Development Of Sensor System Research Articles

Engineering a Graphene Quantum Dot‐Enhanced Surface Plasmon Resonance Sensor for Ultra‐Sensitive Detection of Hg2⁺ Ions

AbstractThe contamination of soil and water by heavy metals poses a significant environmental and public health concern worldwide. To address this issue, a novel graphene quantum dot (GQD)‐based surface plasmon resonance (SPR) sensor is developed for the detection of mercury ions (Hg2+), a notorious heavy metal pollutant. The thiol and amine‐functionalized GQDs (S,N‐GQDs), synthesized via pyrolysis of citric acid and L‐cysteine, are directly immobilized onto the SPR chip surface without prior pretreatment, demonstrating their potential as efficient sensing materials. The SPR sensor exhibits high sensitivity and selectivity toward Hg2+ ions, as confirmed by kinetic binding analysis and isotherm modeling. The Langmuir isotherm model, which accurately describes the interactions between Hg2+ and S,N‐GQDs, provides insights into the sensor's mechanism of action. Furthermore, the sensor demonstrates robustness and reusability, with recoveries ranging from 98% to 104% over multiple cycles of analysis. Given the presence of contaminants in tap water, the developed sensor system holds significant importance for environmental monitoring and public health protection, offering a rapid, accurate, and cost‐effective solution for detecting Hg2+ ions in such samples. Overall, this study represents a significant advancement in the field of heavy metal detection, with potential implications for addressing environmental pollution and ensuring water quality.

Validation of a smartphone-compatible MIP-based sensor for bisphenol A determination in wastewater samples.

A handheld smartphone-compatible molecularly imprinted polymer (MIP)-based sensor was developed for the analysis of bisphenol A (BPA) in wastewater samples. Sensing elements based on ethylene glycol methacrylate phosphate (EGMP)-containing MIP films were designed and optimized using molecular dynamics simulations. The highly porous MIP films were synthesized via in situ polymerization, employing a fragment-based approach. The colorimetric response was based on the 4-aminoantipyrine method, while the MIP films were further utilized to detect BPA with a smartphone. The proposed sensor exhibited a wide linear range from 5 to 250μM, with a limit of detection (LOD) of 5μM (S/N = 3). Furthermore, the designed analytical system demonstrated excellent analytical performance in terms of selectivity, stability, and reproducibility. During sensor validation, real wastewater samples were successfully tested for BPA, showcasing the feasibility of the smartphone-compatible MIP-based sensor. Recovery values of 87.1-114.6% underscored the efficacy and reliability of the developed sensor system.

Comparing the Ground Reaction Forces, Toe Clearances, and Stride Lengths of Young and Older Adults Using a Novel Shoe Sensor System

In this study, we developed a lightweight shoe sensor system equipped with four high-capacity, compact triaxial force sensors and an inertial measurement unit. Remarkably, this system enabled measurements of localized three-directional ground reaction forces (GRFs) at each sensor position (heel, first and fifth metatarsal heads, and toe) and estimations of stride length and toe clearance during walking. Compared to conventional optical motion analysis systems, the developed sensor system provided relatively accurate results for stride length and minimum toe clearance. To test the performance of the system, 15 older and 8 young adults were instructed to walk along a straight line while wearing the system. The results reveal that compared to the young adults, older adults exhibited lower localized GRF contributions from the heel and greater localized GRF contribution from the toe and fifth metatarsal locations. Furthermore, the older adults exhibited greater variability in their stride length and smaller toe clearance with greater variability compared to the young adults. These results underscore the effectiveness of the proposed gait analysis system in distinguishing the gait characteristics of young and older adults, potentially replacing traditional motion capture systems and force plates in gait analysis.

Harmony through Order: Unveiling the Pivotal Role of Structural Precision in Assembling Phthalocyanines and Porphyrins

