects the future development trends and possible challenges, providing ideas for the development of microfluidic chips integrated with electrical impedance sensors in drug screening.

Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency

This research paper presents the development and analysis of a multifaceted smart grid prototype. It combines various technologies for the smart grid operation. The first technology is environmental analysis of smart grid and solar panel cleaning. Secondly, radio-frequency identification (RFID)-based security and access control system has been integrated for smart grid. The third component is internet of things (IoT)-based energy monitoring and load management. For environmental analysis sensors such as temperature, humidity, light-dependent resistor, and flame sensors are connected to a NodeMCU controller for real time monitoring. Moreover, IoT based solar cleaning system is developed in the form of prototype with the help of Blynk and servo motor. The second component of prototype is smart security system which is developed with the help of Arduino and RFID module to facilitate secure access control. The third part of prototype employs voltage and current sensors with an ESP32 microcontroller and the Blynk application for real-time energy consumption analysis. This setup enables remote monitoring of voltage, power dynamics, and consumption patterns in a smart grid. It also offers an IoT based solution for load management and load shedding within the smart grid. The complete prototype overall demonstrates a comprehensive approach to 1) smart grid management, 2) environmental analysis, 3) security, and 4) energy monitoring.

Resistive gas sensors based on nanostructured ternary metal oxide: a review

Resistive gas sensors based on nanostructured ternary metal oxide: a review

Exploration of SF6 and its decomposed gases in adsorption and sensing (four modified WSe2 monolayers at quantum level)

As SF6 is the most widely used high-voltage insulating gas, the monitoring of its decomposition products to ensure the stable operation of GIS equipment and the treatment of leakage have been two important research topics. This work is based on the first principles of adsorption and sensing characterization of SF6 and its decomposed gases (HF, H2S, SO2, SOF2, SO2F2) using four modification strategies on the surface of WSe2. The energy parameters such as transfer charge, density of states, adsorption energy, band gap, and work function are calculated for each system. The results show that the electrical conductivity of the system is greatly enhanced when defect modification is employed, and the band gap is changed from 2.018 eV to 0.008 eV (W@WSe2) and 1.271 eV (Se@WSe2). The CoO and TiO2 doped WSe2 showed excellent adsorption of SF6 (−7.818 eV) and SOF2 (−14.263 eV), respectively, which is of great significance for SF6 gas leakage treatment. In addition, the results of work function calculations indicate that CoO-WSe2 has potential as a work function type sensor for these six gases. Finally, by calculating the desorption time (τ), we systematically explored whether the four modified materials have the characteristics to be used as recyclable resistive type sensors under different temperature environments. Our work will help to explore the adsorption performance and sensing characteristics of WSe2 monolayers with different modifications for SF6 and its decomposed gases, which is crucial for the protection of GIS equipment in power systems.

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning.

A Simple Method to Manufacture a Force Sensor Array Based on a Single-Material 3D-Printed Piezoresistive Foam and Metal Coating.

Nowadays, 3D printing is becoming an increasingly common option for the manufacturing of sensors, primarily due to its capacity to produce intricate geometric shapes. However, a significant challenge persists in integrating multiple materials during printing, for various reasons. In this study, we propose a straightforward approach that combines 3D printing with metal coating to create an array of resistive force sensors from a single material. The core concept involves printing a sensing element using a conductive material and subsequently separating it into distinct parts using metal-coated lines connected to the electrical ground. This post-printing separation process involves manual intervention utilizing a stencil and metallic spray. The primary obstacle lies in establishing a sufficient contact surface between the sprayed metal and the structure, to ensure effective isolation among different zones. To address this challenge, we suggest employing a lattice structure to augment the contact surface area. Through experimental validation, we demonstrate the feasibility of fabricating two sensing elements from a single-material 3D-printed structure, with a maximum electrical isolation ratio between the sensors of above 30. These findings hold promise for the development of a new generation of low-tech 3D-printed force/displacement sensor arrays.

