Selective Sensor Research Articles

Supporting condition-based maintenance implementation: a methodology

PurposeThis paper aims to propose a methodology to assist manufacturing companies in the implementation of condition-based maintenance (CBM) to their equipment. The developed methodology intends to consider the use of sensors already installed on the equipment and, when required, to support the selection of sensors available on the market. Since CBM using sensors is not always feasible, the information gathered for the feasibility study of CBM implementation is also used to assign other maintenance strategies.Design/methodology/approachBased on the literature review, requirements and specifications were established for endowing the methodology with relevant and distinctive characteristics. The structure of the methodology and the associated steps were defined based on this information. Then, the methodology was validated and refined using a case study.FindingsIn the case study company, following the methodology and the respective steps, appropriate maintenance strategies were assigned to a selected manufacturing machine, considering information related to the failure modes with the most significant impact, and CBM was applied to a selected component for which the benefit outweighs the costs involved, using data acquired by sensors subsequently installed on the analyzed machine.Practical implicationsDue to its comprehensiveness, this methodology can contribute to make CBM implementation accessible to a high number of companies and encourage the application of a wide variety of monitoring techniques.Originality/valueThis new methodology can be easily integrated into a computerized maintenance management system and has the advantage of facilitating the collection, organization and standardization of technical knowledge required to support CBM implementation and define the most appropriate maintenance strategy systematically and automatically. It guides the prioritization of equipment and failure modes, and the decision-making regarding the selection of sensors and the allocation of maintenance strategies with the aim of reducing costs.

Cu‐MOF‐74 as Fluorescent Probe: Selective Optical Sensor for H2S Gas

AbstractIn order to address the air quality issues and industrial gas leakages effectively, gas sensors must be highly sensitive, selective, stable, cost‐effective, and with high life time. We present here a facile solvothermal method to synthesize a Cu based metal‐organic framework (Cu‐MOF‐74) and demonstrated its potential as a selective fluorescent probe for environmental H2S gas detection. Cu‐MOF‐74 based fluorescence sensor shows strong affinity towards H2S with the limit of detection as low as 7 ppm., Though Cu‐MOF‐74 based sensor exhibited high sensitivity and selectivity for H2S gas over H2, SO2 and NH3, Co‐MOF‐74 is inactive towards H2S sensing. The increased fluorescence intensity in presence of H2S gas as well as the decreased fluorescence intensity in presence of NO2 gas was investigated by experimental observation and theoretical calculation. In presence of H2S gas, interaction between Cu and sulphur atoms occur, the bond between Cu and ligand. This reduces the extent of ligand to metal charge transfer, resulting in increase in fluorescence intensity. The present attempt led to the progress of highly selective and sensitive fluorescence‐based environmental gas sensing probe using metal organic framework with a wide operating temperature ranging of 25 °C to 90 °C.

Cu doped ceria nanoparticles-rhodamine B as a novel chemiluminescence system and its application for nitrite detection in water and food samples.

Fe, Ni, and Cu doped ceria nanoparticles (CeNPs) were prepared with a simple and one-pot hydrothermal synthesis method. We investigated the chemiluminescence (CL) interaction between these NPs and rhodamine B (Rh B) and found that the highest CL intensity was related to the Rh B- Cu doped CeNPs. We assigned that to the higher catalytic property of Cu doped NPs compared to the others. Cu doped CeNPs have been applied for the first time as a catalyst in chemiluminescence reactions. Rh B- Cu doped CeNPs reaction was introduced as a novel CL system, and its mechanism was studied. Considering the sensitivity, simplicity, portability, and rapidness of the CL methods, we applied the introduced reaction to the development of a CL sensor for nitrite monitoring. In the presence of nitrite, the CL intensity of the system decreased, and there was a linear relationship between the CL intensity and nitrite concentration in the range 0.5-100 µM. Based on this fact, a sensitive and selective CL sensor with the detection limit of 0.2µM was established for nitrite detection in various food and water samples.