This presentation will focus on the strategic creation of 'Designer Solids' using chromophoric building blocks, in particular porphyrins and phthalocyanines. By employing the MOF-approach, suitable photoactive compounds are assembled into crystalline materials. Highlighted will be the latest advancements in the creation of MOF-based devices, covering a range of technologies such as electrochemical, photoelectrochemical, photovoltaic, and sensing applications, again emphasizing the use of porphyrin and phthalocyanine linkers. The talk will also explore the innovative use of internal interfaces in MOF heterostructures for enhanced photon upconversion and diode production.Addressing the challenge of forming stable and consistent electrical contacts with MOF materials, as well as producing high optical quality MOF thin films, we have developed a layer-by-layer (LbL) deposition technique [1] for crafting well-defined, highly oriented, and uniform MOF thin films on suitably prepared conductive and/or transparent surfaces. These films, known as SURMOFs, exhibit promising characteristics, particularly for optical uses [2]. We will detail the principles behind SURMOF construction and present findings from comprehensive studies on their electrical and photophysical behaviors, both in their original state and when their pores are filled with functional additives.[3,4] Additionally, the effects on band structure in crystalline porphyrin arrays constructed via the SURMOF method will be examined.[5]The lecture will also delve into further applications achieved by incorporating nanoparticles or quantum dots into MOFs, and the creation of molecular solids without inversion symmetry for second harmonic generation (SHG).[6]In the final segment, the suitability of SURMOFs for automated research methods will be discussed. Leveraging robotic systems governed by machine learning (ML) algorithms, we can effectively fine-tune thin film attributes such as orientation, crystallinity, and surface smoothness. [7,8] This aspect will be particularly emphasized in relation to the development of SURMOF-based sensor systems.

Gap distance sensing for non-magnetic medium based on magnetoelectric effect under spatial separation condition

This paper presents a novel non-contact spatial gap distance sensing (GDS) method that can provide distance information in spatial separation conditions. In many applications, such as enclosed environments, it could not provide the desired measurement of gap distance of internal non-magnetic medium due to the constraints of physical barriers and poor accessibility. Therefore, a non-invasive sensing system is designed to measure spatial gap distance for non-magnetic medium. The developed sensor system consists of a pair of heteropolar permanent magnets (PMs), a non-magnetic medium, a magnetostrictive-piezoelectric composite unit and an external space, which has the function of spatial separation measurement. By exploiting the magnetoelectric effect, the magneto-machine-electric conversion is achieved by sensing the spatial magnetic field generated by the heteropolar PMs. The coupling modeling, analysis and calibration of sensing system are conducted, and the system prototype is designed and manufactured. Additionally, the performances of the GDS are experimentally validated. Static gap distance (plate thickness) measurements of the plate and variable gap distance (instant water height) measurements of water are performed, and resolution, vibration, and drift tests are carried out. The results show the accuracy and stability of non-contact spatial gap distance detection for non-magnetic medium, highlighting its potential in various applications.

Geometry-modulated all organic 3D printed smart PLA fibers for flextension amplified giant mechanical energy harvesting and Machine learning assisted pressure mapping

Piezoelectret sensors offer a promising avenue for harvesting abundant mechanical energy in the environment, providing a sustainable power solution for low-powered electronics. However, effectively harnessing large impact mechanical energy remains a challenge. In this study, we introduce a novel 3D cylindrical self-powered piezoelectret sensor capable of converting irregular axial impact forces into uniform radial pressures, enabling efficient large-scale impact mechanical energy harvesting. The cylindrical piezoelectret sensor, operating via a flextensional mechanism, was fabricated using 3D printed poly(lactic acid) (PLA) fibers as the piezoelectret layer and poly(3,4-ethylenedioxythiophene) (PEDOT)-coated PLA as the electrode layer. Our cylindrical sensor demonstrates the output voltage, and current of ∼ 11.4 V and 7.2 μA, representing remarkable improvement of 250 % and 160 % respectively, compared to flat fiber membrane device. Furthermore, we leverage IoT connectivity and machine learning techniques to utilize the fabricated robotic sensor for pressure mapping applications. Through machine learning algorithms, we achieve a remarkable accuracy of 100 % in character identification based on sensor data. This work highlights the potential of organic robotic sensors for efficient energy harvesting and intelligent pressure mapping applications, paving the way for the development of sustainable and adaptive sensor systems for various real-world scenarios.

In Situ High-Precision Measurement of Deep-Sea Dissolved Methane by Quartz-Enhanced Photoacoustic and Light-Induced Thermoelastic Spectroscopy.

Rapid and accurate realization of in situ analysis of deep-sea dissolved gases imperative to the study of ecological geology, oil and gas resource exploration, and global climate change. Herein, we report for the first time the deep-sea dissolved methane (CH4) in situ sensor based on quartz-enhanced photoacoustic and light-induced thermoelastic spectroscopy. The developed sensor system has a volume of φ120 mm × 430 mm and a power consumption of 7.6 W. The sensor, in the manner of frequency division multiplexing, is able to simultaneously measure the photoacoustic signals and light-induced thermoelastic signals, which can accurately correct laser-intensity induced influence on concentration. The spectral response of CH4 concentration varying from 0.01 to 5% is calibrated in detail based on the pressure and temperature in the application environment. The trend of the photoacoustic signal of CH4 at different water molecule (H2O) concentrations is investigated. An Allan variance analysis of several hours demonstrates a minimum detection limit of 0.21 ppm for the CH4 spectrometer. The sensor combined with the gas-liquid separation and enrichment unit is integrated into a compact marine standalone system. Since the specifically designed photoacoustic cell has a volume of only 1.2 mL, the time response for dissolved CH4 detection is reduced to 4 min. Furthermore, the sensor is successfully deployed in the vicinity of the "HaiMa" cold seeps at 1380 m underwater in the South China Sea, completing three consecutive days of measurements of dissolved CH4.

