Sensor Performance Research Articles

Highly Sensitive and Selective SnO2-Gr Sensor Photoactivated for Detection of Low NO2 Concentrations at Room Temperature

Chemical nanosensors based on nanoparticles of tin dioxide and graphene-decorated tin dioxide were developed and characterized to detect low NO2 concentrations. Sensitive layers were prepared by the drop casting method. SEM/EDX analyses have been used to investigate the surface morphology and the elemental composition of the sensors. Photoactivation of the sensors allowed for detecting ultra-low NO2 concentrations (100 ppb) at room temperature. The sensors showed very good sensitivity and selectivity to NO2 with low cross-responses to the other pollutant gases tested (CO and CH4). The effect of humidity and the presence of graphene on sensor response were studied. Comparative studies revealed that graphene incorporation improved sensor performance. Detections in complex atmosphere (CO + NO2 or CH4 + NO2, in humid air) confirmed the high selectivity of the graphene sensor in near-real conditions. Thus, the responses were of 600%, 657% and 540% to NO2 (0.5 ppm), NO2 (0.5 ppm) + CO (5 ppm) and NO2 (0.5 ppm) + CH4 (10 ppm), respectively. In addition, the detection mechanisms were discussed and the possible redox equations that can change the sensor conductance were also considered.

Tailoring the performance of the multidimensional electrostatically formed nanowire gas sensor

Abstract Multi-gate field effect transistors (FETs) based on silicon-on-insulator (SOI) have been popular for several decades due to their improved electrostatic control of the channel current between the source and the drain. Chemical sensors based on such multi-gate FET platform can leverage this improved electrostatic control to detect gases at very low concentration with ultrahigh sensitivity. Electrostatically formed nanowire (EFN) is a multiple-gate FET device which has proven to be an excellent platform for detecting volatile organic compounds and gases. In case of such multi-gate FET sensors, it is imperative to rigorously understand the influence of each gate in controlling the sensing performances. Using palladium nanoparticles decorated EFN (Pd-EFN) as an example, the current work presents a detailed methodology for determining the operating parameters for maximal sensing performances of the Pd-EFN sensor towards hydrogen sensing. We observed that a single operating point does not yield best results with regard to sensor response, dynamic range, and power efficiency. By optimising the operating points (by varying the different gate biases), a sensor response of 107 % was reached even at low concentrations of hydrogen (500 ppm) which is significantly lower than the lower explosive limit of 4% and a tunable dynamic range over three decades (4-8000 ppm) was obtained. Also, the sensor response was not compromised at low driving voltages (100 mV) thus contributing to low power consumption of the sensor. Such a correlation between the working point of the transistor and the various sensor performance metrics (maximum sensor response, dynamic range etc ) has not been studied before to the best of our knowledge and this study can be extended to EFN for other gases and any other multi-gate FET sensors (not limited to Si based sensors).This study can pave the way for effective design of future multi-gate transistors for gas sensing.

Efficacy of carbon nanotube bucky paper for interlaminar damage sensing in composites and fiber‐metal laminates

AbstractDelamination between adjacent laminas and debonding at the fiber/metal interface are common failure modes in composites and fiber‐metal laminates (FMLs). During the initial stages, these flaws are difficult to detect because of their small size and limited applicability of existing damage‐sensing techniques due to the associated heterogeneity and anisotropy with composites and FMLs. Here, we demonstrate a feasible interlaminar damage‐sensing strategy in composites and FMLs using carbon nanotube (CNT) bucky paper (BP). The microstructure dependence of BP electrical conductivity allows it to sense the damage under varying loading conditions. The accuracy of strain sensing with BP is similar to the commercial 120 Ω strain gauge. The BP sensor accurately detects the interlaminar damage in composites (glass fiber/epoxy, carbon fiber/epoxy) and glass fiber–Al (GLARE) laminates. The delamination is marked by a steep rise in the electrical resistance of BP‐modified coupons. Moreover, the sensing capability of the BP sensor is found to be independent of the electrical conductivity of the tested coupons. Thus, the present approach is applicable to sense the interlaminar damage in a much wider class of structural composites and FMLs.Highlights Timely and accurate damage detection in composites is important to ensure their structural integrity. Electrical resistance of bucky paper (BP) varies with deformation. Performance of the BP sensor is comparable to the strain gauge at small strains. BP accurately captures interlaminar damage in composites and FMLs. BP is suitable for damage sensing in a wider class of layered materials.

