Sensors For Monitoring Research Articles

Permeability Sensors for Magnetic Steel Structural Health Monitoring

Permeability Sensors for Magnetic Steel Structural Health Monitoring

Key Fundamentals and Examples of Sensors for Human Health: Wearable, Non-Continuous, and Non-Contact Monitoring Devices.

The increasing demand for personalized healthcare, particularly among individuals requiring continuous health monitoring, has driven significant advancements in sensor technology. Wearable, non-continuous monitoring, and non-contact sensors are leading this innovation, providing novel methods for monitoring vital signs and physiological data in both clinical and home settings. However, there is a lack of comprehensive comparative studies assessing the overall functionality of these technologies. This paper aims to address this gap by presenting a detailed comparative analysis of selected wearable, non-continuous monitoring, and non-contact sensors used for health monitoring. To achieve this, we conducted a comprehensive evaluation of various sensors available on the market, utilizing key indicators such as sensor performance, usability, associated platforms functionality, data management, battery efficiency, and cost-effectiveness. Our findings highlight the strengths and limitations of each sensor type, thus offering valuable insights for the selection of the most appropriate technology based on specific healthcare needs. This study has the potential to serve as a valuable resource for researchers, healthcare providers, and policymakers, contributing to a deeper understanding of existing user-centered health monitoring solutions.

Highly sensitive split ring resonator-based sensor for quality monitoring of edible oils

This research presents the design and analysis of a compact metamaterial (MTM)-based star-shaped split-ring resonator (SRR) enclosed in a square, constructed on a cost-effective substrate for liquid chemical sensing applications. The designed structure has dimensions of 10 × 10 mm2 and is optimized for detecting adulteration in edible oils. When the sample holder is filled with different percentages of oil samples, the resonance frequency of the MTM-based SRR sensor shift significantly. The measured results demonstrate that the proposed SRR sensor is superior in terms of sensitivity and quality factor compared to studies in the literature. The proposed sensor shows superior performance in sensitivity and quality factor (Q-factor) compared to existing sensors in the literature. It exhibits a remarkable sensitivity of 0.92 with a frequency shift of 760 MHz for adulteration detection, which is higher than sensors with shifts ranging from 140 to 600 MHz reported in previous studies. Additionally, the design has a high Q-factor of 149, indicating its efficiency in determining adulteration in edible oils. Additionally, the error rate in detecting adulteration is minimal at 3.1%, a significant improvement over prior sensors, which have error rates as high as 8%. These enhancements highlight the sensor’s potential in applications requiring precise, efficient, and cost-effective detection of edible oil adulteration, thus offering a significant advancement in both performance and practical utility over traditional methods.

Skin-Interfaced Wearable Sensor for Long-Term Reliable Monitoring of Uric Acid and pH in Sweat.

Wearable sweat sensors offering real-time monitoring of biomarker levels suffer from stability and accuracy issues, primarily due to low biomarker concentrations, fluctuating sweat pH, and material detachment from sensor deformation. Here, we developed a wearable sensing system integrated with two advanced electrodes and a flexible microchannel for long-term reliable monitoring of sweat pH and uric acid (UA). By printing the ink doped with nanomaterials (Co3O4@CuCo2O4 and polyaniline), we achieved highly stable electrodes for the direct analysis of perspiration, without additional surface modification. Additionally, real-time pH analysis provided a means for sensitivity calibration, reducing the effect of individual metabolism and exercise intensity. As a result, the wearable sensing system for effective gout management was validated by accurately tracking the UA fluctuations in serum and sweat of hyperuricemia patients and healthy individuals. These findings offer a reliable method for tracking biomarkers to assess personal health.

Real-time wastewater quality monitoring by fluorescence sensors: Validation for COD and CEC monitoring and implication for carbon footprint reduction.

This study investigated the applicability of a protein-like fluorescence sensor for wastewater quality monitoring. Several wastewater matrices, including raw, primary, secondary and tertiary effluents from three different wastewater treatment plants were used. Furthermore, the sensor was tested for the monitoring of quaternary effluent in a pilot scale plant installed downstream of a water reuse facility. The pilot plant involved advanced oxidation processes (AOPs) and granular activated carbon (GAC) adsorption. Corrections on excitation/emission matrices (EEMs), including Inner Filter Effect (IFE) and scattering, showed no effect on linear correlation (R2=0.99) between sensor measurement and either raw or corrected benchtop protein-like fluorescence data, suggesting that for this application the signal from the sensor might be interpreted without the need for further adjustments. Furthermore, the use of quenched, diluted and filtered samples did not affect such correlations. Overall, the fluorescence sensor showed a very high capability to monitor a wide range of wastewater matrices, including raw, primary, secondary, tertiary, and quaternary effluents, providing fast information on the efficiency of the processes. The protein-like fluorescence monitoring by the real-time sensor was validated online through 9days of 24-hour continuous monitoring of tertiary wastewater effluents. The employed fluorescence sensor was validated for monitoring the removal of contaminants of emerging concern (CEC), including a wide range of pharmaceuticals, in different AOP systems (ozone and UV based). In view of the results reported in this study, possible environmental implications for the reduction of the carbon footprint have emerged: the use of fluorescence sensors may contribute to the optimization of processes and the reduction of secondary pollution.