Conductive polymer composites for resistive flexible strain sensors

The emergence of wearable devices has spurred demand for strain sensors that are flexible, stretchable, and sensitive. Traditional strain sensors often lack stretchability due to the brittle nature of their materials. Conductive polymer composites (CPCs) are well-suited for this application due to their excellent flexibility, stretchability, and sensitivity, making them the material of choice for strain sensors. Resistive strain sensors, a common type of CPCs-based sensor, are favored for their cost-effectiveness, straightforward manufacturing process, and ease of signal collection. This review explores the sensing mechanisms of various resistive strain sensors, including percolation, crack propagation, contact resistance change and tunneling effect. It examines the impact of different fabrication processes and structural designs on sensor performance parameters including sensitivity, stretchability, linearity, and repeatability. Additionally, the advantages and challenges of integrating discrete sensors into high-density strain sensor arrays are discussed. Finally, we introduce the wide-ranging applications of CPCs-based strain sensors in multiple fields.

Computational investigation of pure antimonene and antimonene/bismuthene heterostructure for detecting thermal runaway gases

The widespread use of lithium-ion batteries in everyday life has emphasized the critical need for safety precautions, particularly in monitoring the release of thermal runaway gases to ensure human well-being. This study employs density functional theory (DFT) to construct adsorption models of pure antimonene, bismuthene, below and above of the heterostructure of Sb/Bi. Pure Sb and Bi exhibited semiconducting behavior, whereas the heterostructure of antimonene with bismuthene transformed the semiconducting behavior to metallic. Target gases, including C2H4, CH4, H2, CO, and CO2, were adsorbed onto these models. All gases displayed physiosorption with the studied materials. By analyzing adsorption energy, charge transfer, energy band structure, density of states, work function, sensing response, electron localization function (ELF), and recovery time, the research reveals that the heterostructure displays strong adsorption properties, a robust sensing response, and quicker desorption times, suggesting its potential as a resistive gas sensor. This theoretical exploration offers valuable insights for experimentalists aiming to design and synthesize novel, sensitive materials for detecting thermal runaway gases.

Ultra-sensitive flexible resistive sensor based on modified PEDOT: PSS inspired by earthworm

Flexible resistive sensors currently face challenges such as low sensitivity and poor stability. In this work, inspired by the skin structure of earthworm, a double strain layer with annular cracks aiming to achieve precise construction of conductive pathways in strain materials is proposed. This design strategy enhances the sensitivity, detection range, stability, and consistency of flexible resistive sensor. The sensing materials utilized primarily consist of mechanically and electrically modified PEDOT:PSS. The sensor showcases high sensitivity (with a gauge factor up to 1973.83), a wide detection range (0 ∼ 10 %), rapid response time (60–70 ms), and exceptional repeatability (1000 cycles). The sensor is integrated into finger clips for measurement of pulse wave. By analyzing features extracted from pulse wave, neural networks are employed for the prediction of encompassing coronary heart disease (CHD), atrial septal defect (ASD), atrial fibrillation (AF) and blood pressure levels. The classification accuracy of CHD, ASD and AF surpasses 96 %. The average prediction errors for systolic blood pressure, diastolic blood pressure and mean arterial pressure are found to be −0.3455, −0.2025 and −0.0319, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society Class A criteria.

Highly sensitive and easy-to-attach wearable sensor for measuring finger force based on curvature changes in an ellipse-shaped finger ring

Technologies for digitizing worker actions to enhance human labor tasks, mitigate accidents, and prevent disabling injuries have garnered significant attention. This study focuses on monitoring the force exerted by the fingers and developing a wearable fingertip force sensor based on a simple elliptical ring structure in conjunction with a commercially available resistive bend sensor. Resembling a ring accessory, the sensor is easy to attach and detach, and exhibits high sensitivity, with a resistance change of approximately 9% for a fingertip load of 1 N. Furthermore, to mitigate crosstalk during finger flexion, we propose a combined configuration employing this ring-shaped sensor alongside another sensor designed for measuring and rectifying finger flexion angles. Additionally, we introduce an empirically derived fitting function and a straightforward calibration procedure to extract the function’s parameters. The proposed system achieves an average RMS error of 0.53 N for force estimations of approximately 5 N, even during finger flexion and postural changes.

