Sensor Array Research Articles

An All-in-One Array of Pressure Sensors and sEMG Electrodes for Scoliosis Monitoring.

Scoliosis often occurs in adolescents and seriously affects physical development and health. Traditionally, medical imaging is the most common means of evaluating the corrective effect of bracing during treatment. However, the imaging approach falls short in providing real-time feedback, and the optimal corrective force remains unclear, potentially slowing the patient's recovery progress. To tackle these challenges, an all-in-one integrated array of pressure sensors and sEMG electrodes based on hierarchical MXene/chitosan/polydimethylsiloxane (PDMS)/polyurethane sponge and MXene/polyimide (PI) is developed. Benefiting from the microstructured electrodes and the modulus enhancement of PDMS, the sensor demonstrates a high sensitivity of 444.3 kPa-1 and a broad linear detection range (up to 81.6kPa). With the help of electrostatic attraction of chitosan and interface locking of PDMS, the pressure sensor achieves remarkable stability of over 100000 cycles. Simultaneously, the sEMG electrodes offer exceptional stretchability and flexibility, functioning effectively at 60% strain, which ensures precise signal capture for various human motions. After integrating the developed all-in-one arrays into a commercial scoliosis brace, the system can accurately categorize human motion and predict Cobb angles aided by deep learning. This study provides real-time insights into brace effectiveness and patient progress, offering new ideas for improving the efficiency of scoliosis treatment.

Identification of Pristine and Protein Corona Coated Micro- and Nanoplastic Particles with a Colorimetric Sensor Array

Identification of Pristine and Protein Corona Coated Micro- and Nanoplastic Particles with a Colorimetric Sensor Array

Visual Sensor Array for Multiple Aromatic Amines via Specific Ascorbic Acid Oxidase Mimic Triggered Schiff-Base Chemistry.

Redox nanozymes have exhibited various applications in recognizing environmental pollutants but not aromatic amines (a type of typical pollutant). Herein, with Cu2+ as a node and tryptophan (Trp) as a linker, Cu-Trp as a specific ascorbic acid oxidase mimic was synthesized, which could catalyze ascorbic acid (AA) oxidation to dehydroascorbic acid (DHAA). Alternatively, with other natural amino acids as linkers to synthesize Cu-based nanozymes, such catalytic performances are also observed. The as-produced DHAA could react with o-phenylenediamine (OPD) and its derivatives (2,3-naphthalene diamine (NDA), 4-nitro-o-phenylenediamine (4-NO2-OPD), 4-fluoro-o-phenylenediamine (4-F-OPD), 4-chloro-o-phenylenediamine(4-Cl-OPD), and 4-bromo-o-phenylenediamine(4-Br-OPD)) to form a Schiff base and emit fluorescence. Based on the results, with Cu-Trp + AA and Cu-Arg (with arginine (Arg) as a linker) + AA as two sensing channels and extracted red, green, and blue (RGB) values from emitted fluorescence as read-out signals, a visual sensor array was constructed to efficiently distinguish OPD, NDA, 4-NO2-OPD, 4-F-OPD, 4-Cl-OPD, and 4-Br-OPD as low as 10 μM. Such detecting performance was further confirmed through discriminating binary, ternary, quinary, and senary mixtures with various concentration ratios, recognizing 18 unknown samples, and even quantitatively analyzing single aromatic amine. Finally, the discriminating ability was further validated in environmental waters, providing an efficient assay for large-scale scanning levels of multiple aromatic amines.

Battery-Free, Wireless Multi-Modal Sensor, and Actuator Array System for Pressure Injury Prevention.

Simultaneous monitoring of critical parameters (e.g., pressure, shear, and temperature) at bony prominences is essential for the prevention of pressure injuries in a systematic manner. However, the development of wireless sensor array for accurate mapping of risk factors has been limited due to the challenges in the convergence of wireless technologies and wearable sensor arrays with a thin and small form factor. Herein, a battery-free, wireless, miniaturized multi-modal sensor array is introduced for continuous mapping of pressure, shear, and temperature at skin interfaces. The sensor array includes an integrated pressure and shear sensor consisting of 3D strain gauges and micromachined components. The mechanically decoupled design of the integrated sensor enables reliable data acquisition of pressure and shear at skin interfaces without the need for additional data processing. The sensor platform enables the analysis of interplay among localized pressure, shear, and temperature in response to changes in the patient's movement, posture, and bed inclination. The validation trials using a novel combination of wireless sensor arrays and customized pneumatic actuator demonstrate the efficacy of the platform in continuous monitoring and efficient redistribution of pressure and shear without repositioning, thereby improving the patient's quality of life.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing.

