Sensor Structure Research Articles

Fabrication of Concentration Gradient Structures for High-Performance Capacitive Pressure Sensors by Novel Acoustic Resonance Mixing

Fabrication of Concentration Gradient Structures for High-Performance Capacitive Pressure Sensors by Novel Acoustic Resonance Mixing

Multi-Structure-Based Refractive Index Sensor and Its Application in Temperature Sensing

In this paper, a new sensor structure is designed, which consists of a metal–insulator–metal (MIM) waveguide and a circular protrusion and a rectangular triangular cavity (CPRTC). The characterization of nanoscale sensors is considered using an approximate numerical method (finite element method). The simulation results show that the sharp asymmetric resonance generated by the interaction between the discrete narrow-band mode and the continuous wideband mode is called Fano resonance. The performance of the sensor is considerably influenced by CPRTC. The sensor structure has attained a sensitivity of 3060 nm/RIU and a figure of merit (FOM) of 53.68. In addition, the application of this structure to temperature sensors is also investigated; its sensitivity is 1.493 nm/°C. The structure also has potential for other nanosensors.

Integrated vertical force transfer structure for high-performance MEMS-based array piezoresistive tactile sensor

Integrated vertical force transfer structure for high-performance MEMS-based array piezoresistive tactile sensor

A linear variable differential transducer for position measurement with an external armature

This paper presents a novel structure for linear position sensors with external armature. The coils are designed to have a nonoverlapping structure to increase the coils’ design optimization flexibility. The 3D finite element method is utilized for design optimization to extend the linearity range of the position sensor. The excitation and pickup coils are segmented to facilitate and improve the winding process. The number of turns in each pickup coil is optimized using the 3D finite element method to minimize the nonlinearity error and enhance the linearity range of the position sensor. The simulation results are compared with experiments to validate the optimization process of the position sensor.

The decoupling and simulation analysis of the six-force sensors structure for aircraft wheel in ground dynamic testing

The decoupling and simulation analysis of the six-force sensors structure for aircraft wheel in ground dynamic testing

Ranking Single Fluorescent Protein-Based Calcium Biosensor Performance by Molecular Dynamics Simulations.

Genetically encoded fluorescent biosensors (GEFBs) have become indispensable tools for visualizing biological processes in vivo. A typical GEFB is composed of a sensory domain (SD) that undergoes a conformational change upon ligand binding or enzymatic reaction; the SD is genetically fused with a fluorescent protein (FP). The changes in the SD allosterically modulate the chromophore environment whose spectral properties are changed. Single fluorescent (FP)-based biosensors, a subclass of GEFBs, offer a simple experimental setup; they are easy to produce in living cells, structurally stable, and simple to use due to their single-wavelength operation. However, they pose a significant challenge for structure optimization, especially concerning the length and residue content of linkers between the FP and SD, which affect how well the chromophore responds to conformational change in the SD. In this work, we use all-atom molecular dynamics simulations to analyze the dynamic properties of a series of calmodulin-based calcium biosensors, all with different FP-SD interaction interfaces and varying degrees of calcium binding-dependent fluorescence change. Our results indicate that biosensor performance can be predicted based on distribution of water molecules around the chromophore and shifts in hydrogen bond occupancies between the ligand-bound and ligand-free sensor structures.

Optimization of Parameters and Comparison of Detection Signals for Planar Coil Particle Detection Sensors with Different Core Materials.

The cleanliness of lubricating oil plays a key role in determining the operational health of mechanical systems, serving as a critical metric that delineates the extent of equipment wear. In this study, we present a magnetic-core-type planar coil particle detection sensor. The detection accuracy and detection limit are improved by optimizing the magnetic field inside the sensor. The optimization of the magnetic field is achieved through the finite element simulation analysis of the coil and the magnetic core. First, the finite element simulation software COMSOL 6.0 is used to model the sensor in three dimensions (3D). Then, we study the distribution of the magnetic field under different coil radii, core conductivity levels, and other parameters. We obtain the sensor structure after optimizing the magnetic field. The sensor is made using experimental methods, and the iron particles and copper particles are detected. The results show that the lower limit of detection of iron particles can reach 46 μm, and the lower limit of detection of copper particles can reach 110 μm.

Point-Cloud Instance Segmentation for Spinning Laser Sensors.