Highly sensitive and selective carbon dots sensor for hydrogen sulfide based on dual quenching effect

Highly sensitive and selective carbon dots sensor for hydrogen sulfide based on dual quenching effect

Enhancing Selectivity of Two‐Dimensional Materials‐Based Gas Sensors

AbstractTwo‐dimensional (2D) materials have emerged as promising candidates for gas sensing applications due to their exceptional electrical, structural, and chemical properties, which enable high sensitivity and rapid response to gas molecules. However, despite their potential, 2D material‐based gas sensors face a significant challenge in achieving adequate selectivity, as many sensors respond similarly to multiple gases, leading to cross‐sensitivity and inaccurate detection. This review provides a comprehensive overview of the recent advancements for improving the selectivity of 2D gas sensors. It explores material modification strategies, such as functionalizing the sensing components and tuning adsorption dynamics, to enhance selective gas interactions. Engineering approaches, including field‐effect modulation and sensor array design, are also discussed as effective methods to fine‐tune sensor performance. Additionally, the integration of machine learning (ML) algorithms is highlighted for their potential to differentiate among multiple analytes. Prospects for further improving selectivity through material optimization, sensor calibration, and drift compensation are explored, along with the incorporation of smart sensing systems into the Internet of Things (IoT). This review outlines key objectives and strategies that pave the way for next‐generation gas sensors with enhanced selectivity, reliability, and versatility, poised to impact a wide range of applications from environmental monitoring to industrial safety.

Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles

Sensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sensor clusters, which result in the collection of redundant information, which can deteriorate performance while also increasing the associated network costs. Therefore, this work combines a predictive posterior distribution methodology with a novel simplified objective function for optimally identifying and forming smaller sensor clusters before activation and measurement collection. The goal of the proposed objective function is to reduce network communication and computation costs while still maintaining the tracking performance of using far more sensors. The developed optimisation algorithm results in reducing the size of selected sensor clusters by an average of 50% while still maintaining the tracking performance of general traditional techniques.

Design of Fluorescence Enhancing Sensor for Mercury Detection via Bamboo Cellulose-Derived Carbon Dots.

Mercury contamination of the environment is extremely hazardous to human health because of its significant toxicity, especially in water. Biomass-derived fluorophores such as carbon dots (CDs) have emerged as eco-friendly and cost-effective alternative sensors that provide comparable efficacy while mitigating the environmental and economic drawbacks of conventional methods. In this work, we report the fabrication of a selective fluorescence-enhancing sensor based on sulfur-doped carbon dots (SCDs) using waste bamboo-derived cellulose and sodium thiosulfate as the soft base dopant, which actively complexes with mercury ions for detection. SCDs with an average size of 4 nm were synthesized hydrothermally, and the sulfur doping was confirmed quantitatively with an atomic percentage of 6.5%. Optical studies reveal an abnormal fluorescence enhancement of SCD in the presence of mercury due to the aggregation of carbon dots via sulfur-containing functional groups. The fabricated sensor exhibits a low detection limit of 5.16 nM, suggesting its application potential as a reliable mercury sensor. Real-time analyses carried out using tap water samples spiked with mercury and industry samples showed high efficiency for Hg(II) detection. The sensing performance was also demonstrated by using SCD-coated filter paper strips.