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

Designing a sparse sensor array for sound field reconstruction using compressive-equivalent source method.

The compressive-equivalent source method (C-ESM) can reconstruct the sound field radiated by sparsely distributed sound sources with a reduced number of sensors. To ensure the performance of the C-ESM, the transfer matrix between the sensors and equivalent point sources should exhibit sufficient incoherence. Given that the configuration of the sensor array affects this incoherence condition, concern regarding the sensor array design would arise. To address such concern, this paper proposes a sensor array design approach. The primary objective of this approach is to minimize the mean coherence of the transfer matrix within the developed iterative framework, providing the incoherence condition required by the C-ESM. Subsequently, the designed sensor array is utilized by the C-ESM for the reconstructions. The effectiveness and practicality of the proposed approach are validated through numerical simulations and experiments.

An E-nose system for identification and quantification of hazardous gas mixtures using a combined strategy of CNNs and attentional mechanisms

The chemical industry generates a broad spectrum of hazardous gases, presenting significant challenges for conventional detection methods due to their diverse chemical properties and low concentration levels. E-nose systems, employing sensor arrays, offer significant potential for the determination of gas mixtures. This study presents a novel E-nose algorithm, CNN-ECA, which integrated CNNs and attention mechanisms to improve the recognition accuracy of E-nose systems. By integrating the attention mechanism module into CNN’s convolutional operations, the algorithm emphasizes critical feature information. Three hazardous gases (ammonia, methanol, and acetone) and their mixtures were chosen as target gases. CNNs were combined with various attention mechanism networks (SENet, ECA, and CBAM) to construct models, which were then employed to train and evaluate data collected from the sensor array. The results were compared with traditional network models (KNN, SVM, and CNN). Experimental findings indicated that the prediction performance of CNN models combined with attention mechanism networks surpassed that of traditional network models. Particularly, the CNN-ECA network model demonstrated the highest performance in both qualitative and quantitative analyses. This study presents a promising solution for mixed gas detection by synergizing CNN and attention mechanism networks, thereby enhancing the accuracy and reliability of mixed gas measurements. Moreover, capitalizing on the lightweight architecture of the CNN-ECA model, transfer learning techniques were employed to adapt it for deployment on the Raspberry Pi hardware platform. This facilitates the development of a real-time E-nose system for gas detection.

Bioimpedance sensor array for monitoring chronic wounds: Validation of method feasibility.

In an ageing society, the incidence of hard-to-heal wounds is rising. Chronic wound healing is a complex process, which requires specialised treatment. Clinical assessment of the wound is essential to establish care approaches but is usually based on visual evaluation and it remains challenging. Therefore, innovative quantitative methods for the assessment of chronic wounds are needed. We conducted a single-centre observational study designed to assess the feasibility of a bioimpedance measurement method conducted with a multielectrode sensor array to monitor the wound healing process in patients with chronic wounds of venous, mixed venous-arterial and diabetic aetiology. In total, 104 measurements of bioimpedance were conducted in 18 ulcers during the study. Across all 7 patients analysed, the bioimpedance of the ulcers was consistently increasing as the wound surface was decreasing. The variables had significant (p < 0.001) and strong negative correlation (r = -0.86). We validated the feasibility of the bioimpedance measurement method for the monitoring of the wound healing process on the lower legs. It may be a promising quantitative method for monitoring the status of the wounds. However, long-term measurements are needed to show the usability of the electrode dressing and bioimpedance measurement in the assessment of chronic wounds.

A Temperature Monitoring Method for Sensor Arrays Based on Temperature Mapping and Improved Mask R-CNN

A Temperature Monitoring Method for Sensor Arrays Based on Temperature Mapping and Improved Mask R-CNN

Research on Gas Recognition Based on Gas Sensor Array and Feature Analysis

Research on Gas Recognition Based on Gas Sensor Array and Feature Analysis

Highly Stretchable, Tissue-like Ag Nanowire-Enhanced Ionogel Nanocomposites as an Ionogel-Based Wearable Sensor for Body Motion Monitoring.