All 3D-printed high-sensitivity adaptive hydrogel strain sensor for accurate plant growth monitoring

All 3D-printed high-sensitivity adaptive hydrogel strain sensor for accurate plant growth monitoring

Ultra-Fast Moisture Sensor for Respiratory Cycle Monitoring and Non-Contact Sensing Applications.

As human-machine interface hardware advances, better sensors are required to detect signals from different stimuli. Among numerous technologies, humidity sensors are critical for applications across different sectors, including environmental monitoring, food production, agriculture, and healthcare. Current humidity sensors rely on materials that absorb moisture, which can take some time to equilibrate with the surrounding environment, thus slowing their temporal response and limiting their applications. Here, this challenge is tackled by combining a nanogap electrode (NGE) architecture with chicked egg-derived albumen as the moisture-absorbing component. The sensors offer inexpensive manufacturing, high responsivity, ultra-fast response, and selectivity to humidity within a relative humidity range of 10-70% RH. Specifically, the egg albumen-based sensor showed negligible response to relevant interfering species and remained specific to water moisture with a room-temperature responsivity of 1.15×104. The nm-short interelectrode distance (circa 20nm) of the NGE architecture enables fast temporal response, with rise/fall times of 10/28ms, respectively, making the devices the fastest humidity sensors reported to date based on a biomaterial. By leveraging these features, non-contact moisture sensing and real-time respiratory cycle monitoring suitable for diagnosing chronic diseases such as sleep apnea, asthma, and pulmonary disease are demonstrated.

Basic Characteristics of Ionic Liquid-Gated Graphene FET Sensors for Nitrogen Cycle Monitoring in Agricultural Soil.

Nitrogen-based fertilizers are crucial in agriculture for maintaining soil health and increasing crop yields. Soil microorganisms transform nitrogen from fertilizers into NO3--N, which is absorbed by crops. However, some nitrogen is converted to nitrous oxide (N2O), a greenhouse gas with a warming potential about 300-times greater than carbon dioxide (CO2). Agricultural activities are the main source of N2O emissions. Monitoring N2O can enhance soil health and optimize nitrogen fertilizer use, thereby supporting precision agriculture. To achieve this, we developed ionic liquid-gated graphene field-effect transistor (FET) sensors to measure N2O concentrations in agricultural soil. We first fabricated and tested the electrical characteristics of the sensors. Then, we analyzed their transfer characteristics in our developed N2O evaluation system using different concentrations of N2O and air. The sensors demonstrated a negative shift in transfer characteristic curves when exposed to N2O, with a Dirac point voltage difference of 0.02 V between 1 and 10 ppm N2O diluted with pure air. These results demonstrate that the ionic liquid-gated graphene FET sensor is a promising device for N2O detection for agricultural soil applications.

Moisture-Electric Generators Working in Subzero Environments Based on Laser-Engraved Hygroscopic Hydrogel Arrays.

Moisture-electric generators (MEGs) generate power by adsorbing water from the air. However, their performance at low temperatures is hindered due to icing. In the present work, MEG arrays are developed by laser engraving techniques and a modulated low-temperature hydrogel as the absorbent material. LTH effectively captures moisture and maintains ion dissociation and migration even at subzero temperatures. Based on the double electric layer pseudocapacitance model, the oscillating circuit theory is introduced to explain the effects of moisture absorption, evaporation, and ion migration on the output current of the MEG, and the circuit calculations are matched with the experimental results. Molecular dynamics simulations indicate that LTH's low-temperature stability results from preferential hydrogen bonding between glycerol molecules and H2O, which disrupts H2O-H2O hydrogen bonds and slows water crystallization. A single MEG unit (0.25 cm2) can produce up to ∼0.8 V and ∼21.2 μW/cm2 at room temperature, and at -35 °C with 16% RH, it generates ∼0.58 V and ∼14.35 μA. MEG realizes the following applications: MEG successfully drives electronic devices in snow; arrays of 16 MEGs can power portable electronics, and 384 MEGs can achieve up to 210 V; MEG absorbs moisture in water and drives LEDs by blowing up; MEG has a flexible wearable nature; MEG is used for respiratory monitoring and photoelectric sensors.