Wireless Accelerometer Architecture for Bridge SHM: From Sensor Design to System Deployment

This paper introduces a framework to perform operational modal analysis (OMA) for structural health monitoring (SHM) by presenting the development and validation of a low-power, solar-powered wireless sensor network (WSN) tailored for bridge structures. The system integrates accelerometers and temperature sensors for dynamic structural assessment, all interconnected through the energy-efficient message queuing telemetry transport (MQTT) messaging protocol. Moreover, it delves into the details of sensor selection, calibration, and the design considerations necessary to address the unique challenges associated with bridge structures. Special attention is given to the solar-powered aspect, allowing for extended deployment periods without the need for frequent maintenance or battery replacements. To validate the proposed system, a comprehensive field deployment was conducted on an actual bridge structure. The collected data were transmitted through MQTT messages and analyzed by means of OMA. Comparative studies with traditional wired systems underscore the advantages of the solar-powered wireless solution in terms of sustainability, scalability, and ease of deployment. Results from the validation phase demonstrate the system’s capability to provide accurate and real-time data needed to assess the health state of the monitored asset. This paper concludes with insights into the practical implications of adopting such a solar-powered WSN, emphasizing its potential to revolutionize bridge health monitoring by offering a cost-effective and energy-efficient solution for long-term infrastructure resilience.

Research Progress on the Application of Nanocellulose in Glucose Sensing.

Nanocellulose is not only a biocompatible and environmentally friendly material but also has excellent mechanical properties, biodegradability, and a large number of hydroxyl groups that have a strong affinity for water. These characteristics have attracted significant attention from researchers in the field of glucose sensing. This review provides a brief overview of the current research status of traditional materials used in glucose sensors. The sensing performance, chemical stability, and environ-mental properties of nanocellulose-based glucose sensors are compared and summarized based on the three sensing methods: electrochemical sensing, colorimetric sensing, and fluo-rescence sensing. The article focuses on recent strategies for glucose sensing using nanocel-lulose as a matrix. The development prospects of nanocellulose-based glucose sensors are also discussed. Nanocellulose has outstanding structural characteristics that contribute signifi-cantly to the sensing performance of glucose sensors in different detection modes. However, the preparation process for high-quality nanocellulose is complicated and has a low yield. Furthermore, the sensitivity and selectivity of nanocellulose-based glucose sensors require further improvement.

Recent emerging trends in dendrimer research: Electrochemical sensors and their multifaceted applications in biomedical fields or healthcare.

Dendrimers enhance the selectivity and sensitivity of sensors through their synthetic, highly branched, three-dimensional structures and large surface area. This unique architecture enables precise functionalization with various recognition elements, significantly improving the specificity and sensitivity of electrochemical sensors for detecting disease markers, biomolecules, and environmental pollutants. Dendrimer-based electrochemical sensors offer promising advancements in healthcare, such as detecting biomarkers for heart disease, monitoring blood glucose levels, and sensitively determining cancer-related proteins. Additionally, incorporating metals and conductive polymers into dendrimer nanocomposites can further enhance sensor performance. This review article provides a detailed overview of dendrimer's history, structure, properties, electrochemical properties, and synthesis methods. Particular attention has been paid to recent developments in the applications of dendrimers including electrochemical sensors, drug delivery, gene therapy and bioimaging. Recent progress in various applications of dendrimer-based electrochemical sensors developed over the last seven years, focusing on their healthcare applications and discussing the primary goals and challenges that will shape future research in this field, is also critically analyzed. These advances in dendrimer technology hold great potential for the development of novel therapeutics and expanded applications in sensor design.

Eco-friendly synthesis of a porous reduced graphene oxide-polypyrrole-gold nanoparticle hybrid nanocomposite for electrochemical detection of methotrexate using a strip sensor.