The analytical model of time-harmonic electric field and impedance spectrum in the multilayered interdigital electrode structure

Accurately determining the time-harmonic electric field and impedance spectrum in the multilayered interdigital electrode (IDE) transducers with lossy dielectrics is essential for the design of the structure, the property estimation of the dielectric, and performance optimization. This paper presents an analytical model of the electric field and impedance within a multilayered interdigitated electrode (IDE) when subjected to alternating excitation through the method of separation of variables (MSV). The equivalent circuit of IDE is established, where the impedance is the parallel of capacitive coupling and resistive coupling between electrodes. The general solutions for the electric field and impedance involving arbitrary numbers and complex permittivities of the dielectric layers are analytically derived. However, due to the approximation of boundary conditions, these physical quantities cannot be accurately obtained by the conventional analytical method using conformal mapping technique (CMT) and partial capacitance technique (PCT). The proposed model exhibits proficiency in generating precise electric field images and impedance values that closely correspond with simulated data, even when accounting for the frequency-dependent characteristics of permittivity and conductivity in lossy dielectrics. Moreover, the approaches to improve the sensitivity of a traditional three-layered IDE-based sensor are demonstrated. For a three-layered IDE-based capacitive sensor, improving the sensitivity can be accomplished through the incorporation of a ground plane at the substrate's bottom and increasing the metallization rate. Conversely, it is recommended to reduce the metallization rate, increase substrate thickness, and remove the ground plane in order to enhance the sensitivity of the IDE-based impedance sensor. The experimental results demonstrate that the proposed model generates outstanding impedance spectra (involving capacitance spectra and resistance spectra) results for various metallization ratios and top layers. This exemplifies the model's potential for predicting, designing, and enhancing a complex multilayered IDE-based transducer to achieve precise and sensitive detection.

Enhancing Multifunctionality and Performance Indicators of Resistive‐Type Strain Sensors with Advanced Conductive Hydrogels

AbstractHydrogels are excellent options for strain sensors as they can stretch, endure mechanical stress, and possess multifunctional qualities. Resistive sensors are particularly promising among the diverse hydrogel strain sensors available. This is attributed to their dedicated focus on improving the indicators of strain‐sensing performance, simplicity of equipment, straightforward sensing mechanisms, and easy design of conductive hydrogels. Various approaches have been explored to create conductive hydrogels, including conductive fillers, conductive polymers, and ionic approaches. This review thoroughly explores diverse approaches for developing advanced conductive hydrogels for resistive‐type hydrogel strain sensors. The focus is particularly on their electrical conductivity and sensing performance indicators, distinguishing them as valuable resources for researchers in the field of strain sensors. First, diverse approaches for achieving electrical conductivity in hydrogels are introduced. The subsequent discussion delves into the multifunctionality of these conductivity approaches for hydrogels. In addition, it also scrutinizes recent applications of strain sensors. Overall, it offers comprehensive updates on the performance indicators such as sensitivity, working range and linearity, response and recovery times, and hysteresis of strain sensors using diverse approaches to conductive hydrogels. This study also includes the latest trends and future perspectives of resistive‐type hydrogel strain sensors.

Fast and green synthesis of iron oxide using low-power laser sintering on reduced graphene oxide sensor for ammonia gas detection

Oxide materials are important for gas sensing applications. Unfortunately, most of the synthesis methods use many toxic chemicals and need long calcination at high temperatures. Thus, developing simple and green synthesis processes, which can be performed fast and at low temperatures, is imperative for both industrial manufacturers and our environment. Here, our fast and green synthesis method based on low-power laser sintering for producing FeOx (with Fe2O3 as the main composition) is presented. Besides, FeOx particles were decorated on reduced graphene oxide (RGO) to form a resistive sensor. Notably, our FeOx material can reduce the response of RGO toward acidic gas (NO2) but it enhances the RGO response toward the basic NH3 gas. Particularly, our hybrid FeOx/RGO sensor can detect NH3 gas at above 2 ppm under dry and humidified conditions (<85 % RH). That effect of FeOx is found due to the formation of alpha-phase Fe2O3 which acts as a p-type semiconductor, which is explored and explained in the discussion section. Finally, our findings not only reveal the potential of our hybrid sensing materials for practical NH3 sensing applications but also confirm the usefulness of laser-sintering to produce catalytic oxide materials environmentally friendly and at low cost.