Amplitude stability research and experimental investigation of the actuation circuit of the inertial sensor for space gravitational wave detection

Abstract The primary measure of scientific performance for inertial sensors used in space gravitational wave detection is the residual acceleration noise of the test mass (TM). This residual noise arises from both the internal circuit and the external environment. The actuation circuit, a crucial component of the internal circuit, significantly affects the TM’s residual acceleration noise through its amplitude stability, thereby impacting the scientific performance of the inertial sensor. In this study, we designed the actuation circuit for an inertial sensor, developed a mathematical model to describe its amplitude stability, and experimentally verified the model's accuracy. Experimental results demonstrate that the current design enables the actuation circuit to achieve an amplitude stability of 3.6 ppm/Hz1/2 at 1 mHz, thereby offering theoretical support for achieving a higher amplitude stability in the millihertz frequency band.

Cybersecurity Challenges in UAV Systems: IEMI Attacks Targeting Inertial Measurement Units

The rapid expansion in unmanned aerial vehicles (UAVs) across various sectors, such as surveillance, agriculture, disaster management, and infrastructure inspection, highlights the growing need for robust navigation systems. However, this growth also exposes critical vulnerabilities, particularly in UAV package delivery operations, where intentional electromagnetic interference (IEMI) poses significant security and safety threats. This paper addresses IEMI attacks targeting inertial measurement units (IMUs) in UAVs, focusing on their susceptibility to medium-power electromagnetic interference. Our approach combines a comprehensive literature review and QuickField simulation with experimental validation using a commercially available 6-degree-of-freedom (DOF) IMU sensor. We propose a hardware-based electromagnetic shielding solution using mu-metal to mitigate IEMI’s impact on sensor performance. The study combines experimental testing with simulations to evaluate the shielding effectiveness under controlled conditions. The results of the measurements showed that medium-power IEMI significantly distorted IMU sensor readings, but our proposed shielding method effectively reduces the impact, improving sensor reliability. We demonstrate the mechanisms by which medium-power IEMI disrupts sensor operation, offering insights for future research directions. These findings also highlight the importance of integrating hardware-based shielding solutions to safeguard UAV systems against electromagnetic threats.

Recent Progress in Surface Acoustic Wave Sensors Based on Low-Dimensional Materials and Their Applications

Benefitting from high sensitivity, rapid response, and cost-effectiveness, surface acoustic wave (SAW) sensors have found extensive applications across various fields, including biomedical diagnostics, environmental monitoring, and industrial automation. Recently, low-dimensional materials have shown great potential in enhancing the performance of SAW sensors due to their exceptional physical, optical, and electronic properties. This review explores recent advancements in the fundamental mechanisms, design, fabrication and applications of SAW sensors based on low-dimensional materials. Specifically, the utilization of low-dimensional materials, including zero-, one- and two-dimensional materials, as sensing materials in SAW sensors are summarized. Their applications in SAW-based gas sensing, ultraviolet light sensing, humidity sensing, as well as biosensing are discussed. Furthermore, major challenges and future perspectives regarding employing low-dimensional materials to enhance SAW sensors are highlighted, providing valuable insights for future research and development in this field.

Label-Free Surface-Enhanced Raman Spectroscopy Detection of Amyloid Beta on Silver Nanostructured Substrates for Alzheimer's Diagnosis.

