Sensor Response Research Articles

Pyridinium surfactants can modulate the UV response of methyl green dye‐doped polymeric films for sensor development

AbstractConcerns about ultraviolet radiation (UV‐R) are increasing due to climate change. The effects of UV‐R on living organisms depend on exposure duration and intensity, making it crucial to monitor UV‐R levels. In this study, we investigated the role of surfactants as photosensitizing or stabilizing agents in a colorimetric sensor based on a polymeric film doped with methyl green dye. The films were synthesized using the casting technique from a filmogenic solution containing either carboxylated polyvinyl alcohol (P1) or hydroxylated polyvinyl alcohol (P2). We evaluated the impact of surfactant structure (dodecylpyridinium chloride, C12; hexadecylpyridinium chloride, C16; or sodium dodecyl sulfate, SDS) and concentration on the colorimetric response of the films to UV‐R. Color changes were monitored using the CIELab system. The results showed that, under UV‐R exposure for up to 5 h, films without surfactants did not lose their blue hue (negative b* parameter in CIELab). While SDS stabilized the dye in the polymeric matrix, films containing pyridinium surfactants (C12 or C16) changed color from blue to pale yellow (positive b* parameter in CIELab). The color change, which was modulated by surfactant concentration, was attributed to radical reactions involving the pyridinium surfactants, influenced by their hydrophobicity and the polymer structure. These results represent a significant advancement in colorimetric sensors for UV‐R, as they allow for the modulation of the sensor's response time through surfactant concentration in the polymeric film.

BiOI: Self‐Powered Humidity Sensor and Breath Monitor

AbstractThe self‐powered, adaptable, quick, and precise humidity sensors are required to measure humidity in the microenvironment. Here, a simple one‐step wet chemical method is presented to prepare stacked layered bismuth oxyiodide (BiOI) nanoplates. The remarkable response and self‐powered behavior of BiOI nanoplates‐based humidity sensors are demonstrated for the first time. The humidity sensing characteristics of BiOI are examined within the range of 30–80% relative humidity (RH) at an operating temperature of 323 K. The highest sensitivity level of 39 kΩ per %RH is determined for a relative humidity of 75.5%. A successful attempt is made to use this sensor as a human breathing process monitor. The sensor response times at room temperature in this test are 3.04(9) s at 0 V and 3.31(7) s at 20 V, whereas the recovery times are estimated to be 6.57(6) s at 0 V and 6.42(4) s at 20 V. Future advancements in sensing devices utilizing BiOI hold the potential to yield highly responsive, ultrafast sensors with simplified device geometries, and self‐powered.

Adaptive Sensor Placement Selection and Response Reconstruction for Bridge Structural Health Monitoring

Abstract Bridge structural health monitoring (SHM) measures the real-time responses of bridges via instrumented sensors. This paper focuses on the bridge displacement reconstruction at unmeasured locations of interest (LoIs) using measurements from sensors. Increasing the number of sensors on the bridge can capture more structural information. However, considering the complex structure and scale of bridges, minimizing the number of sensors and selecting the critical sensing locations (CSL) to place sensors for measurements are significant in bridge SHM. To achieve efficient bridge SHM under limited number of sensors, this paper proposes an adaptive sensor selection and reconstruction neural network (ASSRNN) for determining the CSLs and reconstructing at all LoIs. In particular, a novel adaptive location selection mechanism (ALSM) as well as a unified network model and its learning process is proposed. In the experiments, the proposed model is validated based on a real-world case study of a long-span bridge in California, USA. Through the comparison of different sensor placement schemes. The experimental results show that the proposed model is effective and accurate in reconstructing the structural displacement responses via adaptive sensor selection for bridge SHM.

CuO Nanowire Humidity Sensors Enhanced by Au Nanostructure Prepared on TSV Substrate

Abstract The authors studied the growth of cupric oxide nanowires (CuO NWs) on through-silicon via (TSV) substrates and the subsequent fabrication of humidity sensors utilizing these CuO NWs. We also explored the effect of annealing gold nanoparticles (Au NPs) deposited on CuO NWs through sputtering with varying thicknesses of the Au film (1–5 nm) to enhance the humidity response. The morphology and crystallography of both CuO NWs and Au NPs were characterized using multi-function environmental field-emission scanning electron microscopy and X-ray diffraction techniques. The results indicate that the resistance of the CuO NWs increased with increasing relative humidity, attributed to the p-type conductivity of CuO. Adding the Au NPs further augmented the humidity response. Interestingly, an increase in the initial thickness of the Au film led to a proportional increase in the average diameter of the annealed Au NPs, ranging from 11 to 31 nm for Au film thicknesses of 1–5 nm. Notably, the sample with 11 nm Au NP/CuO NWs exhibited superior sensor response compared to the samples of other thicknesses.