Transformer oil temperature sensing utilizing bundle plastic optical fiber sensor

A bundle plastic optical fiber (POF) that works based on an intensity modulation technique is experimentally demonstrated to sense the temperature of transformer oil. The sensor was developed using a bundle POF that is located perpendicular to an aluminum reflective film with an airgap cavity between these two elements. The simplicity of the architecture allows the development of an economical optical sensor system. To avoid interference effects by other substances in the oil, the sensor head is encapsulated with a metal protecting tube. The temperature measurement was realized in this study by monitoring the output light intensity in the visible light spectrum. For linearity range from 40 °C to 75 °C, the tested sensor exhibits a sensitivity of 0.0064 °C−1, a linearity coefficient of 0.95 and a resolution of 1.56 °C. These results demonstrate the suitability of the developed sensor for temperature oil monitoring in an electrical power transformer system.

Advancements in Energy Harvesting: Piezoelectric, Triboelectric, Pyroelectric, and Magnetoelectric Technologies for Self-powered Sensor Systems

In the era of Internet of Things (IoT) advancements, millions of sensors and electronic devices are interconnected through the internet. A reliable power source is required for their continuous operation. Self-powered sensor systems have emerged as promising solutions for various applications including wearable devices and environmental monitoring, benefiting both society and the environment. These systems can maintain themselves by eliminating the need for external power sources or frequent battery replacements and converting discarded energy sources around them into electrical energy. Among various energy harvesting technologies, piezoelectric, triboelectric, pyroelectric, and magnetoelectric mechanisms have garnered significant attention due to their ability to convert mechanical, frictional, thermal, and magnetic energy into electrical power, respectively. This review aims to provide a comprehensive overview of recent advances in these technologies for self-powered sensor systems, catering to a wide audience. It explores fundamental principles of their-energy harvesting mechanisms, highlighting their strengths, limitations, and potential applications. In addition, this review discusses challenges related to the development of nanogenerator-based self-powered sensor systems and presents new opportunities for their advancement.

Development of an energy-autonomous eddy current sensor system for in-situ component monitoring

Energy-autonomous non-destructive testing (NDT) systems provide new opportunities for structural health monitoring. An NDT system that is energy-autonomous and transmits the recorded data wirelessly can be used in places that are not easily accessible for inspection. In addition, it can provide for continuous monitoring, which can improve the operational safety of various components and structures. This study presents a concept that enables energy-autonomous and wireless structural health monitoring using a splined shaft as a demonstrator. To monitor mechanical overloads, a material sensor is applied to critical areas of these components by a laser heat treatment. The structural change that occurs as a result of an overload can be monitored by the developed smart sensor system. This system consists of a material sensor, a low-cost eddy current sensor, an energy-efficient evaluation unit with an ultra-low-power microcontroller unit (MCU) and a Long-Range Wide Area Network (LoRaWAN) data transmission unit. Wireless data transmission via LoRaWAN provides access to the Industrial Internet of Things network, enabling the collection of NDT data from multiple smart sensors in a cloud and the processing of these data to assess the condition of the component from any location. It is shown that the MCU-based eddy current testing system is able to generate a similar signal quality for material characterisation compared to an industrial eddy current testing system, despite a much more compact design and a significantly higher energy efficiency. The energy autonomy of the smart sensor system is achieved by an energy harvesting system integrated into the splined shaft. In addition, three different testing scenarios are presented, which differ mainly in the scope of data processing and evaluation on the MCU, and thus also in the energy required.

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

AbstractPeriodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900–1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed ‘niGLUC-2.0v’. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland–Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification.

In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data.

Development of optical fiber strain sensor system based on machine learning and polarization

Based on the principle that the polarization state of light propagating in a single-mode fiber changes with external strains, an optical fiber sensor system based on machine learning and polarization for multi-point strain measurement is proposed. To address the influence of the front sensor on the rear sensor and to minimize interference from unrelated inputs, we have employed a data processing method that constructs an individual neural network model for each sensor. This approach uses the polarization state of the reflected light of the sensors as the neural networks’ input and the sensors’ rotation angles as the output, training the designed neural networks for learning. The trained neural networks produce predicted outputs that demonstrate high consistency with the experimental data, achieving an average prediction accuracy of 99% on test data. These results validate the effectiveness of our sensor system and data processing method.

