Reliable Sensor Research Articles

Improving the NO2 Gas Sensing Performances at Room Temperature Based on TiO2 NTs/rGO Heterojunction Nanocomposites

The development of energy-efficient, sensitive, and reliable gas sensors for monitoring NO2 concentrations has garnered considerable attention in recent years. In this manuscript, TiO2 nanotube arrays/reduced graphene oxide nanocomposites with varying rGO contents (TiO2 NTs/rGO) were synthesized via a two-step method for room temperature NO2 gas detection. From SEM and TEM images, it is evident that the rGO sheets not only partially surround the TiO2 nanotubes but also establish interconnection bridges between adjacent nanotubes, which is anticipated to enhance electron–hole separation by facilitating electron transfer. The optimized TiO2 NTs/rGO sensor demonstrated a sensitive response of 19.1 to 1 ppm of NO2, a 5.26-fold improvement over the undoped TiO2 sensor. Additionally, rGO doping significantly enhanced the sensor’s response/recovery times, reducing them from 24 s/42 s to 18 s/33 s with just 1 wt.% rGO. These enhancements are attributed to the increased specific surface area, higher concentration of chemisorbed oxygen species, and the formation of p-n heterojunctions between TiO2 and rGO within the nanocomposites. This study provides valuable insights for the development of TiO2/graphene-based gas sensors for detecting oxidizing gases at room temperature.

Real‐Time Lactate Detection in A Dynamic Environment Using Micrsensing Needles

AbstractThis study introduces a novel microneedle‐based lactate sensor with SU‐8 micropillar enhancement, designed for real‐time monitoring in dynamic environments. Utilizing inkjet‐printing technology, the sensor demonstrates enhanced sensitivity and a reduced limit of detection (LoD), addressing critical challenges in clinical applications like hemodialysis and patient monitoring in ICU. Design enhancements in the medical steel needle improve stress resistance during insertion, contributing to the sensor's reliability. The experimental findings demonstrate that the microneedle is capable of achieving a high level of linearity at 0.99, with a sensitivity of 3.38 µA mM−1/mm−2–0.5 µA mM−1/mm−2 observed within the range of 0.1–0.5 mM and 1–10 mM, respectively. Meanwhile, the microneedle exhibits a low limit of detection (LoD) of 0.01 mM when tested in phosphate‐buffered saline (PBS) with varying lactate concentrations. Moreover, it demonstrates a linearity of 0.98, sensitivity of 1.13 µA mM−1 mm−2, and the same LoD of 0.01 mM in urine. The sensor maintains its performance at flow rates up to 500 mL min−1. Overall, this flexible and inkjet‐printed lactate sensor represents a significant advancement in real‐time clinical monitoring technology.

A review on exploring the potential of PVA and chitosan in biomedical applications: A focus on tissue engineering, drug delivery and biomedical sensors.

Polymers have been integral to the advancement of biomedicine, owing to their exceptional versatility and functionality. Among these, polyvinyl alcohol (PVA) and chitosan both natural polymers stand out for their remarkable biocompatibility, biodegradability, and unique properties. This review article provides a comprehensive examination of the diverse applications of PVA and chitosan in three pivotal areas: tissue engineering, drug delivery, and biosensors. In tissue engineering, the discussion centres on how PVA and chitosan are engineered into scaffolds that not only support cell growth and differentiation but also promote tissue regeneration by closely mimicking the extracellular matrix. These scaffolds offer the necessary mechanical strength and adaptability for various biomedical applications. For drug delivery, the article delves into the development of sophisticated controlled release systems and targeted drug carriers, highlighting the polymers' customizable properties and their mucoadhesive nature, which make them highly effective across multiple drug delivery methods. Furthermore, the potential of PVA and chitosan in biosensor technology is explored, particularly their ability to interact with biomolecules and their intrinsic conductivity attributes that are essential for creating sensitive, reliable, and biocompatible sensors for medical diagnostics. By synthesizing recent research findings and suggesting future research directions, this review underscores the versatility and critical role of PVA and chitosan in pushing the boundaries of biomedical innovation. It offers valuable insights for researchers and scientists dedicated to advancing healthcare through the application of these natural polymers.

Advancements in Binary Solvent-Assisted Hydrogel Composites for Wearable Sensing Applications

The advancement of wearable sensing technologies has been pivotal in revolutionizing healthcare, environmental monitoring, and personal fitness. Among the diverse materials employed in these technologies, multifunctional hydrogel composites have emerged as critical components due to their unique properties, including high water content, flexibility, and biocompatibility. This review provides a comprehensive overview of the state-of-the-art in binary solvent-assisted hydrogel composites for wearable sensing applications. It begins by defining hydrogel composites and their essential attributes for wearable sensors, specifically focusing on binary solvent-assisted methods that enhance their performance and functionality. The review then delves into the applications of these composites in health monitoring, environmental detection, and sports and fitness, highlighting their role in advancing wearable technologies. Despite their promising features, there are significant challenges related to durability, sensitivity, and integration that need to be addressed to fully exploit these materials in wearable devices. This review discusses these challenges and presents potential solutions, including the development of new materials, improvement in fabrication processes, and strategies for achieving multifunctionality and sustainable design. Looking forward, the paper outlines future directions for research in this field, emphasizing the need for innovative materials and technologies that can lead to more effective, reliable, and eco-friendly wearable sensors. This review aims to inspire further research and development in the field of wearable sensing, paving the way for new applications and advancements in healthcare, environmental monitoring, and personal fitness technologies.