Photocatalytic Organic Semiconductor-Bacteria Imprinted Polymers for Highly Selective Determination of Staphylococcus aureus at the Single-Cell Level.

This work utilized a combination of photocatalytic organic semiconductors and bacteria to create a photocatalytic organic semiconductor-bacterial biomixture system based on a bacteria imprinted polymers (OBBIPs-PEC) sensor, for the detection of S. aureus with high sensitivity in "turn-on" mode at the single-cell level. This outstanding sensor arises from an integration of two different types of semiconductor materials to form heterojunctions. As well this sensor involves combining a semiconductor material with cationic side chains and an electron transport chain within a natural cellular environment, in which the cationic side chain of poly(fluorene-co-phenylene) organic semiconductor at 2-(4-mesyl-2-nitrobenzoyl)-1,3-cyclohexanedione (PFP-OC@MNC) demonstrated the ability to penetrate the cell membrane of S. aureus and interact with specific binding sites through electrostatic interactions. As the cavities in the BIPs were occupied by S. aureus, during light irradiation, the electrons stimulated by the photoexcitation process in the manufactured PFP-OC@MNC semiconductors were successfully transmitted to S. aureus, where these electrons played a role in the regeneration of NADH and FADH2, and then the presence of S. aureus acted as a proficient electron acceptor for photoexcited electrons; thereby the PEC response of the OBBIPs-PEC sensor was significantly enhanced. Of note, it exhibited high selectivity for S. aureus over other bacteria and maintained excellent performance in complex matrices, distinguishing S. aureus with concentrations as low as 10 CFU/mL. This work dramatically reduces the influence of interference factors in the traditional mode and offers a powerful way for microorganism detection in food and environmental fields.

Sensorized laparoscopic surgical grasper with integrated capacitive force sensor for robot-assisted minimally invasive surgery

Purpose This paper aims to introduce a sensorized surgical grasper with a novel flexible capacitive tactile force sensor integrated within the surgical grasper for minimally invasive surgery (MIS) and robot-assisted MIS (RMIS) procedures. Design/methodology/approach The proposed sensor offers a unique configuration of sensing electrodes with one top excitation electrode and three bottom electrodes enabling the measurement of normal and shear forces without incorporating any complex decoupling algorithms. The design of the sensor is optimized using finite-element method simulations, ensuring efficiency and reliability. Findings Experimental validation, real-time sensor response and application in lump detection through stiffness assessment demonstrate the decoupled force response (0–5 N normal range and 0–2 N shear range) with high sensitivity 0.0124/N, repeatability and hysteresis response with 5.65% and 4.7% errors respectively. Originality/value The compact design of the sensor makes it compliant with surgical graspers and therefore enhances the overall efficiency of robotic surgical procedures. The sensorized surgical grasper is fabricated using conventional machining and rapid prototyping techniques, presenting a cost-effective solution for adoption.

MOX Nanosensors to Detect Colorectal Cancer Relapses from Patient's Blood at Three Years Follow-Up, and Gender Correlation.

Colorectal cancer represents 10% of all the annual tumors diagnosed worldwide, being often not timely diagnosed, because its symptoms are typically lacking or very mild. Therefore, it is crucial to develop and validate innovative low-invasive techniques to detect it before becoming intractable. To this aim, a device equipped with nanostructured gas sensors has been employed to detect the airborne molecules of blood samples collected from healthy subjects, and from colorectal cancer affected patients at different stages of their pre- and post-surgery therapeutic path. Data was scrutinized by using statistical standard techniques to highlight their statistical differences, and through principal component analysis and support vector machine to classify them. The device was able to readily distinguish between the pre-surgery blood samples (i.e., taken when the patient had cancer), and the ones up to three years post-surgery (i.e., following the tumor removal) or the ones from healthy subjects. Finally, the correlation of the sensor responses with the patient/healthy subject's gender was investigated, resulting negligible. These results pave the path toward a clinical validation of this device to monitor the patient's health status by detecting possible relapses, to parallel to clinical follow-up protocols.

