Sensor Array Research Articles

Mapping dose distribution in small radiation field using dual-generation optical fiber X-ray sensor array technology

Small-field radiotherapy is an emerging technique that offers the potential to reduce damage to normal tissue, yet accurately measuring the dose distribution of small radiation fields presents a significant challenge. This study aims to develop an optical fiber X-ray sensor array (OFXSA) with high spatial resolution for dosimetry in small fields, thereby enhancing the effectiveness and safety of small-field radiotherapy treatments. The OFXSA combines the precision of single-point dosimeters with the ability to detect dose distribution across a small area. Two generations of sensor arrays were designed: the first-generation OFXSA comprises 7 optical fibers arranged in a hexagonal configuration (10.8 × 9.84 mm2), and the second-generation OFXSA consists of 37 optical fibers in a four-layer hexagonal distribution. These arrays were used to measure small field sizes of 1 × 1 cm2 and 0.8 × 0.8 cm2. Results from the first-generation OFXSA indicate dose distribution attenuation from the center to the periphery, while the second-generation OFXSA, with higher spatial resolution, captures more detailed and irregular dose attenuation, as well as non-uniform distribution across the field. This study demonstrates the viability of OFXSA for precise measurements in radiotherapy, particularly within small field dimensions, and lays the groundwork for future advancements in improving spatial resolution of sensor applications in small fields through complex fiber optic configurations.

Machine learning supported ground beef freshness monitoring based on near‐infrared and paper chromogenic array

AbstractMaintaining freshness and quality is crucial in the meat industry, as lipid oxidation can lead to undesirable odors, flavors, and potential health risks. Traditional methods for assessing meat freshness often involve time‐consuming and destructive techniques, highlighting the need for rapid, noninvasive approaches. Recent advancements in spectroscopic and chromogenic sensor array technologies have opened up new avenues for monitoring meat quality parameters, offering the potential for real‐time, accurate, and cost‐effective solutions. As thiobarbituric acid reactive substances (TBARS) value is a classic indicator of meat lipid oxidation, this study investigated the data fusion of near‐infrared spectroscopy (NIR) and paper chromogenic array (PCA) for monitoring ground beef TBARS. A standardized PCA was fabricated by photolithography with nine chemoresponsive dyes. Changes in ground beef volatile organic compounds during storage were captured in the shifts of PCA color patterns. Nippy, an open‐source Python module, was used for automated NIR spectra preprocessing. The optimal preprocessing pipeline was found by 10‐fold cross‐validation in machine learning model development. Among optimized models, partial least square regression showed the best performance in coefficient of determination (R2) of .9477, root mean squared error of prediction of 0.0545 mg malondialdehyde/kg meat, and residual prediction deviation of 4.3717. The promising result of this study indicated the potential for NIR and PCA combinations to monitor TBARS values for ground beef freshness assessment.

Terahertz Fingerprint Metasurface Sensor Based on Temperature Variation for Trace Molecules.

Terahertz (THz) spectroscopy has demonstrated significant potential for substance detection due to its low destructiveness and due to the abundance of molecular fingerprint absorption signatures that it contains. However, there is limited research on the fingerprint detection of substances at different temperatures. Here, we propose a THz metamaterial slit array sensor that exploits localized surface plasmons to enhance the electric field within the slit. The transmission peak frequency can be modulated via temperature adjustments. This method enables the detection of molecular absorption characteristics at multiple spectral frequency points, thereby achieving a specific and highly sensitive detection of characteristic analyte fingerprint spectra. Additionally, the sensor supports the detection of substances at multiple temperatures and sensitively identifies changes in their absorption properties as a function of temperature. Our research has employed temperature variation to achieve a highly sensitive and specific detection of trace analytes, offering a new solution for THz molecular detection.

Maglev train high-precision positioning technology based on FBG array time division multiplexing

The positioning system is crucial in operating, controlling and monitoring maglev trains. This study proposes a maglev train positioning technology based on weak Fiber Bragg Grating (wFBG) sensing array time division multiplexing (TDM). The onboard Halbach permanent magnet array generates a traveling wave magnetic field that follows the train movement. The wFBG magnetic sensor array fixed on the track senses the magnetic field changes which cause periodical variation in sensors’ center wavelength. The relative position of the onboard Halbach array and the sensors on the track can be determined with the changes in center wavelength. Therefore, train positioning can be achieved. A rotating motor equipped with a circumferentially arranged magnet array is utilized for simulation experiment to verify the frequency response characteristics of the magnetic sensor. The result is that the operating frequency range of the sensor is at most 160 Hz so the sensor can be applied to positioning under the theoretical speed of 23 km/h. A small-scale experiment containing nine sensors is also conducted, which achieves relative positioning accuracy of 20 mm and absolute positioning accuracy of 540 mm, demonstrating the feasibility of the positioning technology. This study introduces the FBG TDM technology into maglev train positioning and provides new ideas about maglev train positioning.