In this paper, we face the point-cloud segmentation problem for spinning laser sensors from a deep-learning (DL) perspective. Since the sensors natively provide their measurements in a 2D grid, we directly use state-of-the-art models designed for visual information for the segmentation task and then exploit the range information to ensure 3D accuracy. This allows us to effectively address the main challenges of applying DL techniques to point clouds, i.e., lack of structure and increased dimensionality. To the best of our knowledge, this is the first work that faces the 3D segmentation problem from a 2D perspective without explicitly re-projecting 3D point clouds. Moreover, our approach exploits multiple channels available in modern sensors, i.e., range, reflectivity, and ambient illumination. We also introduce a novel data-mining pipeline that enables the annotation of 3D scans without human intervention. Together with this paper, we present a new public dataset with all the data collected for training and evaluating our approach, where point clouds preserve their native sensor structure and where every single measurement contains range, reflectivity, and ambient information, together with its associated 3D point. As experimental results show, our approach achieves state-of-the-art results both in terms of performance and inference time. Additionally, we provide a novel ablation test that analyses the individual and combined contributions of the different channels provided by modern laser sensors.

Design of unexploded ordnance detection sensor based on frequency-domain electromagnetic method

ABSTRACT Detecting underground metal targets (e.g. unexploded ordnance) using simple devices and seeking to improve the detection accuracy and depth is a hot issue in electromagnetic detection in recent years. Electromagnetic detection based on the frequency-domain method has a simple excitation method, a wide excitation range, and high detection resolution and frequency flexibility. However, the primary field generated by the excitation coil and the secondary field generated by the unexploded ordnance exist at the same time, which has a large impact on the effective signal; and the current detection accuracy is not high, and the effective eigenvalues are less. To address these situations, in this paper, we use the analytical solution of the rectangular coil and finite element simulation to design a new type of electromagnetic sensor with differential multi-component reception, and the influence of the primary field can be eliminated with the help of its coupling characteristics. Based on the unique sensor structure, we propose a four-quadrant fast horizontal localisation as well as a method for target identification and a method for depth fixing of the target by SNR eigenvalue fitting. In a laboratory environment with 10 V low-power excitation, the sensor has a farthest detection depth of about 1.2 m and a positioning error of about 14.43 mm within a depth of 0.9 m, and it can be detected as far as 0.6 m in an outdoor environment.

Surface-Functionalizing Strategies for Multiplexed Molecular Biosensing: Developments Powered by Advancements in Nanotechnologies.

Multiplexed biosensing methods for simultaneously detecting multiple biomolecules are important for investigating biological mechanisms associated with physiological processes, developing applications in life sciences, and conducting medical tests. The development of biosensors, especially those advanced biosensors with multiplexing potentials, strongly depends on advancements in nanotechnologies, including the nano-coating of thin films, micro-nano 3D structures, and nanotags for signal generation. Surface functionalization is a critical process for biosensing applications, one which enables the immobilization of biological probes or other structures that assist in the capturing of biomolecules. During this functionalizing process, nanomaterials can either be the objects of surface modification or the materials used to modify other base surfaces. These surface-functionalizing strategies, involving the coordination of sensor structures and materials, as well as the associated modifying methods, are largely determinative in the performance of biosensing applications. This review introduces the current studies on biosensors with multiplexing potentials and focuses specifically on the roles of nanomaterials in the design and functionalization of these biosensors. A detailed description of the paradigms used for method selection has been set forth to assist understanding and accelerate the application of novel nanotechnologies in the development of biosensors.

Design of one-dimensional phononic crystals comprising robust Fano edge modes as a highly sensitive sensor for alcohols

This work introduces various designs of phononic crystals (PnCs), referred to as topological phononic crystals (TPnCs), as novel, stable, and high-performance sensing tools. Meanwhile, we introduce the concept of the topological edge state to address the discrepancies between theoretical predictions and experimental results of PnC sensors. Consequently, the design of a PnC sensor structure that maintains high stability amidst fluctuations in layer manufacturing and deformations during construction represents the mainstay of our study. Notably, the numerical findings demonstrate the stability of the proposed sensor in the presence of various geometric changes. In addition, we assess the effectiveness of several periodic PnC designs in sensing the physical properties of fluids, specifically alcohols like butanol. Accordingly, temperature sensing of butanol is conducted over a wide range (170°C–270°C) by monitoring the displacement of Fano resonance modes. In this regard, the proposed PnC structure demonstrates an impressive sensitivity of 119.23 kHz/°C. Furthermore, our design achieves a high-quality factor and figure of merit of 378.23 and 1.085, respectively, across the temperature range of 170°C–230°C. These outcomes are promising for the development of ultrasensitive thermal sensors. Ultimately, our research provides valuable insights into the creation of highly sensitive and stable temperature sensors suitable for a range of industrial applications.