QD/SnO2 Photoactivated Chemoresistive Sensor for Selective Detection of Primary Alcohols at Room Temperature

Sensors based on nanocomposites of quantum dots (QDs) and wide-gap metal oxides are of exceptional interest for photoactivated detection of toxic and pollutant gases without thermal heating. However, the class of detecting gases has been limited almost exclusively to oxidizing gases like NO2. Here, we designed a photoactivated sensor for the selective detection of primary alcohols at room temperature using CdSe quantum dots coupled to a wide-gap SnO2 semiconductor matrix. Our concept of the sensor operations is based on the photochemical reaction of primary alcohols via photoactivated QD-SnO2 charge transfer and does not involve chemisorbed oxygen, which is traditional for the operation of metal oxide sensors. We demonstrated an efficient sensor response to C1–C4 primary alcohols of ppm concentration under photoexcitation with a yellow LED in the absence of a signal from other volatile organic compounds (VOCs). We believe that proposed sensor concept opens up new ways to design photoactivated sensors without heating for the detection of VOCs.

Birefringence axis rotation and nonlinear response of side-hole fiber pressure sensor

Birefringence axis rotation and nonlinear response of side-hole fiber pressure sensor

Investigating nonlinear dynamic properties of an inertial sensor with rotational velocity-dependent rigidity

This study investigates the nonlinear dynamics of a system with frequency-dependent stiffness using a MEMS-based capacitive inertial sensor as a case study. The sensor is positioned directly on a rotating component of a machine and consists of a microbeam clamped at both ends by fixed supports with a fixed central proof mass. The nonlinear behavior is determined by electrostatic forces, axial and bending motion coupling, and frequency-dependent stiffness. The numerical Galerkin approach is employed for discretization of the coupled differential equations in spatial coordinates. To obtain the sensor response as a function of frequency, a continuation arc-length method based on weak formulation energy balance method is used. This approach uses a physical gradient descent learning based method to obtain unknown coefficients of the considered response. The presented method computes the periodic steady-state solution of the design by considering different frequency contents within the response, including different harmonics of the response expansion. However, the primary and secondary resonances at various harmonic frequencies within the response can be predicted. In this article, the dynamic behavior of the design is investigated by testing different levels of shaft acceleration and different bias voltages. The purpose of the evaluation is to determine the frequency-dependent stiffness of the accelerometer for different levels of shaft speed, taking into account various sensor geometric parameters. The analysis demonstrates the behavior of the sensors under different conditions, particularly highlighting the nonlinearity of the sensor, which causes primary and secondary resonances in the first, second, and third harmonic responses of the accelerometer, especially at higher bias voltages. Fast Fourier analyses indicated that the transversal vibration amplitude of the structure in the second and third harmonies are so considerable that cannot be neglected. The mathematical method used in this study can be applied by researchers and engineers in the design of such sensors to effectively create these devices.

Oxygen-Dependent Photoluminescence and Electrical Conductance of Zinc Tin Oxide (ZTO): A Modified Stern-Volmer Description.

Zinc tin oxide (ZTO) is investigated as a photoluminescent sensor for oxygen (O2); chemisorbed oxygen quenches the luminescence intensity. At the same time, ZTO is also studied as a resistive sensor; being an n-type semiconductor, its electrical conductance decreases by adsorption of oxygen. Both phenomena can be exploited for quantitative O2 sensing. The respective sensor responses can be described by the same modified Stern-Volmer model that distinguishes between accessible and non-accessible luminescence centers or charge carriers, respectively. The impact of the temperature is studied in the range from room temperature up to 150 °C.

Role of denoisers in simulation-based inference from graph-structured data: a case study for position inference in an astroparticle detector

The focus of this paper is to improve simulation-based inference through improved denoising of experimental data. Statistical inference of parameters governing a complex physical process from observed data is an important task for several scientific domains. In a likelihood-free inference setup, the inference engine used to make inference from the experimental data can be trained using simulated data. In many scenarios, experimental data is corrupted by noise during the data acquisition process, which is either unaccounted for in the simulator or whose strength might not match with the one set in the simulator. This deteriorates the accuracy of the inference. While advances in denoising can be leveraged to address this challenge, many denoising techniques ignore the fact that experimental data in many scientific domains tends to be irregular/graph-structured. This paper addresses these two challenges by developing a kernel-based learnable graph (KBLG) denoiser, which can be used to denoise experimental graph-structured data. In order to exhibit the efficacy of the developed denoiser in simulation-driven inference problems, this paper considers the problem of inference of the position of an interaction in an astroparticle detector. The available simulated data in this case is the snapshots of the luminous responses of the photomultiplier tube sensors used within the dark matter detection experiment. In experimental situations, these measurements are corrupted by noise generated by secondary optical and electronic processes. The proposed KBLG denoiser is used to denoise the multiple snapshots of the experimental measurements, which are then used for position reconstruction using a multilayer perceptron trained using noiseless simulated data. Numerical results exhibit that the proposed KBLG denoiser outperforms a graph-agnostic denoiser in terms of MLP-based position reconstruction performances for different levels of noise (noise variance).