Fast Low-Sidelobe Pattern Synthesis Using the Symmetry of Array Geometry.

Array pattern synthesis with low sidelobe levels is widely used in practice. An effective way to incorporate sensor patterns in the design procedure is to use numerical optimization methods. However, the dimension of the optimization variables is very high for large-scale arrays, leading to high computational complexity. Fortunately, sensor arrays used in practice usually have symmetric structures that can be utilized to accelerate the optimization algorithms. This paper studies a fast pattern synthesis method by using the symmetry of array geometry. In this method, the problem of amplitude weighting is formulated as a second-order cone programming (SOCP) problem, in which the dynamic range of the weighting coefficients can also be taken into account. Then, by utilizing the symmetric property of array geometry, the dimension of the optimization problem as well as the number of constraints can be reduced significantly. As a consequence, the computational efficiency is greatly improved. Numerical experiments show that, for a uniform rectangular array (URA) with 1024 sensors, the computational efficiency is improved by a factor of 158, while for a uniform hexagonal array (UHA) with 1261 sensors, the improvement factor is 284.

Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction.

Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.

Machine learning-assisted fluorescence/fluorescence colorimetric sensor array for discriminating amyloid fibrils

Misfolding and aggregation of proteins often lead to the development of diseases, and amyloid has gained widespread attention as a biomarker for a variety of diseases. In this study, we developed a fluorescence/fluorescence colorimetric dual-mode sensor array for the detection of amyloid fibrils using several commercially available organic small molecular dyes and alkaloids, including Thioflavin T, Congo Red, 8-anilino-1-naphthalenesulfonic acid, Safranine T, berberine and coptisine, as the elements. Herein, the array could not only use the fluorescence intensities change before and after protein interaction as a pattern recognition signal, but also read the ΔR/ΔG/ΔB values of the photos taken in the UV dark box on a smartphone-based platform, which converted the chromaticity information into intuitive data. Five studied amyloid fibrils, i.e. insulin, lysozyme, bovine serum albumin, amyloid-β 42 and α-synuclein fibrils, were properly distinguished with data processing assisted by machine learning algorithms, i.e. linear discriminant analysis, principal component analysis and hierarchical cluster analysis. After reducing the number of elements by principal component analysis, a simplified array quantified individual amyloid fibrils at 0.05–5 μM and 0.5–10 μM with fluorescence and fluorescence colorimetric signals, respectively, and successfully identified 25 unknown samples with high accuracy in diluted human plasma matrix and artificial cerebrospinal fluid. The array had good selectivity and sensitivity, providing a simple and inexpensive method for amyloid discrimination.

Accurately recognizing chromium species with multi-functionalized nano Au-based sensor array

High-resolution identification of chromium (Cr) species, especially various organic-Cr complexes, in a convenient and economically-feasible manner is the prerequisite for achieving the advanced treatment of chromium wastewater. To this end, a colorimetric nano-Au sensor array was developed by taking advantage of the UV-spectra shift of gold nanoparticles (Au NPs) upon interaction with Cr species; specifically, four molecular modifiers [i.e., iminodiacetic acid (IDA), tripolyphosphate (TPP), cetyltrimethylammonium bromide (CTAB), and 1,5-diphenylcarbazide (DPC)] were intentionally employed for assembling nano-Au array receptors, which showed respective responses toward different Cr species through the formation of coordination, hydrophobic interaction, electrostatic attraction, and redox reaction, respectively; the “fingerprint” differences of the unique optical properties were then integrated for semi-quantitatively recognizing Cr species by pattern recognition techniques. Eleven ubiquitous Cr species [i.e., Cr(III), Cr(VI), and various Cr(III)-organic complexes] served as the model samples, which could be sensitively identified, no matter in individual or mixture mode, by the developed nano-Au sensor array on the basis of the colorimetric responses resulted from diverse nano-Au-aggregation behaviors, with excellent anti-interference ability in the simulated or actual water scenario. Attractively, the nano-Au sensor array can achieve very sensitive detection limit of the quantitative analyses of Cr species in a prompt in-situ manner, which usually requires a two-step process of separation and detection for the conventional analytical methods. Such a convenient strategy of Cr species discrimination conduces to rationally designing specific protocols for the advanced treatment of chromium wastewater.