Chemotherapy is a crucial cancer treatment, but its effectiveness requires precise monitoring of drug concentrations in patients. This study introduces an innovative electrochemical strip sensor design to detect and continuously monitor methotrexate (MTX), a key chemotherapeutic drug. The sensor is crafted through an eco-friendly synthesis process that produces porous reduced graphene oxide (PrGO), which is then integrated with gold nanocomposites and polypyrrole (PPy) to boost the performance of a screen-printed carbon electrode (SPCE). Advanced techniques were employed for detailed characterization of the nanocomposites such as X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and BET analysis. The enhanced sensor exhibited a notable increase in the electrochemical oxidation signals of MTX, attributed to the improved electron transfer at the SPCE/PrGO-PPy-Au electrode interface. Superior electrochemical interfacial properties were well characterized with the techniques of cyclic voltammetry, electrochemical impedance spectroscopy, and square wave voltammetry. The sensor demonstrates an efficient electrochemical response toward the detection of MTX with a broad detection range from 130 nM to 1 μM, an impressively low detection limit of 0.4 nM in human serum, and a sensitivity of 24.1 μA μM-1. This combination highlights its exceptional performance in detecting analytes with high precision and sensitivity. The sensor exhibited a long-term continuous monitoring stability response to monitor the MTX drug in human serum for 4 hours. The sensor's high sensitivity, selectivity, reproducibility, and stability over time emphasize its potential as a valuable tool for the swift on-site testing of anticancer drugs in clinical and environmental settings.

Novel carbon paste sensor modified with MWCNT and Ca-doped ZnO nanocomposite for therapeutic monitoring of Favipiravir in COVID-19 patients

Favipiravir (FAV) is a prodrug with a proven antiviral efficacy against coronavirus 2. Close therapeutic monitoring of FAV is crucial for COVID-19 patients due to its variable dosing, complex pharmacokinetics, and notable drug interactions. This study presents a novel, selective electrochemical sensor for FAV detection, using a carbon paste electrode (CPE) modified with multi-walled carbon nanotubes (MWCNTs) and calcium-doped ZnO nanoparticles (Ca-ZnO NPs). Characterization of the Ca-ZnO NPs was performed using Fourier-transform infrared spectroscopy (FT-IR), field emission-scanning electron microscope (FE-SEM), and X-ray photoelectron spectroscopy (XPS). Several operational parameters were optimized, including carbon paste composition, buffer selection, pH, and electrochemical waveform settings. Under optimized conditions, an oxidation signal of FAV was identified at approximately + 1.22 V versus an Ag/AgCl reference electrode in Britton-Robinson buffer (BRB) at pH 4.0. Differential pulse voltammetry (DPV) facilitated the detection of FAV across a wide dynamic concentration range of 0.6–100.0 µM, with detection and quantification limits of 0.17 and 0.51 µM, respectively. The developed method demonstrated high selectivity towards FAV, effectively distinguishing it from its acidic degradation product (ADP), qualifying it as a stability-indicating assay. Evaluations using the GAPI, AGREE, and RGB 12 metrics confirmed alignment with green and white analytical chemistry principles. Furthermore, this method was successfully applied to quantify FAV in human plasma, making it suitable for therapeutic drug monitoring (TDM) in COVID-19 patients.

Atomic-scale insights into sensing mechanisms of platinum-doped tin dioxide as highly selective hydrogen sensor

Atomic-scale insights into sensing mechanisms of platinum-doped tin dioxide as highly selective hydrogen sensor

Harnessing mixed-phase MoS2 for efficient room-temperature ammonia sensing.

Molybdenum disulfide (MoS2), a notable two-dimensional (2D) material, has attracted considerable interest for its potential applications in gas sensing, despite its typically insulating characteristics, which have limited its practical use. In this study, we present the use of mixed phase MoS2 (1T@2H-MoS2) to overcome sensing limitations of MoS2 material by enhancing its conductivity and demonstrating its high-performance characteristics for sensing ammonia (NH3) at room temperature (20 °C). The 1T@2H-MoS2 was synthesized via a hydrothermal process, and the coexistence of two different phases (the 1T and 2H phases) was confirmed by transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and Raman spectroscopy. The flower-like morphology was confirmed by field emission scanning electron microscopy (FESEM) and TEM. Our results indicate that the presence of both 1T and 2H phases within the material introduces sulfur vacancies, which we propose are critical to significantly enhancing its sensitivity to NH3 gas. The ammonia-sensing performance of the 1T@2H-MoS2 material was evaluated, and it demonstrated rapid and selective detection of NH3 gas across a wide concentration range (2 ppm to 100 ppm), with a very swift response time (7 s), fast recovery and high selectivity at room temperature without requiring heating. This improvement is attributed to the increased conductivity and effective active sites provided by the sulfur defects. This study underscores the potential of mixed-phase MoS2 in developing rapidly responsive and highly selective NH3 sensors, paving the way for the safety monitoring of hazardous gases in various industrial settings.