ZnO/MOx Nanofiber Heterostructures: MOx Receptor's Role in Gas Detection.

ZnO/MOx (M = FeIII, CoII,III, NiII, SnIV, InIII, GaIII; [M]/([Zn] + [M]) = 15 mol%) nanofiber heterostructures were obtained by co-electrospinning and characterized by X-ray diffraction, scanning electron microscopy and X-ray fluorescence spectroscopy. The sensor properties of ZnO and ZnO/MOx nanofibers were studied toward reducing gases CO (20 ppm), methanol (20 ppm), acetone (20 ppm), and oxidizing gas NO2 (1 ppm) in dry air. It was demonstrated that the temperature of the maximum sensor response of ZnO/MOx nanofibers toward reducing gases is primarily influenced by the binding energy of chemisorbed oxygen with the surface of the modifier's oxides. When detecting oxidizing gas NO2, high sensitivity at a low measurement temperature can be achieved with a high concentration of free electrons in the near-surface layer of zinc oxide grains, which is determined by the band bending at the ZnO/MOx interface characterized by the difference in the electron work function of ZnO and MOx.

Graphene/TiO2 Heterostructure Integrated with a Micro-Lightplate for Low-Power NO2 Gas Detection.

Low-power gas sensors that can be used in IoT (Internet of Things) systems, consumer devices, and point-of-care devices will enable new applications in environmental monitoring and health protection. We fabricated a monolithic chemiresistive gas sensor by integrating a micro-lightplate with a 2D sensing material composed of single-layer graphene and monolayer-thick TiO2. Applying ultraviolet (380 nm) light with quantum energy above the TiO2 bandgap effectively enhanced the sensor responses. Low (<1 μW optical) power operation of the device was demonstrated by measuring NO2 gas at low concentrations, which is typical in air quality monitoring, with an estimated limit of detection < 0.1 ppb. The gas response amplitudes remained nearly constant over the studied light intensity range (1-150 mW/cm2) owing to the balance between the photoinduced adsorption and desorption processes of the gas molecules. The rates of both processes followed an approximately square-root dependence on light intensity, plausibly because the electron-hole recombination of photoinduced charge carriers is the primary rate-limiting factor. These results pave the way for integrating 2D materials with micro-LED arrays as a feasible path to advanced electronic noses.

Accelerating Plasmonic Hydrogen Sensors for Inert Gas Environments by Transformer-Based Deep Learning.

Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevant conditions. Here, we demonstrate how a tailored long short-term transformer ensemble model for accelerated sensing (LEMAS) speeds up the response of an optical plasmonic hydrogen sensor by up to a factor of 40 and eliminates its intrinsic pressure dependence in an environment emulating the inert gas encapsulation of large-scale hydrogen installations by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware. Moreover, LEMAS provides a measure for the uncertainty of the predictions that are pivotal for safety-critical sensor applications. Our results advertise the use of deep learning for the acceleration of sensor response, also beyond the realm of plasmonic hydrogen detection.

Experimental Validation of Virtual Torque Sensing for Wind Turbine Gearboxes Based on Strain Measurements

ABSTRACTIn efforts to reduce the operation and maintenance cost of wind turbines, there is an increasing interest to monitor key turbine quantities such as the torque load on the gearbox. Monitoring the torque paves the way for the calculation of remaining useful lifetime, leading to cost reductions through improved reliability and maintenance planning. In order to avoid expensive direct torque sensors, this paper investigates the potential of virtual torque sensing, a technique based on 3 basic components: first, a set of non‐intrusive sensors installed on the gearbox. Three groups of strain gauges on the gearbox as well an angular encoder are considered in this paper. Second is a physics‐based model, capable of predicting the response of aforementioned sensors. These models are constructed with a purposeful balance between accuracy and computational cost. Model validation and updating are performed to ensure efficient and accurate prediction of the sensor output. Finally, an augmented extended Kalman filter (AEKF) is used to combine the measured response with predictions from the model to infer the gearbox input torque. Since a key factor determining the performance of the AEKF is the tuning of the AEKF covariance matrices, multiple methods are introduced to systematically tune the covariance matrices. Experimental validation results show that the virtual torque sensor can detect the load torque with a normalized mean absolute error (NMAE) between 3.41% and 7.47%, depending on the sensor set. The influence of the amount of sensors used and the tuning method are also investigated.