Sensor Selection for an Electronic Tongue for the Rapid Detection of Paralytic Shellfish Toxins: A Case Study

The performance of an electronic tongue can be optimized by varying the number and types of sensors in the array and by employing data-processing methods. Sensor selection is typically performed empirically, with sensors picked up either by analyzing their characteristics or through trial and error, which does not guarantee an optimized sensor array composition. This study focuses on developing a method for sensor selection for an electronic tongue using simulated sensor data and Lasso regularization. Simulated sensor responses were calculated using sensor parameters such as sensitivity and selectivity, which were determined in the individual analyte solutions. Sensor selection was carried out using Lasso regularization, which removes redundant or highly correlated variables without much loss of information. The objective of the optimization of the sensor array was twofold, aiming to minimize both quantification errors and the number of sensors in the array. The quantification of toxins belonging to one of the groups of marine toxins—paralytic shellfish toxins (PSTs)—using arrays of potentiometric chemical sensors was used as a case study. Eight PSTs corresponding to the toxin profiles in bivalves due to the two common toxin-producing phytoplankton species, G. catenatum (dcSTX, GTX5, GTX6, and C1+2) and A. minitum (STX, GTX2+3), as well as total sample toxicity, were included in the study. Experimental validation with mixed solutions of two groups of toxins confirmed the suitability of the proposed method of sensor array optimization with better performance obtained for the a priori optimized sensor arrays. The results indicate that the use of simulated sensor responses and Lasso regularization is a rapid and efficient method for the selection of an optimized sensor array.

Classification and Identification of Perfume Scents by an Enantioselective Colorimetric Sensor Array of Chiral Metal–Organic‐Framework‐Based Fabry‐Pérot Films

Classification and Identification of Perfume Scents by an Enantioselective Colorimetric Sensor Array of Chiral Metal–Organic‐Framework‐Based Fabry‐Pérot Films

A supramolecular fluorescence sensor array for the differentiation of multiple anions and prediction of iodine in artificial urine using machine learning.

The simultaneous discrimination and detection of multiple anions in an aqueous solution has been a major challenge due to their structural similarity and low charge radii. In this study, we have constructed a supramolecular fluorescence sensor array based on three host-guest complexes to distinguish five anions (F-, Cl-, Br-, I-, and ClO-) in an aqueous solution using anionic-induced fluorescence quenching combined with linear discriminant analysis. Due to the different affinities of the three host-guest complexes for each anion the anion quenching efficiency for each host-guest complex was likewise different, and the five anions were well recognized. The fluorescence sensor array not only distinguished anions at different concentrations (0.5, 10, and 50 µM) with 100% accuracy but also showed good linearity within a certain concentration range. The limit of detection (LOD) was < 0.5 µM. Our interference study showed that the developed sensor array had good anti-interference ability. The practicability of the developed sensor array was also verified by the identification and differentiation of toothpaste brands with different fluoride content and the prediction of the iodine concentration in urine combined with machine learning.

Experimental Setup for Evaluating Depth Sensors in Augmented Reality Technologies Used in Medical Devices.

This paper presents a fully automated experimental setup tailored for evaluating the effectiveness of augmented and virtual reality technologies in healthcare settings for regulatory purposes, with a focus on the characterization of depth sensors. The setup is constructed as a modular benchtop platform that enables quantitative analysis of depth cameras essential for extended reality technologies in a controlled environment. We detail a design concept and considerations for an experimental configuration aimed at simulating realistic scenarios for head-mounted displays. The system includes an observation platform equipped with a three-degree-of-freedom motorized system and a test object stage. To accurately replicate real-world scenarios, we utilized an array of sensors, including commonly available range-sensing cameras and commercial augmented reality headsets, notably the Intel RealSense L515 LiDAR camera, integrated into the motion control system. The paper elaborates on the system architecture and the automated data collection process. We discuss several evaluation studies performed with this setup, examining factors such as spatial resolution, Z-accuracy, and pixel-to-pixel correlation. These studies provide valuable insights into the precision and reliability of these technologies in simulated healthcare environments.