The development of wearable electronic devices for human health monitoring requires materials with high mechanical performance and sensitivity. In this study, we present a novel transparent tissue-like ionogel-based wearable sensor based on silver nanowire-reinforced ionogel nanocomposites, P(AAm-co-AA) ionogel-Ag NWs composite. The composite exhibits a high stretchability of 605% strain and a moderate fracture stress of about 377 kPa. The sensor also demonstrates a sensitive response to temperature changes and electrostatic adsorption. By encapsulating the nanocomposite in a polyurethane transparent film dressing, we address issues such as skin irritation and enable multidirectional stretching. Measuring resistive changes of the ionogel nanocomposite in response to corresponding strain changes enables its utility as a highly stretchable wearable sensor with excellent performance in sensitivity, stability, and repeatability. The fabricated pressure sensor array exhibits great proficiency in stress distribution, capacitance sensing, and discernment of fluctuations in both external electric fields and stress. Our findings suggest that this material holds promise for applications in wearable and flexible strain sensors, temperature sensors, pressure sensors, and actuators.

SLSR-Based Microwave NDT Array Sensor for Surface Cracks Inspection on Coated CFRP Structures

SLSR-Based Microwave NDT Array Sensor for Surface Cracks Inspection on Coated CFRP Structures

Home Air Quality Monitoring System

Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.

Home Air Quality Monitoring System

Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.

Explosives Detection Using Octanethiol Gold Nanoparticle Inkjet Printed Devices

This study focuses on implementing 1-octanethiol gold nanoparticles (OT-AuNPs) as chemiresistive sensors and comparing fabrication by inkjet-printing with conventional drop-casting to detect volatile organic compounds (VOCs) and explosives including HMTD, PETN, KClO3, TATP, RDX, TNT, and UN. Inkjet-printing technology potentially offers more controlled deposition of OT-AuNPs and more uniform sensor properties compared to a drop-casting process. Using inkjet-printing, we found that a minimum of 600 OT-AuNPs printed layers with 2.4[Formula: see text]pL drop volume, 9 drops per layer, and 60[Formula: see text][Formula: see text]m drop spacing was sufficient to reduce chemiresistor device baseline resistances to around [Formula: see text] and obtain more consistent responses. By contrast, 12[Formula: see text][Formula: see text]L OT-AuNPs drop-casting yielded device resistances spread over a range of [Formula: see text] due to uneven deposition. For inkjet-printed devices, higher response magnitudes with lower variability were achieved for explosive vapor detection whereas a larger spread was observed for drop casting. The improved uniformity of baseline resistances and sensor responses indicates inkjet-printed devices were more reproducible and repeatable than drop-casting. In addition, inkjet-printing consumes lower amounts of OT-AuNPs for material and cost savings. The results demonstrate that inkjet-printed devices are promising for use in sensor arrays to fabricate electronic noses for VOCs and explosives detection.

Determination of Tetracaine and Oxymetazoline in Drugs and Saliva via Potentiometric Sensor Arrays Based on Fluoropolymer/Polyaniline Composites

AbstractA growing interest in dental practice in intranasal anesthesia using tetracaine and oxymetazoline dictates the need for their simultaneous determination in combination drugs and human saliva. Potentiometric multisensory systems based on perfluorosulfonic acid membranes, including polyaniline‐modified ones, were developed for these purposes. A change in the distribution of the sensor sensitivity to the related analytes was achieved by variation of the conditions for concentration polarization at the membrane interface with a studied solution due to a change in the intrapore volume, nature, and availability of the sorption centers, as well as the hydrophilicity of the membrane surface that were specified by the conditions for their synthesis and subsequent hydrothermal treatment. Reversibility of the analyte sorption using the chosen conditions for regeneration provided long‐term stable work of both the sensors and the calibration equations established by multivariate linear regression. The membrane modification promoted their resistance to fouling. The relative errors of the simultaneous tetracaine and oxymetazoline determination in the combination drug solutions were no greater than 7% and 11%, while in the artificial saliva solutions, they were 15% and 17%, respectively, when an array of the cross‐sensitive sensors based on the composite membranes prepared by different methods was used. The analysis errors were reduced to 3%–6% when analyzing the drug and to 0.2%–6% when analyzing the artificial saliva if an array was organized with the sensors based on the membrane with the dopant and the membrane without it, due to the decreasing correlation between their responses.

Construction of colorimetric sensor arrays using steel slag-based composites for highly sensitive detection of tetracycline antibiotics.