Analysis of electrical microstructure and sensor response in cerium oxide fibers-based interdigitated electrode (IDE) devices

Analysis of electrical microstructure and sensor response in cerium oxide fibers-based interdigitated electrode (IDE) devices

Kevlar-Covered Subresonant Pressure Sensor for Flow Measurements

This study presents wall pressure sensors based on wall-flush Kevlar-covered cavities. The sensor dynamic response is characterized using a variety of acoustic and flow experiments, including a flow disturbance quantification performed using wall-parallel particle image velocimetry. Various sensor configurations are studied to understand the relation between cavity shape and Kevlar scrim on the sensor response. Using a localized excitation study, the sensor’s spatial sensitivity is characterized, and a mathematical model to estimate the wall pressure spectrum is presented. It is shown that these sensors have a well-defined, second-order dynamic response without grazing flow. Their performance in flow shows the disturbance to the flow below the measurement uncertainty, indicating no significant flow disturbance. Comparing measured data with the model estimates reveals close agreement, within ±3 dB, and that discrepancy is accounted for by grazing flow effects and sensor spatial sensitivity.

Ultra-sensitive, on-site pesticide detection for environmental and food safety monitoring using flexible cellulose nano fiber/Au nanorod@Ag SERS sensor.

This paper introduces a highly absorbent and sensitive cellulose nanofiber (CNF)/gold nanorod (GNR)@Ag surface-enhanced Raman scattering (SERS) sensor, fabricated using the vacuum filtration method. By optimizing the Ag thickness in the GNR@Ag core-shell structures and integrating them with CNFs, optimal SERS hotspots were identified using the Raman probe molecule 4-aminothiophenol (4-ATP). To concentrate pesticides extracted from fruit and vegetable surfaces, we utilized the evaporation enrichment effect using hydrophilic CNF and hole-punched hydrophobic polydimethylsiloxane (PDMS). This design leverages the hydrophilic substrate and localized evaporation to create a microfluidic flow that concentrates analytes within a small hole area, enhancing SERS sensitivity by up to 465 %. The sensor achieved on-site detection limits for Thiram as low as 10-11 M on fruit surfaces, specifically apples and chili peppers. This approach underscores how localized molecule enrichment can substantially improve field-based pesticide analysis. the sensor's response to interfering substances (e.g., glucose and citric acid) and other harmful molecules (e.g., carbendazim and nitrofurazone was also evaluated, demonstrating high sensitivity and accuracy). The PDMS-assisted CNF/GNR@Ag SERS sensor exhibits flexibility, ease of fabrication, and excellent sensitivity and selectivity, showing significant potential for applications in food safety, agriculture, and environmental monitoring. These advancements are anticipated to promote the practical adoption of SERS-based sensor technology across diverse fields, suggesting broad future utility.

A dual-stream model based on PRNU and quaternion RGB for detecting fake faces.

The forensic examination of AIGC(Artificial Intelligence Generated Content) faces poses a contemporary challenge within the realm of color image forensics. A myriad of artificially generated faces by AIGC encompasses both global and local manipulations. While there has been noteworthy progress in the forensic scrutiny of fake faces, current research primarily focuses on the isolated detection of globally and locally manipulated fake faces, thus lacking a universally effective detection methodology. To address this limitation, we propose a sophisticated forensic model that incorporates a dual-stream framework comprising quaternion RGB and PRNU(Photo Response Non-Uniformity). The PRNU stream extracts the "camera fingerprint" feature by discerning the non-uniform response of the image sensor under varying lighting conditions, thereby encapsulating the overall distribution characteristics of globally manipulated faces. The quaternion RGB stream leverages the inherent nonlinear properties of quaternions and their informative representation capabilities to accurately describe changes in image color, background, and spatial structure, facilitating the meticulous capture of nuanced local distinctions between locally manipulated faces and real faces. Ultimately, we integrate the two streams to establish the exchange of feature information between PRNU and quaternion RGB streams. This strategic integration fully exploits the complementarity between two streams to amalgamate local and global features effectively. Experimental results obtained from diverse datasets underscore the advantages of our method in terms of accuracy, achieving a detection accuracy of 96.81%.