Probe-type optical fiber sensors for electric field distribution measurement

This paper reports a compact fiber optical electric field (E-field) sensor aiming for the precise detection of transient E-field distributions. Here, a reflective polarization-reciprocal optical path is proposed, which inherently mitigates the temperature-induced birefringence interference of the electro-optical crystal without the need for additional optical elements, thereby facilitating a reduced-size sensing probe. Furthermore, an adaptive particle swarm optimization (A-PSO) algorithm has been utilized for the first time to optimize the insulation structure of the optical E-field sensor, which significantly suppresses field distortion within the sensing region by 50%. This method addresses the technical gap in optical E-field sensor structure optimization, providing an effective means to improve its spatial resolution. The experimental results demonstrate that the proposed sensor exhibits a broad response frequency range from 50 Hz to 15 MHz, with a sensitivity of 0.675 V/kV cm−1. The proposed sensor successfully monitors the spatial distribution of electric fields in the lightning interception region of a lightning rod, thereby validating its effectiveness.

Fabrication and characterization of strain-induced graphene for chemisorption-based graphene resonant mass sensors

Abstract We propose a strain application process and structure for a chemisorption-based resonant mass sensor using graphene, aiming to develop a novel sensing platform with high sensitivity and selectivity. The results demonstrate the successful application of 0.26%–0.30% strain to suspended graphene through the thermal shrinkage of SU-8, resulting in a 25% improvement in resonance characteristics. Furthermore, we demonstrated the mass sensing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spikes chemisorbed on the strain-applied graphene resonant mass sensor, achieving a mass sensitivity 149,000 times higher than that of silicon-based chemisorption mass sensors.

Surface Plasmon Resonance Sensor with 2D Materials for Enhanced Refractive Index Detection of Chemical Pollutants in Seawater

This paper introduces a highly sensitive, graphene-based, multilayer surface plasmon resonance (SPR) refractive index sensor is designed for the detection of chemical pollutants in seawater. The sensor structure consists of multiple layers, specifically BK7 glass, Chromium (Cr), Copper (Cu), MXene, and Graphene. SPR sensors have gained significant attention in the field of real-time chemical sensing due to their label-free detection capabilities, high sensitivity, and excellent reproducibility. In this sensor design, the refractive index (RI) of the sensing region is altered by the interaction of chemical pollutants present in the seawater. These variations in RI directly affect the excitation of surface plasmon polaritons (SPPs) at the multilayer sensor interface. This interaction forms the basis for detecting chemical pollutants, as changes in RI modulate the sensor's optical response, which can be accurately measured. The performance of the proposed sensor is thoroughly evaluated using numerical simulations based on the Transfer Matrix Method (TMM). The simulations are carried out over a refractive index range of 1.329 to 1.433, covering the typical RI variations caused by chemical pollutants in seawater. The sensor demonstrated exceptional performance, achieving a maximum sensitivity of 186deg/RIU at an operational wavelength of 633nm. Additionally, the sensor is exhibited a high detection accuracy (DA) of 1.5deg⁻¹ and a figure of merit (FOM) of 205 RIU⁻¹, highlighting its ability to precisely distinguish small changes in refractive index. These results emphasize the potential of this graphene-based SPR sensor configuration for high-performance detection of chemical pollutants, making it an excellent candidate for environmental monitoring applications. Its robust design, combined with its high sensitivity and accuracy, positions it as a promising solution for addressing challenges in seawater pollution detection and monitoring.

Screen-Printed PVDF Piezoelectric Pressure Transducer for Unsteadiness Study of Oblique Shock Wave Boundary Layer Interaction.