Alzheimer's disease (AD) is a public health concern, for which an early diagnosis is essential. Biomass-derived carbon fibres with surface-decorated silver nanoparticles (Ag@CFs) have been utilized as surface enhanced raman spectroscopy (SERS) sensor platformfor the detection of the amyloid beta Aβ (25-35) for the pre-diagnosis of Alzheimer's disease. Structural and morphological characterizations confirmed the distribution of plasmonic silver nanoparticles over the surface of carbon fibres. The SERS sensor performance of Ag@CFs was evaluated using rhodamine 6G, which showed an enhancement of the order of 106 which proved the effectiveness of the developed SERS sensor towards trace level detection of analyte. A range of amyloid beta concentrations, from 100uM to 10pM, have been analyzed as a proof-of concept for this study, showcasing the efficacy of the Ag@CFs based SERS sensor to detect trace level concentrations of amyloid beta, even as low as 10 pM. This investigation is a promising development in the field of AD diagnostics since it may turn out to be a non-invasive, economical and early diagnostic tool.

Performance Testing and Analysis of Automotive-Grade CMOS Sensors in Different Environments

Automotive-grade Complementary Metal-Oxide-Semiconductor (CMOS) sensors play a crucial role in automotive electronic systems, especially in the context of the rapid development of Advanced Driver Assistance Systems (ADAS) and autonomous driving technologies. Their performance is directly related to the safety and reliability of vehicles. However, automobiles will face a variety of complex environmental conditions during the actual operation, such as high temperature, low temperature, vibration, humidity changes, and light changes, which may have an impact on the performance of CMOS sensors. Therefore, it is of great significance to study the performance of automotive-grade CMOS sensors in different environments.

Electrophilic-Attack Doped Organic Field-Effect Transistors for Ultrasensitive and Selective Hydrogen Sulfide Detection.

Doping of organic semiconductors (OSCs) has been developed as an effective means of modulating the density and transfer efficiency of charge carriers; however, realization of effective doping to tailor the chemical sensing performance of OSC-based sensors still remains not explored extensively. In addition, the application of OSCs in chemical sensors is usually limited by the poor stability and low selectivity. Herein, flexible donor-acceptor copolymer-based organic field-effect transistor (OFET) chemical sensors are designed via an electrophilic attack doping strategy. The p-dopant trityl tetrakis(pentafluorophenyl) borate (TrTPFB) can be effectively doped into the host molecular N-alkyl-diketopyrrolo-pyrroledithienylthieno[3,2-b]thiophene (DPPDTT). It is simple to alter the doping efficiency and film thickness (ca. 5.5-17.7 nm) by adjusting the proportion and concentration of guest-host molecules, which endows facile carrier mobility and enhanced sensing sensitivity modulation toward reducing gases at room temperature. Particularly, 1.0 mol% TrTPFB-doped DPPDTT achieved the highest response to H2S gas, including ultralow detection concentration (0.5 ppb), excellent selectivity, high humidity stability, and long-term storage stability. This work can provide a new strategy for the potential applications of the organic electronic sensing devices.

PM2.5 IoT Sensor Calibration and Implementation Issues Including Machine Learning

Affordable IoT PM2.5 sensors, enabled by the Internet of Things, offer new ways to monitor air quality. However, concerns exist about their data accuracy. This study aimed (1) to investigate the low-cost PM sensor's performance under various outdoor ambient circumstances and (2) to evaluate seven calibration methods, which include decision trees, gradient-boosted trees, linear regression, nearest neighbors, neural networks, random forests, and the Gaussian Process. The Davis AirLink was used as a reference to compare the Plantower PMS3003 sensor's performance. The data from the Plantower PMS3003 sensor were then compared to the Davis AirLink values using calibration curves created by machine learning algorithms. Calibration curves were generated using machine learning algorithms trained on sensor measurements collected in two Thai cities (Nakhon Si Thammarat and Phuket). Our results show that all machine learning methods outperformed traditional linear regression, with decision trees and neural networks demonstrating the most significant improvement. This research highlights the need for sensor calibration and the limitations of current calibration methods and paves the way for advancements in cloud-based calibration and machine learning for improved data accuracy in IoT PM2.5sensor technology. Doi: 10.28991/ESJ-2024-08-06-08 Full Text: PDF

Comprehensive overview of detection mechanisms for toxic gases based on surface acoustic wave technology