Low-Cost Sensors for the Measurement of Soil Water Content for Rainfall-Induced Shallow Landslide Early Warning Systems

Monitoring soil water content (SWC) can improve the effectiveness of early warning systems (EWSs) designed to mitigate rainfall-induced shallow landslide risk. In extensive areas, like along linear infrastructures, the adoption of cost-effective sensors is critical for the EWS implementation. The present study aims to evaluate the reliability of different low-cost SWC sensors (frequency domain reflectometry and capacitance-based) in capturing soil moisture conditions critical for EWS, without performing soil-specific calibration. The reliability of the low-cost sensors is assessed through a comparative analysis of their measurements against those from high-cost and well-established sensors (time domain reflectometry) over a two-year period in a shallow landslide-prone area of Oltrepò Pavese, Italy. Although no landslides are observed during the monitoring period, meteorological conditions are reconstructed and statistical analysis of sensor’s responses to different rainfall events is conducted. Results indicate that, despite differences in absolute readings, low-cost sensors effectively capture relative SWC variations and demonstrate sensitivity to rainfall events across both cold and warm periods. The presented low-cost sensors can serve as reliable indicators of soil infiltration and saturation levels, highlighting their potential for real-time monitoring within extensive networks for EWS.

Health Monitoring and Prediction Using Non-invasive Sensors in IoT Environment

Introduction: IoT's blend with non-invasive sensors has made health state prediction possible and constant real-time monitoring transformed healthcare. Using photoplethysmography (PPG) and electrocardiograms (ECG), this device evaluates important parameters including heart rate, blood oxygen saturation, and blood pressure. As healthcare systems face increasing demands, this technology provides a patient-centered and efficient method for long-term health tracking—needed for early intervention and management of chronic diseases. Objectives: The main goal is to evaluate using time-series data from PPG and ECG sensors the efficiency of an IoT-enabled health monitoring system coupled with machine learning models in predicting health conditions including arrhythmias and hypoxia. Methods: This system detects events based on temporal pattern recognition by use of Long Short-Term Memory (LSTM) networks and Random Forest classifiers. Noise reduction is preprocessed and wearable device data is labeled for training of machine learning models. The design combines wearable sensors, a smartphone app, and cloud-based analysis for continuous monitoring and real-time input. Results: Experimental data shows that the LSTM model attained a 95% accuracy while the Random Forest classifier attained a 92.5% accuracy. Both models displayed remarkable accuracy and recall even while the LSTM model excels in identifying anomalies in time-series data and the Random Forest classifier skillfully detects health events depending on certain criteria. Conclusions: This study demonstrates the capability of IoT-based health monitoring systems to enhance patient outcomes through continuous, non-invasive monitoring and predictive analytics. Despite its potential, difficulties persist, including data security, sensor reliability, and energy efficiency, necessitating additional research for broad use and clinical integration.

System-level Prognostics and Health Management for Complex Industrial Systems

Prognostics and health management (PHM) has become essential to guarantee aware and safe system operation and to inform economic decision-making. However, due to the nature of detection, diagnostics, and prognostic methods, applications have mainly been limited to the component level. In practice, most industrial systems consist of multiple interacting components whose partial degradation could lead to the system's failure (or subsystems).This research addresses the limitations of traditional component-level PHM techniques by proposing a novel system-level framework. By implementing a hierarchical structure of components and subsystems, we will select an optimal method for each subsystem to aggregate its component health assessments. The overall system health can then be estimated by further combining the obtained estimates. The research considers simplified and holistic modeling techniques, margin-based methods, and hybrid graphical models. This approach aims to provide reliable system health predictions and online components' sensitivity measures to enhance maintenance decision-making. We consider an application in the context of the nuclear industry, characterized by strict safety and economic requirements. Using a SIMULINK model to approximate a Pressurized Water Reactor (PWR) with real industrial inputs, we plan to add component degradation modules and use simulated sensor data and reliability information to test the proposed framework. Initial results on artificial case studies show the feasibility of integrating component-level health predictions.

EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study

Background Current health recommender systems lack interactivity that relates to the current situation. Methods We designed and implemented an intervention study for obese patients that incorporates context information obtained from smartphone and smartwatch sensors, gamification, as well as joint goal management of patients and health coaches. We developed a health behaviour recommendation system comprising of a smartphone application, cloud platform for data management, and a data dashboard for coaches. Results We conducted a three months long study and analysed data from eight patients, focusing on system function, patient adherence, satisfaction and overall impact of the proposed system on changing health-related habits. Along with data analysis, we also provide patient feedback collected during interview round after the end of the study. Conclusion Patients could successfully implement the goals using the EghiFit app. Challenges regarding the data collection, recommendation synthesis, and patient engagement persist. Furthermore, reliable sensor data processing on current smartphone platforms is difficult due to system restrictions. Future research should further integrate sensor data, gaming, and health behaviour intervention design using smart devices.