Analisis Risiko dan Strategi Peningkatan Keselamatan Kerja di Sektor Industri Manufaktur: Tinjauan Literatur

Workplace safety in the manufacturing sector is a critical issue, given the high incidence of workplace accidents that may hurt the company and the workers. This study was conducted to analyze occupational safety risks and strategies for improvement, with a focus on the challenges faced in implementing safety policies that are often suboptimal. This research is a literature review employing a descriptive approach, where data from various secondary sources such as academic journals, reports, and official publications are systematically analyzed. The findings of the study indicate four main categories of occupational safety risks in the industrial sector: physical, chemical, ergonomic, and psychosocial risks. The application of technologies, such as automation and monitoring sensors based on the Internet of Things (IoT), has proven effective in reducing workplace accidents, but access to these technologies remains limited. Additional challenges include cost limitations, low worker awareness, weak safety culture implementation, and suboptimal enforcement of safety regulations. This study recommends the importance of collaboration between governments, companies, and workers to strengthen safety culture through investment in technology, ongoing training, and more rigorous regulation enforcement. With these measures, workplace safety in the industrial sector is expected to improve significantly and sustainably. Keywords: industrial risk, occupational safety, regulation, safety strategy

Verification and Usability of Indoor Air Quality Monitoring Tools in the Framework of Health-Related Studies

Indoor air quality (IAQ) significantly impacts human health, particularly in enclosed spaces where people spend most of their time. This study evaluates the performance of low-cost IAQ sensors, focusing on their ability to measure carbon dioxide (CO2) and particulate matter (PM) under real-world conditions. Measurements provided by these sensors were verified against calibrated reference equipment. The study utilized two commercial devices from inBiot and Kaiterra, comparing their outputs to a reference sensor across a range of CO2 concentrations (500–1200 ppm) and environmental conditions (21–25 °C, 27–92% RH). Data were analyzed for relative error, temporal stability, and reproducibility. Results indicate strong correlation between low-cost sensors (LCSs) and the reference sensor at lower CO2 concentrations, with minor deviations at higher levels. Environmental conditions had minimal impact on sensor performance, highlighting robustness to temperature and humidity within the tested ranges. For PM measurements, low-cost sensors effectively tracked trends, but inaccuracies increased with particle concentration. Overall, these findings support the feasibility of using low-cost sensors for non-critical IAQ monitoring, offering an affordable alternative for tracking CO2 and PM trends. Additionally, LCSs can assess long-term exposure to contaminants, providing insights into potential health risks and useful information for non-expert users.

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.

Integrated Solution Combining Low-Frequency Forced Oscillation Technique and Continuous Equivital Sensor Monitoring for Assessment of Non-Invasive Ambulatory Respiratory Mechanics

Early assessment of respiratory mechanics is crucial for early-stage diagnosing and managing lung diseases, leading to greater patient outcomes. Traditional methods like spirometry are limited in continuous monitoring and patient compliance as they require forced maneuvers with significant patient cooperation, which may not be available in fragile individuals. The Forced Oscillation Technique (FOT) is a non-invasive measurement method, only based on the tidal breathing at rest from the patient for a limited time period. The proposed solution integrates low-frequency FOT with continuous monitoring using Equivital (EQV) sensors to enhance respiratory mechanics information with heart rate variability. Data were collected over a two-hour period from six healthy volunteers, measuring respiratory impedance every 7 min and continuously recording physiological parameters. The best-fitting fractional-order models for impedance data were identified using genetic algorithms. This study also explores the correlation between impedance model parameters and EQV data, discussing the potential of AI tools for forecasting respiratory properties. Our findings indicate that combined monitoring techniques and AI analysis provides additional complementary information, subsequently aiding the improved evaluation of respiratory function and tissue mechanics. The proposed protocol allows for ambulatory assessment and can be easily performed in normal breathing conditions.