Identification and detection of toxic/explosive environmental gases are of paramount importance to various sectors such as oil/gas industries, defense, industrial processing, and civilian security. Surface acoustic wave (SAW)-based gas sensors have recently gained significant attention, owing to their desirable sensitivity, fast response/recovery time, wireless capabilities, and reliability. For detecting various types of targeted gases, SAW sensors with different device structures and sensitive materials have been developed with diversified working mechanisms. This paper is focused on overviewing recent advances in working mechanisms and theories of dominant sensitive materials and key mechanisms/principles for targeting various gases in the realm of SAW gas sensors. The basic sensing theories and parameters of SAW gas sensors are briefly discussed, and then the major influencing factors are systematically reviewed, including the effects of various sensitive layer materials, temperature/humidity, and UV illumination on the overall performance of SAW gas sensors. We further highlight the relationships and adsorption/desorption principles between sensing materials and key targeted gases, including NH3, NO2, H2S, explosive gases of H2, and 2,4,6-trinitrotoluene, and organic gases of isopropanol, ethanol, and acetone, as well as others gases of CO, SO2, and HCl. Finally, we discuss key challenges and future outlooks in designing methodologies of sensing materials and enhancing the performance of SAW gas sensors, offering fundamental guidance for developing SAW gas sensors with good sensing performance.

Fe/Pt Dual-Atom Catalyst-Enabled Wearable Microfluidic Patch for Superior Uric Acid Detection in Sweat

Wearable sweat sensors offer a promising non-invasive approach for real-time physiological monitoring, with significant potential in personalized medicine. In this study, we present an innovative wearable patch designed for highly sensitive and accurate detection of uric acid (UA) in human sweat. The sensor integrates superior platinum-iron dual-atom catalysts (Pt/Fe DACs), developed based on iron single-atom catalysts (Fe SACs), to achieve selective and precise UA detection across a wide concentration range (6.25–1500 μM). To enhance the sensor's performance, a pH electrode based on polyaniline (PANI) is incorporated for reliable pH calibration. Density functional theory (DFT) calculations are used to explore the catalytic mechanism of UA detection and the synergistic interaction between Fe and Pt atoms in the catalyst, which improves sensor sensitivity. Additionally, we developed a microfluidic patch made of polydimethylsiloxane (PDMS) with enhanced hydrophilicity to facilitate efficient sweat collection. This work presents a valuable approach for advancing wearable sweat sensors for UA detection and offers a promising strategy for the application of wearable sensors in personalized health monitoring.

Self-driven photoelectrochemical sensor based on Z-type perovskite heterojunction for profenofos detection in milk and cabbage

With growing public concern about the accumulation of pesticide residues in food and their potential impacts on health and the environment, the demand for advanced detection technologies has become more critical. To address this need, we have developed an innovative self-driven photoelectrochemical (PEC) sensor, utilizing a Z-type heterojunction structure. This sensor exploited the synergistic interactions between the carbonyl groups in polyvinylpyrrolidone and the Pb2+ ions in the perovskite, and was further enhanced by the strong hydrogen bonds formed between polyethylene glycol and MA+. To optimize the sensor's performance, we incorporated perylene tetracarboxylic acid, which enhanced interface adhesion and promotes charge separation, elements crucial for the visible light-driven PEC process. This strategic design resulted in a sensor with high sensitivity and stability, which was capable of detecting profenofos residues in complex food matrices such as milk and cabbage. The sensor demonstrated a linear detection range from 0.1 to 107ngL-1 and an exceptional detection limit of 0.033ngL-1. When applied to real samples, the sensor achieved recovery rates ranging from 96.68% to 107.2%, with relative standard deviations between 1.08% and 4.54%. In light of these results, the sensor not only showed high efficiency in real-time monitoring of pesticide contamination, but also highlights its significant potential for broader application in the field of food safety.