The Influence of Microstructure on TCR for Inkjet-Printed Resistive Temperature Detectors Fabricated Using AgNO3/Ethylene-Glycol-Based Inks.

This study investigated the influence of microstructure on the performance of Ag inkjet-printed, resistive temperature detectors (RTDs) fabricated using particle-free inks based on a silver nitrate (AgNO3) precursor and ethylene glycol as the ink solvent. Specifically, the temperature coefficient of resistance (TCR) and sensitivity for sensors printed using inks that use monoethylene glycol (mono-EG), diethylene glycol (di-EG), and triethylene glycol (tri-EG) and subjected to a low-pressure argon (Ar) plasma after printing were investigated. Scanning electron microscopy (SEM) confirmed previous findings that microstructure is strongly influenced by the ink solvent, with mono-EG inks producing dense structures, while di- and tri-EG inks produce porous structures, with tri-EG inks yielding the most porous structures. RTD testing revealed that sensors printed using mono-EG ink exhibited the highest TCR (1.7 × 10-3/°C), followed by di-EG ink (8.2 × 10-4/°C) and tri-EG ink (7.2 × 10-4/°C). These findings indicate that porosity exhibits a strong negative influence on TCR. Sensitivity was not strongly influenced by microstructure but rather by the resistance of RTD. The highest sensitivity (0.84 Ω/°C) was observed for an RTD printed using mono-EG ink but not under plasma exposure conditions that yield the highest TCR.

Redox behavior of unsubstituted cobalt phthalocyanine in nanostructured Langmuir-Schaefer films

Although the properties of unsubstituted metal tetrapyrroles in solutions have received attention in numerous work, the physico-chemistry of their thin films remains poorly investigated. In this work, we constructed thin films of unsubstituted cobalt phthalocyanine on the surface of quartz plates and indium tin oxide (ITO) electrodes using the Langmuir-Schaefer (LS) and drop casting techniques. In LS films, CoPc species are involved in strong intermolecular interactions, which result in formation of CoPc nanostructures and prominent changes in the ultraviolet-visible spectrum. Electrochemical reduction of CoPc in LS films at −0.9 V (versus silver chloride electrode) results in formation of Co(I) species. In contrast to the highly reactive nature of Co(I)Pc in solution, these species generated in thin films are relatively resistant to dioxygen due to the dense packing of CoPc units. These findings can further the development of CoPc-based resistive sensors utilizing not only common Co(II), but also Co(I) species, which may possess different sensitivity toward analytes.

Bipedal robot center of pressure feedback simulation for center of mass learning

This research aims to create a walking bipedal robot with center of pressure feedback simulation for the center of mass learning, describe its feasibility for learning, describe students' motivation to learn, and describe students' science literacy after using it. The research method used ADDIE (analysis, design, development, implementation, and evaluation). The research data was obtained using a motivation scale questionnaire, science literacy scale, and feasibility scale. The research sample was 48 people; after the research obtained, the simulation of bipedal robot pressure center feedback for center of mass learning can be implemented with the principle of the robot's center of mass detected on the sole of the robot's foot equipped with a force sensitive resistor (FSR) sensor, the position of the center of mass is visible on the monitor screen as a center of mass learning, so that it can motivate students to learn and improve students' science literacy. This can be seen from the feasibility scale score, motivation scale, and science literacy scale of 4.133, 4.072, and 4.067 (scale 1 to 5), respectively, in the "good" category.