Research on the Application of Flexible Pressure Sensors in Wearable Devices

The potential applications of transparent, adaptable electronic devices in the human body are driving up demand for them. For flexible and wearable electronics, such as medical surveillance devices and artificial skin, pressure sensing is mandatory. The flexibility and sensitivity of pressure sensors have advanced recently owing to developments in material and device structure. Moreover, their low manufacturing costs enable widespread implementation, leading to promising application prospects. The sensors utilize different materials and structures, including capacitive pressure sensors with ionically charged CFMs, magnetic particle-infused carbon sponges, CNT-PDMS composites, and MoSe2/MWNT combinations. These sensors offer advantages such as high sensitivity, wide pressure-detecting ranges, optical transparency, and energy efficiency. They can detect both large and subtle human movements, from walking and finger bending to breathing and eye blinking. The fabrication methods range from simple and cost-effective processes to more complex techniques like photolithography. Many of the sensors incorporate carbon nanotubes (CNTs) in various forms, exploiting their excellent electrical properties. The review highlights the potential of these sensors for healthcare monitoring, clinical diagnosis, and integration into wearable electronics, emphasizing their flexibility, biocompatibility, and ability to provide real-time, non-invasive measurements of physiological signals. Moreover, their low manufacturing costs enable widespread implementation, leading to promising application prospects.

Chemical Detection Using Mobile Platforms and AI-Based Data Processing Technologies

The development of reliable gas sensors is very important in many fields such as safety, environment, and agriculture, and is especially essential for industrial waste and air pollution monitoring. As the performance of mobile platforms equipped with sensors such as smartphones and drones and the technologies supporting them (wireless communication, battery performance, data processing technology, etc.) are spreading and improving, a lot of efforts are being made to perform these tasks by using portable systems such as smartphones or installing them on unmanned wireless platforms such as drones. For example, research is continuously being conducted on chemical sensors for field monitoring using smartphones and rapid monitoring of air pollution using unmanned aerial vehicles (UAVs). In this paper, we review the measurement results of various chemical sensors available on mobile platforms including drones and smartphones, and the analysis of detection results using machine learning. This topic covers a wide range of specialized fields such as materials engineering, aerospace engineering, physics, chemistry, environmental engineering, electrical engineering, and machine learning, and it is difficult for experts in one field to grasp the entire content. Therefore, we have explained various concepts with relatively simple pictures so that experts in various fields can comprehensively understand the overall topics.

Design of a System for Driver Drowsiness Detection and Seat Belt Monitoring Using Raspberry Pi 4 and Arduino Nano

This research explores the design of a system for monitoring driver drowsiness and supervising seat belt usage in interprovincial buses. In Peru, road accidents involving long-distance bus transportation amounted to 5449 in 2022, and the human factor plays a significant role. It is essential to understand how the use of non-invasive sensors for monitoring and supervising passengers and drivers can enhance safety in interprovincial transportation. The objective of this research is to develop a system using a Raspberry Pi 4 and Arduino Nano that allows for the storage of monitoring data. To achieve this, a conventional camera and MediaPipe were used for driver drowsiness detection, while passenger supervision was carried out using a combination of commercially available sensors as well as custom-built sensors. RS485 communication was utilized to store data related to both the driver and passengers. The simulations conducted demonstrate a high level of reliability in detecting driver drowsiness under specific conditions and the correct operation of the sensors for passenger supervision. Therefore, the proposed system is feasible and can be implemented for real-world testing. The implications of this research suggest that the system’s cost is not a barrier to its implementation, thus contributing to improved safety in interprovincial transportation.

A Prediction Model for Methane Concentration in the Buertai Coal Mine Based on Improved Black Kite Algorithm–Informer–Bidirectional Long Short-Term Memory

Accurate prediction of methane concentration in mine roadways is crucial for ensuring miner safety and enhancing the economic benefits of mining enterprises in the field of coal mine safety. Taking the Buertai Coal Mine as an example, this study employs laser methane concentration monitoring sensors to conduct precise real-time measurements of methane concentration in coal mine roadways. A prediction model for methane concentration in coal mine roadways, based on an Improved Black Kite Algorithm (IBKA) coupled with Informer-BiLSTM, is proposed. Initially, the traditional Black Kite Algorithm (BKA) is enhanced by introducing Tent chaotic mapping, integrating dynamic convex lens imaging, and adopting a Fraunhofer diffraction search strategy. Experimental results demonstrate that the proposed improvements effectively enhance the algorithm’s performance, resulting in the IBKA exhibiting higher search accuracy, faster convergence speed, and robust practicality. Subsequently, seven hyperparameters in the Informer-BiLSTM prediction model are optimized to further refine the model’s predictive accuracy. Finally, the prediction results of the IBKA-Informer-BiLSTM model are compared with those of six reference models. The research findings indicate that the coupled model achieves Mean Absolute Errors (MAE) of 0.00067624 and 0.0005971 for the training and test sets, respectively, Root Mean Square Errors (RMSE) of 0.00088187 and 0.0008005, and Coefficient of Determination (R2) values of 0.9769 and 0.9589. These results are significantly superior to those of the other compared models. Furthermore, when applied to additional methane concentration datasets from the Buertai Coal Mine roadways, the model demonstrates R2 values exceeding 0.95 for both the training and test sets, validating its excellent generalization ability, predictive performance, and potential for practical applications.