Highly sensitivity and wide-range flexible humidity sensor based on LiCl/cellulose nanofiber membrane by one-step electrospinning

Cellulose is considered an ideal material for flexible electronic humidity sensors due to its green renewable nature, rich surface groups and strong hydrophilicity. However, the traditional cellulose-based humidity sensor cannot simultaneously have wide detection range, high sensitivity and fast response time, which seriously hinder their development and application. Here, an ultrathin Lithium chloride (LiCl)/cellulose nanofiber membrane as moisture sensitive materials was prepared by one-step electrospinning and used to assemble a high-performance humidity sensor. N, N-Dimethylacetamide/Lithium chloride (DMAc/LiCl) solvent system was used to dissolve the cellulose, and DMAc volatilized into the air while LiCl remained in the cellulose nanofibers during the electrospinning process. LiCl as a highly moisture-sensitive inorganic salt has strong hydrophilicity and enhances the moisture sensing detection limit of cellulose fiber (especially under low humidity conditions), thus giving the cellulose moisture sensor excellent sensitivity (up to 4191 %) and a wide detection range (5–98 % RH). The nanometer size of cellulose fibers, the extremely low thickness and large pores of cellulose membranes accelerate the mass exchange of moisture between the air and the membrane, thus giving cellulose humidity sensor a fast response/recovery time (99/110 s) and low hysteresis (2.9 %). Moreover, the performances of humidity sensors didn’t degrade even after being subjected to long-term application (>30 days), high/low temperatures (73.6/0.1 ℃) and thousands of bending cycles. The proposed LiCl/cellulose nanofiber humidity sensor brings innovative solutions for the design of cellulose based sensor with great potential for use in non-contact humidity detection, respiratory monitoring and sleep apnea detection applications.

Enhanced sensing performance of piezoelectric pressure sensors via integrated nanofillers incorporated with P(VDF-TrFE) composites

Enhanced sensing performance of piezoelectric pressure sensors via integrated nanofillers incorporated with P(VDF-TrFE) composites

Piezotronic Strain Sensor with Uniform and Switchable Sensitivity by Conductivity Transformation

Piezotronic strain sensors that convert mechanical deformation into electrical signals are becoming increasingly important in artificial intelligence, human-machine interfaces, and robotic technologies. These applications require piezotronic sensor with the integration of high-sensitivity, high-stability, and versatile-functionality, which are limited by the single conductivity mechanism. In this study, we propose a piezotronic strain sensor with uniform and switchable sensitivity in a short channel field effect junction.The strain-induced piezo-potential can be used to switch the conductivity between Schottky and Ohmic regime, leading to an exponential (linear) piezotronic modulation in Schottky (Ohmic) conductivity elucidated by Fermi occupation theory. Local gauge factor reaches a high value of 1330 in Schottky conductivity and a low value of 320 in Ohmic regime, yielding a higher ratio of 4.2. The stable conductivity makes these high and low sensitivity uniform over a wide strain range. This study gives a deep insight into the correlation of strain-sensing performance and conductive mechanism in piezotronic sensors, and offers a new avenue to develop multifunctional and high-sensitivity sensors.

A comparative analysis of PlanetScope 4-band and 8-band imageries for land use land cover classification

Earth-observing satellites have become essential in comprehending human impacts on the landscape. Satellite-based imagery is indispensable for mapping Earth's features, managing resources, and studying environmental changes. Readily available remote sensing data with improved radiometric, spectral, spatial, and temporal resolution presents opportunities for advanced data analysis. Precise and accurate land use land cover (LULC) information is essential for the surveillance of environmental conditions and the effective management of natural resources. This research assesses the performance of PlanetScope product SuperDove sensor (PSB.SD), having two different band combinations, including 4-band (Red, Blue, Green and Near-Infrared (NIR)) and 8-band (Blue, Green II, Red, NIR, Coastal Blue, Green, Yellow, Red-Edge) imagery in ArcGIS Pro for the month of July 2021. Four different supervised classifiers, including support vector machine (SVM), k-nearest neighbours (KNN), random forest (RF), and maximum likelihood (ML) classifiers. This study was carried out for the three major areas, i.e., City of Summerside, City of Charlottetown, and Town of Three Rivers in Prince Edward Island (PEI), Canada and LULC classification scheme consist of six major classes which includes Agriculture, Forest, Vegetation, Bare Land, Urban and Water bodies. For accuracy assessment, overall accuracy as well as kappa coefficient were estimated to identify the most accurate combination of LULC classifier and different band combination imagery from PlanetScope. Results show that the highest overall accuracy of 0.94 for Town of Three Rivers and 0.93 for City of Summerside and City of Charlottetown were observed using 8-band imagery with SVM classifier. The lowest overall accuracy of 0.78 for Town of Three Rivers, 0.83 for City of Charlottetown, and 0.82 for City of Summerside was observed using 4-band imagery using ML classifier. Further, the SVM classifier performs well in accuracy with 8-band imagery of PlanetScope, showcasing its potential in LULC classification compared to previous PlanetScope 4-band imagery.