A Facile Synthesis of Bimetallic Copper-Silver Nanocomposite and Their Application in Ascorbic Acid Detection

Background: An important antioxidant, ascorbic acid, must be detected in several industrial samples collected from food, pharmaceuticals, and water treatment plants. Herein, we reported a method to produce a bimetallic copper-silver (Cu-Ag) nanocomposite and used it in the development of very sensitive and selective electrochemical sensor for the detection of ascorbic acid. Methods: A simple chemistry concept was used during the synthesis process to reduce the cost while minimizing the use of dangerous chemicals and minimizing the environmental impact. The Strobilanthes kunthiana leaves extract effectively reduced the copper and silver ions, resulting in the creation of an extremely stable and evenly distributed Cu-Ag nanocomposite. Results: As-prepared bimetallic Cu-Ag nanocomposite exhibited outstanding electrochemical activity against ascorbic acid oxidation. The nanocomposite was examined using field emission scanning electron microscopy (FE-SEM), energy dispersive spectroscopy (EDS), elemental mapping (EMap) and X-ray diffraction analysis (XRD) to ascertain its composition, structure, and stability. Using cyclic voltammetry (CV), the electrochemical performance of the nanocomposite and also the detection of ascorbic acid were carried out. The bimetallic Cu-Ag nanocomposite also exhibited better long-term stability and fouling resistance, making it appropriate for use in real-world applications and complex sample matrices. Conclusion: The bimetallic Cu-Ag nanocomposite coated electrode was used to detect the concentration of ascorbic acid by amperometry. As a result, this study offered a simple chemical method for creating a bimetallic copper-silver nanocomposite with superior electrochemical qualities for the accurate detection of ascorbic acid.

Randomized greedy methods for weak submodular sensor selection with robustness considerations

We study a pair of budget- and performance-constrained weak submodular maximization problems. For computational efficiency, we explore the use of stochastic greedy algorithms which limit the search space via random sampling instead of the standard greedy procedure which explores the entire feasible search space. We propose a pair of stochastic greedy algorithms, namely, Modified Randomized Greedy (MRG) and Dual Randomized Greedy (DRG) to approximately solve the budget- and performance-constrained problems, respectively. For both algorithms, we derive approximation guarantees that hold with high probability. We then examine the use of DRG in robust optimization problems wherein the objective is to maximize the worst-case of a number of weak submodular objectives and propose the Randomized Weak Submodular Saturation Algorithm (Random-WSSA). We further derive a high-probability guarantee for when Random-WSSA successfully constructs a robust solution. Finally, we showcase the effectiveness of these algorithms in a variety of relevant uses within the context of Earth-observing LEO constellations which estimate atmospheric weather conditions and provide Earth coverage.

Adding colour to ion-selective membranes.

An idea of using ion-exchanger salt containing optically active cations to prepare ion-selective membranes is proposed. Although the presence of an ion-exchanger in the composition of neutral ionophore based sensors is necessary, the choice of available salts for cation-selective sensors preparation, is usually limited to sodium or potassium compounds. In this work we propose application of an alternative salt, using a cation optically active both in absorption and emission mode as a mobile one. Thus, coloured ion-selective membranes can be obtained. This in turn opens new possibilities of monitoring the state of the receptor layer as well as allows direct analytical application of ion-selective membranes in simple optical mode with all benefits related to eliminating the necessity of using reference electrodes. As a model system Nile blue derivative of tetrakis[3,5-bis(trifluoromethyl)phenyl]borate ion-exchanger was prepared and used to obtain potassium or calcium selective sensors. Selective exchange of ions between the membrane and solution, leading to an increase in optical signal of the solution, can be used to quantify the presence of analyte ions. Thus the sensor pretreatment process is becoming a source of analytical information. The applicability of this approach was verified in determining the presence of potassium ions in the vast majority of interfering ions, e.g. present as impurities in the reagent grade calcium chloride. The resulting potassium ions contents was well comparable with values obtained in course of ICP-MS approach.