Sensor array methods have received much attention in recent years. In this study, a colorimetric sensor array consisting of three kinds of steel slag-based composites (including porphyrin-functionalized non-magnetic steel slag (NMSS-Por), alkali-excited steel slag (A-SS), and platinum modified steel slag (ALANH-Pt)) was developed for the detection and recognition of tetracycline antibiotics (TCs) such as tetracycline (TC), oxytetracycline (OTC) and doxycycline (DOX). Linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) showed that the colorimetric sensor array has excellent recognition ability for TCs. The detection limits of this sensor array for TC, OTC, and DOX were 0.059 μM, 0.111 μM and 0.118 μM, respectively, which provided higher sensitivity compared to the colorimetric sensors composed of a single steel slag-based composite material. At the same time, the array sensor has anti-interference ability, and this study provides a new application route for steel slag.

Knowledge-assisted differential evolution based non-contact voltage measurement for multiconductor systems in smart grid

Multiconductor systems, such as overhead lines and multicore cables, are widely used in smart grids for their reliable power transmission capabilities. However, measuring the voltage of individual conductors in these systems has been challenging despite their promising applications. This paper proposes a novel framework to address this issue by utilizing a low-cost electric field (EF) sensor array and a knowledge-assisted differential evolution algorithm. The framework involves designing an annular EF sensor array to capture EF information around the multiconductor systems while ensuring its anti-interference capabilities through grounding and balanced electrodes. Furthermore, an optimization problem is formulated to reflect the model accuracy, and a knowledge-assisted differential evolution algorithm is proposed to optimize the voltage values and conductor positions by leveraging knowledge about the conductor positions and EF distribution. The use of this knowledge is expected to generate promising initial solutions for accelerating the optimization process. The proposed framework is experimentally validated, and the results demonstrate the robustness and accuracy of the EF sensor array and algorithm in obtaining the voltage of individual conductors in multiconductor systems.

Colorimetric sensor array for identifying antioxidants based on pyrolysis-free synthesis of Fe–N/C single-atom nanozymes

Iron-anchored nitrogen/doped carbon single-atom nanozymes (Fe–N/C), which possess homogeneous active sites and adjustable catalytic environment, represent an exemplary model for investigating the structure-function relationship and catalytic activity. However, the development of pyrolysis-free synthesis technique for Fe–N/C with adjustable enzyme-mimicking activity still presents a significant challenge. Herein, Fe–N/C anchored three carrier morphologies were created via a pyrolysis-free approach by covalent organic polymers. The peroxidase-like activity of these Fe–N/C nanozymes was regulated via the pores of the anchored carrier, resulting in varying electron transfer efficiency due to disparities in contact efficacy between substrates and catalytic sites within diverse microenvironments. Additionally, a colorimetric sensor array for identifying antioxidants was developed: (1) the Fe–N/C catalytically oxidized two substrates TMB and ABTS, respectively; (2) the development of a colorimetric sensor array utilizing oxTMB and oxABTS as sensing channels enabled accurate discrimination of antioxidants such as ascorbic acid (AsA), glutathione (GSH), cysteine (Cys), gallic acid (GA), and caffeic acid (CA). Subsequently, the sensor array underwent rigorous testing to validate its performance, including assessment of antioxidant mixtures and individual antioxidants at varying concentrations, as well as target antioxidants and interfering substances. In general, the present study offered valuable insights into the active origin and rational design of nanozyme materials, and highlighting their potential applications in food analysis.

Construction of a sensor array using TiO2 NRs/CuO/rMIP with imprinted sites regulation as light-activated nanozyme to colorimetric detect fluoroquinolones

Fluoroquinolone antibiotics are widely used in daily life, but their abuse has caused serious problems, so it is very important to detect fluoroquinolones accurately and conveniently. In this study, a light-activated nanozyme sensor array (TiO2 NRs/CuO/rMIP) was prepared, and principal component analysis (PCA) was employed for qualitative and quantitative determination of four fluoroquinolones in water environments. The photoactive sites of TiO2 were determined by photo-deposition of CuO, and the molecularly imprinted sites was anchored to the photoactive site by the composite action between antibiotic templates and CuO, which led to the consistent adjustment of the imprinted sites and photoactive sites, thus enhancing the enzyme-like activity of TiO2 NRs/CuO/rMIP. In Addition, by adjusting the types and proportions of antibiotic templates during the preparation of MIP, TiO2 NRs/CuO/rMIP sensors with different response signals to four fluoroquinolones were prepared, and a sensor array was established based on them. Through an RGB analysis system of smart phone and PCA analysis method, qualitative and quantitative detection of the four fluoroquinolones was realized. The detection range of the four antibiotics was 0.1–3000 μM, which showed a good prospect for the actual detection of antibiotics.