Efficient capture and dual-model fluorescence detection of ReO4− using a TPE-based pyridinium network

99Technetium is one of the highly radioactive contaminants in nuclear wastewater. ReO4− is an ideal substitute for the safe handling of 99TcO4−, its efficient capture and detection are also significant for scarce rhenium resource recovery. However, designing novel materials for the efficient capture and detection of ReO4− and 99TcO4− is still very challenging. Herein, we establish dual-model fluorescence detection for ReO4− via quick ion exchange by a TPE-based cationic polymeric network (TPDC). TPDC displays good sensor performance for ReO4− accompanying the fluorescence enhancement as well as I− accompanying the fluorescence quenching (on-off). Interestingly, the quenched fluorescence can be reopened by ReO4− (off-on) with a slight increase in the detection limit. Moreover, the adsorption capacity for ReO4− reaches 804.2 mg g−1 and the adsorption equilibrium is achieved within 2 min with a kd value of 6.225×105 mL g−1. This work provides a multifunctional material that manifests efficient capture and dual-model fluorescence detection for ReO4− via quick ion exchange accompanying separate or continuous fluorescence change.

Development of a reagent-free electrochemical disposable sensor strip for the quantification of sweat chloride

In this work, we developed a reagent-free disposable sweat chloride sensor using silver platinum reduced graphene oxide composite. The sensing mechanism involves the silver chloride formation from electro-oxidized silver in the presence of chloride ions. The combination of platinum with silver and reduced graphene oxide resulted in an approximately sixfold increase in sensitivity, significantly enhancing the sensor performance. The developed disposable strip exhibited an excellent sensitivity of 6.592 ± 0.007 and 17.420 ± 0.011 mA (log mM)−1 cm−2 for 5–40 mM and 40–200 mM chloride concentration ranges, respectively with a detection limit of 12 µM. The developed sensor exhibited exceptional selectivity against interfering molecules in sweat samples. The practical applicability of the developed sensor was evaluated in sweat samples, revealing an outstanding sensitivity of 0.901 ± 0.007 mA (log mM)−1 cm−2 for detecting sweat chloride ions. The estimated chloride concentrations are in good agreement with the clinically tested values with a relative error of less than 1.5%. The performance of the fabricated Ag-Pt/rGO/SPE sensor opens advanced point-of-care testing opportunities through non-invasive, and convenient analysis.

Enhanced adsorption and sensing properties of Ptn(n=1,3)-doped SnS2 monolayers for warfare toxic gases: A DFT Study

Toxic gases have been extensively employed in warfare, spanning from the deployment of irritant tear gas to fatal nerve gas. These chemical armaments have caused significant damage and casualties in times of conflict. This study explores the adsorption properties and sensing mechanisms of three chemical warfare agents(chlorine Cl2, phosgene COCl2, chloropicrin CCl3NO2) on both pristine SnS2 and doped SnS2 using density functional theory (DFT). The results highlight the superior adsorption energy, charge transfer efficiency, and band structure of doped Ptn/SnS2 (n=1,3) compared to pristine SnS2. Pt/SnS2 exhibits exceptional adsorption capabilities for all three war gases. Under specific conditions, Pt3/SnS2(cluster) emerges as an ideal material for detecting Cl2 and CCl3NO2, while also serving as an effective adsorbent for COCl2 gas. This research serves as a valuable reference for sensor performance studies grounded in first-principles theory, showcasing the potential of doped Pt3/SnS2 as a viable sensor for warfare gases. The insights from this study contribute to the development of advanced poison gas sensors, facilitating improvements in poison gas detection and prevention capabilities.