Advancements in Superhydrophobic Paper-Based Materials: A Comprehensive Review of Modification Methods and Applications.

Superhydrophobic paper-based functional materials have emerged as a sustainable solution with a wide range of applications due to their unique water-repelling properties. Inspired by natural examples like the lotus leaf, these materials combine low surface energy with micro/nanostructures to create air pockets that maintain a high contact angle. This review provides an in-depth analysis of recent advancements in the development of superhydrophobic paper-based materials, focusing on methodologies for modification, underlying mechanisms, and performance in various applications. The paper-based materials, leveraging their porous structure and flexibility, are modified to achieve superhydrophobicity, which broadens their application in oil-water separation, anti-corrosion, and self-cleaning. The review describes the use of these superhydrophobic paper-based materials in diagnostics, environmental management, energy generation, food testing, and smart packaging. It also discusses various superhydrophobic modification techniques, including surface chemical modification, coating technology, physical composite technology, laser etching, and other innovative methods. The applications and development prospects of these materials are explored, emphasizing their potential in self-cleaning materials, oil-water separation, droplet manipulation, and paper-based sensors for wearable electronics and environmental monitoring.

Simulation Research on Data Acquisition and Supervisory Control System of Distribution Network Based on Vector Regression Model

With the gradual maturity of deep learning and the Internet of Things, a smart grid is urgently needed to be established. For the intellectualization of the power grid, non-intrusive load monitoring (NILM) technology is the key step of intellectualization. The traditional load monitoring method is an invasive scheme, in which monitoring sensors are usually installed at the output end of each grid load. This method requires a lot of manpower and material resources in terms of equipment installation and maintenance, which is difficult to sustain. Therefore, monitoring electrical appliances are installed at the entrance of the distribution network to decompose the classification and operation status of the individual electrical appliances in the grid. Aiming at the problem of long-term multi-state electrical apparatus monitoring, this paper mainly optimizes the two optimal deep learning models Seq2Seq and WindowGRU based on activation function optimization, regular function optimization and neural network structure optimization. PReLU, Leaky-ReLU and ELU are used and tested. For the optimization based on regular functions, the value of F1-score is 0.9700, which is 0.1534 more than the optimization algorithm Seq2SeqLR in this paper. Meanwhile, it can also reach 0.8100 for other devices, which is 0.2373 more than the nilmTCN designed in this paper. The Recall value of the identification is improved to 0.6673, which is significantly higher than that of other models. It is proved that the research contents of this paper can improve the effective analysis of load energy consumption data, and can minimize unnecessary energy consumption to achieve the purpose of saving electricity.

Conductive Eutectogels Fabricated by Dialdehyde Xylan/Liquid Metal-Initiated Rapid Polymerization for Multi-Response Sensors and Self-Powered Applications.

Conductive eutectogels have emerged as candidates for constructing functional flexible electronics as they are free from the constraints posed by inherent defects associated with solvents and feeble network structures. Nevertheless, developing a facile, environmentally friendly, and rapid polymerization strategy for the construction of conductive eutectogels with integrated multifunctionality is still immensely challenging. Herein, a conductive eutectogel is fabricated through a one-step dialdehyde xylan (DAX)/liquid metal (LM)-initiated polymerization of a deep eutectic solvent. DAX acts as a stabilizer for the preparation of LM nanodroplets and plays a crucial role in facilitating ultrafast gelation (less than 2 min) by virtue of its reducing dialdehyde groups. Notably, this fabrication strategy obviates the use of toxic chemical initiators and cross-linkers. The resultant eutectogels exhibit extremely high stretchability (2860%), desirable self-healing ability, high conductivity (0.72 S m-1), biocompatibility, excellent environmental stability, and exceptional responsiveness to tensile strain (GF = 4.08) and temperature (TCR = 5.35% K-1). Benefiting from these integrated features, the conductive eutectogels serve as multifunctional flexible sensors for human motion recognition and temperature monitoring. Furthermore, the eutectogel serves as a pliable electrode in the assembly of a triboelectric nanogenerator (TENG), designed to harvest mechanical energy, convert it into stable electrical outputs, and enable self-powered sensing. This study offers an approach to fabricating multifunctional integrated conductive eutectogels, making it a step closer to the development of intelligent flexible electronics.