Shock wave boundary/layer interactions (SWBLIs) are critical in high-speed aerodynamic flows, particularly within supersonic regimes, where unsteady dynamics can induce structural fatigue and degrade vehicle performance. Conventional measurement techniques, such as pressure-sensitive paint (PSP), face limitations in frequency response, calibration complexity, and intrusive instrumentation. Similarly, MEMS-based sensors, like Kulite® sensors, present challenges in terms of intrusiveness, cost, and integration complexity. This study presents a flexible, lightweight polyvinylidene fluoride (PVDF) piezoelectric sensor array designed for high-resolution wall-pressure measurements in SWBLI research. The primary objective is to optimize low-frequency pressure fluctuation detection, addressing SWBLI's need for accurate, real-time measurements of low-frequency unsteadiness. Fabricated using a double-sided screen-printing technique, this sensor array is low-cost, flexible, and provides stable, high-sensitivity data. Finite Element Method (FEM) simulations indicate that the sensor structure also has potential for high-frequency responses, behaving as a high-pass filter with minimal signal attenuation up to 300 kHz, although the current study's experimental testing is focused on low-frequency calibration and validation. A custom low-frequency sound pressure setup was used to calibrate the PVDF sensor array, ensuring uniform pressure distribution across sensor elements. Wind tunnel tests at Mach 2 verified the PVDF sensor's ability to capture pressure fluctuations and unsteady behaviors consistent with those recorded by Kulite sensors. The findings suggest that PVDF sensors are promising alternatives for capturing low-frequency disturbances and intricate flow structures in advanced aerodynamic research, with high-frequency performance to be further explored in future work.

Multi-Level High Entropy-Dissipative Structure Enables Efficient Self-Decoupling of Triple Signals.

The theory of high entropy-dissipative structure is confined to high-entropy alloys and their oxide materials under harsh conditions, but it is very difficult to obtain high entropy-dissipative structure for smart sensors based on polymers and metal oxides under mild conditions. Moreover, multiple signal coupling effect heavily hinder the sensor applications, and current multimodal integrated devices can solve two signal-decoupling, but need very complicated process way. In this work, new synthesis concept is the first time to fabricate high entropy-dissipative conductive layer of smart sensors with triple-signal response and self-decoupling ability within poly-pyrrole/zinc oxide (PPy/ZnO) system. The sensor (SPZ20) amplifies pressure (17.54%/kPa) and gas (0.37%/ppm), reduces humidity (0.41%/% RH) and temperature (0.12%/°C) signals, simultaneously achieving the triple self-decoupling effect of pressure and gas in the complex temperature-humidity field because of the enlarged pressure-contact area, enhanced gas-responsive sites, altered vapor path and its own heat insulation function. Additionally, it inherits the strong robustness (500 rubbing, washing, and heating or freezing cycles) and endurance (10000 photo-purification cycles) of traditional high-entropy materials for information transmission and smart alarms in emergencies or harsh environments. This work gives a new insight into the multiple-signal response and smart flexible electronic design from natural fibers.

VOC Detection Using p-Type Sm(Fe,Co)O3 Perovskite Oxides

In recent years, there has been concern about the resurgence of sick house syndrome caused by volatile organic compounds (VOCs) emitted from building materials, and there is an urgent need to develop sensors that can continuously detect VOCs with high sensitivity. Our research group is conducting research on VOC detection characteristics using SmFeO3. SmFeO3 is a p-type semiconductor with a perovskite crystal structure and has high oxidation activity against VOCs. However, SmFeO3 has high electrical resistance in air, and sensors using SmFeO3 require heating to a measurable resistance range (approximately 300°C or higher), and also require a measurement system with high input impedance. For this reason, a high cost for apparatus and high power consumption are issues for practical application. Therefore, in this study, we focused on SmFe1-xCoxO3 as the sensing electrode material to operate sensors at reduced temperature. Since SmFeO3 is a p-type semiconductor, Co substitution or hole doping is expected to increase electrical conductivity, and a good redox property of cobalt ion may enhance catalytic activity of VOC decomposition. Regarding the oxidation activity of SmFe1-xCoxO3 powder, the oxidation activity against toluene increased with increasing Co content. T50 = 210oC (x=0), 205 oC (x=0.1), 155 oC (x=0.3), 165 oC (x=0.5). The oxidation activity for ethanol also increased with the amount of substitution, T50 =130oC (x=0), 110 oC (x=0.1), 85 oC (x=0.3), 75 oC (x=0.5). The temperature at which the maximum response value of each sensor was shown is 300℃ (x=0), 250℃ (x=0.1), 250℃(x=0.3), and 200oC (x=0.5). It was found that the Co substitution effectively reduce operating temperature. The response value (RVOC/Rair) in ethanol was results in S=4.6 (x=0 at 300oC), S=6.4 (x=0.1 at 250oC) and the sensor with x=0.1 increased sensor response at reduced temperature. The increase in the response value is thought to be because the sensor with x=0.1 has a higher oxidation activity at 250 oC than that with x=0 at 300 oC, The response of the sensors followed the power law, S=aPVOC n. Here, a and n are constants, and PVOC is the partial pressure of the gas. The constants a and n were determined from the equation log(S)=log(a)+nlog(PVOC), and the response value to 0.1 ppm ethanol was predicted as S=1.11 (x=0), 1.88 (x=0.1), 1.88 (x=0.3), and 1.94 (x=0.5), and it was expected that the response values at 0.1 ppm would increase due to cobalt substitution. Sensor properties mainly depend on material properties and sensor structure (sensing membrane structure). Therefore, it is suggested that higher sensitivity can be achieved by optimizing the sensor structure in the future, and it is considered that the use of cobalt substitutes as a sensor material is useful from the viewpoint of sensitivity.