Gold nanoclusters-based fluorescence sensor array for herbicides qualitative and quantitative analysis

Herbicides have been extensively used around the world, which poses a potential hazard to humans and wildlife. Accurate detection of herbicides is crucial for the environment and human health. Herein, a simple and sensitive fluorescence sensor array was constructed for discrimination and identification of herbicides. Fluorescent gold nanoclusters modified with 11-mercaptoundecanoic acid or reduced glutathione were prepared, respectively. Metal ions quenched the fluorescence of nanoclusters through coordination and leading to the aggregation of gold nanoclusters. The addition of auxin herbicides (2,4-dichlorophenoxyacetic acid, 2-methyl-4-chlorophenoxyacetic acid, decamba, picloram, quinclorac) restored the fluorescence of nanoclusters with different degrees. The mechanism study showed auxin herbicides can bind with metal ions and re-disperse the gold nanoclusters from the aggregation state. The "on-off-on" fluorescent sensor array was constructed basic on above detection mechanism. Combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA) methods, auxin herbicides are well separated on 2D/3D PCA score plots and HCA dendrogram in the range of 40–500 μm. In addition, the fluorescence sensor array performed successful in detecting real samples and blind samples. The developed sensor system shows a promising in practical detection of herbicides.

Towards a Miniaturized Photoacoustic Sensor for Transcutaneous CO2 Monitoring.

A photoacoustic sensor system (PAS) intended for carbon dioxide (CO2) blood gas detection is presented. The development focuses on a photoacoustic (PA) sensor based on the so-called two-chamber principle, i.e., comprising a measuring cell and a detection chamber. The aim is the reliable continuous monitoring of transcutaneous CO2 values, which is very important, for example, in intensive care unit patient monitoring. An infrared light-emitting diode (LED) with an emission peak wavelength at 4.3 µm was used as a light source. A micro-electro-mechanical system (MEMS) microphone and the target gas CO2 are inside a hermetically sealed detection chamber for selective target gas detection. Based on conducted simulations and measurement results in a laboratory setup, a miniaturized PA CO2 sensor with an absorption path length of 2.0 mm and a diameter of 3.0 mm was developed for the investigation of cross-sensitivities, detection limit, and signal stability and was compared to a commercial infrared CO2 sensor with a similar measurement range. The achieved detection limit of the presented PA CO2 sensor during laboratory tests is 1 vol. % CO2. Compared to the commercial sensor, our PA sensor showed less influences of humidity and oxygen on the detected signal and a faster response and recovery time. Finally, the developed sensor system was fixed to the skin of a test person, and an arterialization time of 181 min could be determined.

Decoding Biomechanical Cues Based on DNA Sensors.

Biological systems perceive and respond to mechanical forces, generating mechanical cues to regulate life processes. Analyzing biomechanical forces has profound significance for understanding biological functions. Therefore, a series of molecular mechanical techniques have been developed, mainly including single-molecule force spectroscopy, traction force microscopy, and molecular tension sensor systems, which provide indispensable tools for advancing the field of mechanobiology. DNA molecules with a programmable structure and well-defined mechanical characteristics have attached much attention to molecular tension sensors as sensing elements, and are designed for the study of biomechanical forces to present biomechanical information with high sensitivity and resolution. In this work, a comprehensive overview of molecular mechanical technology is presented, with a particular focus on molecular tension sensor systems, specifically those based on DNA. Finally, the future development and challenges of DNA-based molecular tension sensor systems are looked upon.

Selective sensing of cyanide ions: impact of molecular design and assembly on the response of π-conjugated acylhydrazone compounds.

This study investigates the sensing properties of two distinct compounds, denoted as 1 and 2, featuring acylhydrazone units. Spectroscopic analyses reveal the disruption of the supramolecular assembly upon binding with cyanide ions, consequently due to the hydrogen bonding interaction with acylhydrazone units. This leads to a ratiometric, color-changing response of both the compounds specifically towards cyanide ions. The investigation sheds light on the reversible nature of the cyanide-probe interaction and highlights the potential for reusability in cyanide ion detection. Moreover, compound 1, distinguished by its long alkyl chains, displays a superior response to CN- ions (∼4-fold larger signal), in contrast to compound 2. However, interference was observed from other basic anions, such as F- and AcO-. The research suggests the dominating role of supramolecular assembly, intermolecular interaction, and local hydrophobic environment around the binding sites on the analytical performance of the probe molecules. The findings underscore the significance of structural design and molecular assembly in dictating the selectivity and sensitivity of compounds, offering valuable insights for the development of efficient sensor systems in diverse real-world applications.