Microcontroller Based Automatic Street Light Control by Using LDR &amp; IR Sensors

This project aims at designing and executing the advanced development in embedded systems for energy saving of street lights. Nowadays, human has become too busy, and is unable to find time even to switch the lights wherever not necessary. The present system is like, the street lights which will be switched on in the evening before the sun sets and they are switched off the next day morning after there is sufficient light on the roads. This paper gives the best solution for electrical power wastage. Also the manual operation of the lighting system is completely eliminated. In this the two sensors are used which are Light Dependent Resistor(LDR) sensor to indicate a day/night time and theIRsensors to detect the movement on the street. The microcontroller PIC16F877A is used as brain to control the street light system, where the programming language used for developing the software to the microcontroller is C-language. Finally, the system has been successfully designed and implemented as prototype system. Key Words: Embeded systems, Microcontroller, LDR ,IR, Lighting

Computer-Vision- and Deep-Learning-Based Determination of Flow Regimes, Void Fraction, and Resistance Sensor Data in Microchannel Flow Boiling.

The aim of this article is to introduce a novel approach to identifying flow regimes and void fractions in microchannel flow boiling, which is based on binary image segmentation using digital image processing and deep learning. The proposed image processing pipeline uses adaptive thresholding, blurring, gamma correction, contour detection, and histogram comparison to separate vapor from liquid areas, while the deep learning method uses a customized version of a convolutional neural network (CNN) called U-net to extract meaningful features from video frames. Both approaches enabled the automatic detection of flow boiling conditions, such as bubbly, slug, and annular flow, as well as automatic void fraction calculation. Especially CNN demonstrated its ability to deliver fast and dependable results, presenting an appealing substitute to manual feature extraction. The U-net-based CNN was able to segment flow boiling images with a Dice score of 99.1% and classify the above flow regimes with an overall classification accuracy of 91%. In addition, the neural network was able to predict resistance sensor readings from image data and assign them to a flow state with a mean squared error (MSE) < 10-6.

One-pot synthesis of AC/ZnO nanocomposites for highly sensitive, repeatable and fast response humidity sensor

A highly sensitive and repeatable humidity sensor that can measure relative humidity at room temperature (25 °C) was fabricated using activated carbon-ZnO (AC/ZnO) nanocomposites. The AC/ZnO nanocomposites with high oxygen vacancies were prepared by a facile one-pot synthesis method through carbonization of ZnCl2-impregnated biomass precursor. The sensor exhibited a better response (96 % at 0.5 V biasing), short response time (17.4 s), and recovery time (32.1 s). The same 96 % relative response at 0.5V biasing for all four consecutive cycles was observed without losing the response-recovery time, indicating excellent repeatability. On the other hand, biomass-derived single-phase activated carbon (AC) did not respond to humidity change at room temperature. The improved humidity sensing in AC/ZnO nanocomposites was ascribed to the synergistic effects of increased oxygen vacancies, increased active sites in the AC phase, and the formation of p–n heterojunction at the composite interface, substantiated with X-ray diffraction (XRD), Raman and X-ray photoelectron spectroscopy (XPS) studies. The sensing mechanism of AC/ZnO nanocomposite was discussed based on the enhanced reduction of H2O by adsorbed O2− at AC/ZnO heterojunction, leading to increasing sensor resistance.

Room Temperature Detection of H2S by Two Dimensional WS2 based Chemiresistive Sensors

H2S is a toxic gas which play important roles in different applications. This work reports detection of H2S by tungsten disulphide (WS2) nanoflakes which was synthesized using a simple liquid exfoliation method. The thickness of the nanoflakes were found to range from 0.7–2 nm while the lateral dimensions varied between 2–4 µm when observed using atomic force microscopy. The band gap of WS2 nanoflakes was found to be 2.66 eV when studied using UV-Visible spectroscopy. The total flowrate of gas was found to modulate the sensing performance of WS2. Hence, the WS2 based resistive sensor was tested with 1–27 ppm of H2S at 100 sccm flowrate and with 0.7–5 ppm H2S at 1000 sccm flowrate at room temperature (23–25°C). WS2 nanoflakes exhibited 0.86–15.36% response for 1–27 ppm H2S at 100 sccm while the response was observed to increase and vary between 0.47–15.66% for 0.7–5 ppm at 1000 sccm. The recovery of the sensors at 1000 sccm was found to be incomplete but the sensor demonstrated excellent repeatability and selectivity towards H2S. The limit of detection of the sensor was found as 253 and 53 ppb at 100 and 1000 sccm, respectively.