MoS2-xSex lamellae assembled with lotus-leaf-like structures for sensitive NO2 gas sensors at room temperature

Transition metal disulfides (TMDs) have been the subject of extensive research interest in the field of gas sensors due to their distinctive physical and chemical characteristics. However, the insufficient stability and poor moisture resistance, is the two major challenges of these materials in practical applications. In this work, pristine Mo-S bonds were partially transformed into Mo-Se bonds by thermal selenization treatment under H2(5%)/Ar atmosphere conditions to form 3D MoS2-xSex alloy nanocomposites, which the surface of the composites possess a nanoscale and dense convex structure (100–400 nm), similar with the bionic structure of the lotus leaf in morphology. The fabricated MoS2-xSex sensor with lotus-leaf-like structure and large number of sulfur defects revealed high sensitivity (S = 71.2), fast response (4.7 s) and lower limit (50 ppb) to NO2 at room temperature (RT, 25 °C). Notably, the unique lotus leaf-like structural design of the MoS2-xSex alloy nanocomposite considerably improves the moisture resistance and enhances its stability in humid environments.

High-sensitivity ocean temperature sensor usinga reflective optical microfiber coupler andmachine learning methods.

This paper proposes a novel seawater temperature sensor, to the best of our knowledge, that utilizes an optical microfiber coupler combined with a reflective silver mirror (OMCM). The sensor's sensitivity and durability are enhanced by encapsulating it in polydimethylsiloxane (PDMS). Additionally, a specially designed metal casing prevents the OMCM from responding to pressure, thus avoiding the challenge of multi-parameter demodulation and increasing its adaptability to harsh environments. The paper analyzes the advantages of the new sensor structure and evaluates its performance in terms of temperature sensitivity and compressive strength through experiments. Finally, the paper employs machine learning demodulation methods. Compared with traditional demodulation methods, the particle swarm optimization support vector regression (PSO-SVR) algorithm demonstrates a substantial reduction in the demodulation error. Specifically, the mean absolute percentage error (MAPE) relative to the full scale drops from 2.16% to 0.157%. This paper provides an effective solution for high-precision monitoring of the ocean environmental temperature.

Colorimetric Thermography by a Long-Infrared Dual-Band Metalens.

Infrared (IR) radiation thermography is extensively utilized in diverse fields due to its non-contact capability. Nevertheless, its effectiveness is often compromised by the significant emissivity variations among different objects, limiting its application to specific setups or focused object types. Colorimetric thermography is introduced as an alternative emissivity-independent method of radiation thermometry. This technique involves measuring radiance across two or more spectral bands and calculating the object's temperature based on the signal ratio, thereby mitigating emissivity effects under certain conditions. However, this method has the trade-off of necessitating bulky optical systems, complex filter imaging configurations, and sensor structures. To meet the requirements of IR thermography for compact structure, lightweight design, and customizability, a dual-band metalens is developed for the IR colorimetric thermography. The central wavelengths targeted are 9.5 and 12.5µm. The dual-band IR imaging by the fabricated dual-band metalens is demonstrated, and the colorimetric thermography of low-emissivity objects is performed without presetting emissivity values. This approach significantly eliminates measurement errors associated with emissivity by an average of 50.16% across a temperature range of 60-180°C. This innovation paves the way for dynamic and multi-target thermography using compact IR systems in complex environments.