Non-Newtonian fluid coupling media for wearable ultrasound imaging systems using rigid linear sensor array

Wearable ultrasound systems are surpassing traditional boundaries for continuous and real-time patient care and diagnostics. However, these systems have limitations due to the use of conventional coupling media with a high dehydration rate for long-term use, leading image quality to deteriorate over time. Additionally, the use of conventional acoustic gel with ultrasound probes on wearable mounts around curved human surfaces leads to imperfect probe edge contact. In this paper, we propose a Non-Newtonian-Fluid based acoustic gel coupler with high structural stability and a very low dehydration rate for wearable ultrasound systems that overcomes the above limitations. The proposed acoustic coupler exhibited enhanced imaging with a 35 % increase in lateral resolution on curved surfaces and material parameters with a low dehydration rate of 32 % and a 3.5 % increase in density, over a frequency range of 4.60 MHz to 10.60 MHz. In addition, the measurements revealed a reduced acoustic attenuation of 0.328 dB/cm/MHz with a speed of sound of 1595 m/s. Thus, integrating Non-Newtonian-based acoustic gel with these improved parameters will make wearable ultrasound systems more appropriate for consistent and continuous use on irregular and curved surfaces.

Recognizing sitting activities of excavator operators using multi-sensor data fusion with machine learning and deep learning algorithms

Recognizing excavator operators' sitting activities is crucial for improving their health, safety, and productivity. Moreover, it provides essential information for comprehending operators' behavior patterns and their interaction with construction equipment. However, limited research has been conducted on recognizing excavator operators' sitting activities. This paper presents a method for recognizing excavator operators' sitting activities by leveraging multi-sensor data and employing machine learning and deep learning algorithms. A multi-sensor system integrating interface pressure sensor arrays and inertial measurement units was developed to capture excavator operators' sitting activity information at a real construction site. Results suggest that the gated recurrent unit achieved outstanding performance, with 98.50% accuracy for static sitting postures and 94.25% accuracy for compound sitting actions. Moreover, several multi-sensor combination schemes were proposed to strike a balance between practicability and recognition accuracy. These findings demonstrate the feasibility and potential of the proposed approach for recognizing operators' sitting activities on construction sites.

3D printed stretchable coaxial fiber grid for dual-mode multifunctional tactile sensor array

Tactile sensors play a critical role in the Internet of Things (IoT) and wearable devices. However, although current tactile sensors can precisely measure pressure, the simple fabrication of stretchable high-performance tactile sensors with a broad measuring range and multifunctions is still challenging. Herein, an ink-direct writing 3D printing method is developed to fabricate novel stretchable dual-mode tactile sensors with coaxial fiber structure. The 3D printed coaxial fibers have an outer skin composed of silicone rubber and polytetrafluoroethylene micropowders thixotropic agent and an inner core of an ionic conductive solution composed of polyvinyl alcohol and sodium chloride. The coaxial fibers exhibit excellent electrical conductivity (0.54 S cm−1) and outstanding stretchability, as high as 390 %. The intersection of the two fibers constitutes a dual-mode tactile sensor which can operate in triboelectric or capacitive modes with complementary measurement ranges from 0.0003 to 0.4517 N. Besides, by combining the signals from the two modes, the sensor can identify the material that comes into contact with the sensor. A 3D printed network of tactile sensor arrays made of warp-weft interwoven coaxial fibers is able to accurately detect the locations of multi-point contact with the array. This dual-mode multifunctional tactile sensor can be widely used in various applications such as the IoT and wearable devices.

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.

Wearable multichannel-active pressurized pulse sensing platform

With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human–computer interaction. Due to the high sensitivity (460.1 kPa−1), high linearity (R2 > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual’s blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.

High‐Resolution Carbon‐Based Tactile Sensor Array for Dynamic Pulse Imaging

AbstractWith the development of modern medicine, the importance of continuous and reliable pulse wave monitoring has increased significantly in physiological evaluation and disease diagnosis. Among them, the 3D reconstruction of the pulse wave is indispensable, and needs rely on ultra‐high resolution sensor arrays, that is, high spatial resolution, temporal resolution, and force resolution. Herein, a flexible high‐density 32 × 32 tactile sensor array based on pressure‐sensitive tunneling mechanism is develpoed. Conformal graphene nanowalls (GNWs) pattern arrays are deposited on micro‐pyramidal structural Si substrate via mask‐assisted plasma enhanced chemical vapor deposition (PECVD) method and are adopted as pressure‐sensitive electrode, exhibiting a spatial resolution of 64 dots/cm2, high sensitivity (222.36 kPa−1) and short response time (2 ms). More importantly, HfO2 tunneling layer can effectively suppress noise current, which made it sense weak pressure signals with 1/1000 force resolution and SNR of 36.32 dB. By leveraging its high‐resolution array, more holistic pulse signals are acquired and the 3D shape of the pulse wave are successfully replicated. This work shows high‐resolution sensors have significant promise for applications in remote intelligent diagnostics.

Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination.

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.