Adsorption and Sensing Performance of Transition Metal Decorated Graphene Quantum Dots for AsH3, NH3, PH3, and H2S.

We have conducted a systematic study employing density functional theory (DFT) and quantum theory of atoms in molecules (QTAIM) to explore the gas sensing capabilities of nitrogen-doped single vacancy graphene quantum dots (SV/3N) decorated with transition metals (TM = Mn, Co, Cu). We have studied the interactions between TM@SV/3N and four different target gases (AsH3, NH3, PH3, and H2S) through the computation of adsorption energies, charge transfer, noncovalent interaction, density of states, band gap, and work function for 12 distinct adsorption systems. Our comprehensive analysis included an in-depth assessment of sensors' stability, sensitivity, selectivity, and reusability for practical applications. Our findings indicate that the Co@SV/3N surface strongly interacts with PH3, with the highest adsorption energy (-1.15 eV). It shows remarkable sensitivity and selectivity toward PH3, making it a promising candidate for PH3 gas sensing applications. Similarly, Mn@SV/3N exhibits high sensitivity and selectivity toward NH3, positioning it as a suitable candidate for NH3 gas sensing applications. We believe this study will provide valuable theoretical guidance for developing TM@SV/3N-based gas sensors.

Nanomaterials-Enabled Sensors for Detecting and Monitoring Chemical Warfare Agents.

Despite their restrictions under international treaties, many chemical warfare agents (CWAs) and their toxic analogues are still used in various industrial sectors such as agriculture and chemical manufacturing. Thus, the need for sensitive and selective CWA detection remains critical. Commercially available detection methods, while accurate, are often bulky, expensive, and require specialized personnel. Sensors incorporating nanomaterials present a promising alternative, offering rapid, portable, and cost-effective detection due to their unique properties, such as high surface area and tunable reactivity. This review covers the four main CWA categories: nerve agents, blister agents, blood agents, and choking agents, highlighting recent progress in nanosensor development for each category. It discusses various sensing mechanisms employed, including fluorescence, colorimetry, chemiresistivity, electrochemistry, and Raman spectroscopy. Despite these advancements, challenges remain, particularly regarding the scalability, stability, and selectivity of nanomaterials-based sensors in complex environments. The review concludes by emphasizing the need to address these challenges and explore novel nanomaterials, the development of scalable nanomanufacturing techniques, and the integration of artificial intelligence to fully unlock the potential of nanomaterials in CWA sensing for homeland security and personal safety.

CubeSat Imaging Payload Design for Environmental Monitoring of Greenland

Climate change affects the Earth’s ecosystem, and understanding human impact on sparsely populated polar regions is crucial, especially in glacial dynamics. Nanosatellites can play an essential role in monitoring remote regions due to their flexibility in adding remote sensors for Earth observation. However, they have hardware constraints such as physical space limitations, low power generation, and low bandwidth, as well as environmental challenges of vacuum, heat, cold and radiation. This paper details the preliminary system design of an imaging payload integrated into a nanosatellite for monitoring field study sites in Greenland. The payload is capable of supporting advanced image processing and Machine Learning (ML) applications. Key design elements, including the selection of imaging sensors, onboard processing units, and data transmission systems, which are optimized for the constraints of a nanosatellite platform, are presented. As a result, we presented a novel imaging payload system design, which shows a significant step towards leveraging space technology for environmental research.