Development of Fe (Iii) Sensor System Using Carbon Nanodots Derived From <i>Plectranthus amboinicus</i>

Carbon Dots (CDs) are a course of carbon nanomaterials just under 10 nm in dimension endowed with signature optical and electronic properties finding applications in sensors, photocatalysis, biomedical as well as optoelectronics. Single stroke hydrothermal synthesis method seems to have been adopted as the generation of nanocarbon dots from the Indian medicinal plant, Plectranthus amboinicus. Advanced characterisation methods such as UV- Visible absorption spectroscopy, fluorescence spectroscopy, Fourier transform infrared spectroscopy and HR TEM study have been adopted to confirm the structure of carbon nanoparticles. The dependence on the excitation of photoluminescence emission behaviour of CDs have been confirmed using PL spectroscopy. The reaction between the many metal ions with the photoluminescence of CDs are studied and found a striking interaction with Fe (III) ions. The equation from Stern-Volmer is used to study the mechanism of extinction involved in the sensing action of carbon dots and the threshold for recognition is found to be 0.30 μM. The existence of surface functional groups leading to the complexation with Fe (III) ions can primarily be the reason for the observed sensing application. The design and development of eco-friendly sensor systems for Iron metal which is also considered as an essential mineral for human health for its application in biomedical and environmental applications is discussed in this paper.

Deep Learning of Sensor Data in Cybersecurity of Robotic Systems: Overview and Case Study Results

Recent technological advances have enabled the development of sophisticated robotic and sensor systems monitored and controlled by algorithms based on computational intelligence. The deeply intertwined and cooperating devices connected to the Internet and local networks, usually through wireless communication, are increasingly used in systems deployed among people in public spaces. The challenge is to ensure that physical and digital components work together securely, especially as the impact of cyberattacks is significantly increasing. The paper addresses cybersecurity issues of mobile service robots with distributed control architectures. The focus is on automatically detecting anomalous behaviors possibly caused by cyberattacks on onboard and external sensors measuring the robot and environmental parameters. We provide an overview of the methods and techniques for protecting robotic systems. Particular attention is paid to our technique for anomaly detection in a service robot’s operation based on sensor readings and deep recurrent neural networks, assuming that attacks result in the robot behaving inconsistently. The paper presents the architecture of two artificial neural networks, their parameters, and attributes based on which the potential attacks are identified. The solution was validated on the PAL Robotics TIAGo robot operating in the laboratory and replicating a home environment. The results confirm that the proposed system can effectively support the detection of computer threats affecting the sensors’ measurements and, consequently, the functioning of a service robotic system.

Blood pressure monitoring with piezoelectric bed sensor systems

ObjectiveBallistocardiography (BCG) measures vital signals without direct contact, which shows great potential for continuous sleep monitoring. This study proposed a BCG-based blood pressure (BP) estimation algorithm framework using piezoelectric bed sensor systems. Methods: To derive BP, a combination of morphological features, spectral features, and fractal dimensions of the BCG signal were utilized. Bayesian neural networks were employed to weigh the contribution of each feature and generate input-dependent coefficients. Two data balancing procedures were tested, and the proposed system's effectiveness was evaluated, including in-hospital patients and healthy subjects. Transfer learning techniques were employed to further improve the system's performance and showcase the similarity between bed systems with different piezoelectric sensors. Results: The combination of morphological and spectral features significantly improves BP estimation accuracy. The fractal dimension captures short-term BP fluctuations, improving intra-subject BP trend estimation. For young and healthy subjects, calibration-free mean absolute error (MAE) for systolic BP (SBP) and diastolic BP (DBP) is 4.20/4.25 mmHg at a 5-second time resolution. In the case of in-hospital patients, the best MAE for overnight SBP/DBP is 9.96/7.59 mmHg. Transfer learning, combined with data balancing techniques, substantially enhances BP estimation accuracy for in-hospital patients, providing insights for future algorithm designs. Conclusion: The study concludes that bed-sensor systems can track BP changes in relatively healthy subjects. However, BCG alone may not be sufficient for subjects with severe cardiovascular dysfunctions to obtain reliable BP readings. Transfer learning and proper data balancing may facilitate the fast development of new bed sensor systems with similar technologies.