Nano‐Patterned CuO Nanowire Nanogap Hydrogen Gas Sensor with Voids

AbstractHydrogen (H2) is increasingly employed in industrial applications, such as developing hydrogen fuel cells for vehicles and power plants. However, H2 explodes at a concentration limit of 4%, necessitating the development of ultrasensitive hydrogen sensors capable of early‐stage detection of hydrogen leaks. In this study, nano‐patterned polycrystalline cupric oxide (CuO) nanowire array nanogap gas sensors with voids are fabricated using electron‐beam lithography and ex situ oxidization by annealing, which could detect 5 ppb H2 and show response and recovery times of less than 10 s without a baseline shift. Combining a pre‐H2 annealing process in Ar/H2 for Cu nanowires and a low‐rate oxidation process enhances the crystallinity of CuO nanowires, facilitating the preparation of polycrystalline CuO nanowires with voids, which is a significantly practical approach for the improvements of gas sensing properties. Response and recovery times of <10 s can be obtained for a gap separation of ≈30 nm. These improvements are discussed based on a high electric field of ≈1.3 MV cm−1. The relationship between the normalized response and H2 concentration is discussed based on the power law. This paper presents highly reliable and fast H2 sensors without a baseline shift to meet the demands of the hydrogen industry.

Ultra Enhancement of the Resolution of the Divergence Beam-Based SPR Sensor Using a Wavelet Filter

Surface Plasmon Resonance (SPR) sensors are highly sensitive to refractive index variations, making them ideal for biosensing and chemical detection applications. However, their performance can be constrained by noise, resolution limits, and signal distortions, particularly in divergence beam-based configurations. This research presents an ultra-enhancement of the performance of divergence beam-based SPR sensors by employing advanced wavelet filtering techniques. Wavelets, with their multi-resolution analysis capability, are applied to denoise and refine the sensor signal, significantly improving key performance metrics, including resolution, mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), sensitivity matrix (SM), and combined sensitivity factor (CSF).The wavelet filter effectively decomposes the SPR signal into distinct frequency bands, separating noise while retaining high-frequency components required for accurate sensing. This results in a significant reduction in MSE and RMSE of 30 and 92.26%, respectively, while simultaneously improving SNR by 54.08%, maintaining signal quality. The wavelet filter significantly improved the resolution of the SPR sensor by 99.95% (1.3772×10-7 RIU), allowing for more precise detection of refractive index changes and boosting diverge beam-based SPR sensor performance. In addition, a sensitivity matrix (SM) and combined sensitivity factor (CSF) were improved by 42 and 41.94%, respectively, allowing for a more thorough evaluation of the sensor's performance across multiple operational parameters. Simulation and experimentation show that wavelet filtering surpasses standard filtering approaches in terms of noise suppression and signal clarity. This technology has considerable potential for improving the accuracy and reliability of SPR sensors in high-sensitivity applications

Novel amino-functionalized MOF-based sensor for zinc ion detection in water and blood serum samples

Aquatic systems with low zinc levels can experience a significant decrease in carbon dioxide uptake and limited growth of phytoplankton species. In this study, we describe the use of a new fluorescent sensor based on NH2-MIL-53(Al), and modified with glutaraldehyde and sulfadoxine, for selectively detecting zinc ions in water and blood serum samples. Characterization of the synthesized material was performed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller (BET) surface area analysis, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM), confirming successful functionalization and preservation of the MOF structure. The sensor’s performance for Zn2+ detection was evaluated by spectrofluorometry, demonstrating a significant fluorescence enhancement upon Zn2+ binding due to the interaction between Zn2+ ions and the sulfonamide groups. With a detection limit as low as 3.14 × 10–2 ppm, the sensor demonstrates high selectivity for Zn2+ over other common metal ions. The sensor’s response is rapid, stable, and reproducible, making it suitable for practical applications. Real sample analysis was conducted in tap water and blood serum samples, with the results compared to those obtained using ICP-OES and a colorimetric test with 5-bromo-PAPS. The comparison confirmed the high accuracy and reliability of the fluorescent sensor in detecting Zn2+ ions in complex matrices. NH2-MIL-53(Al) modified with glutaraldehyde and sulfadoxine shows potential as a selective fluorescent sensor for Zn2+ detection, making it a valuable tool for monitoring the environment and biology.

Modeling, assessment and characterization of soiling on PV Technologies. Toward a Better understanding of the Relation between dust deposition and performance losses

Soiling is a critical, site-specific challenge that affects the performance and economic viability of photovoltaic power plants. This study evaluates the effectiveness of an outdoor microscopy method under the specific climatic conditions and dust composition of the Mid-south region of Morocco. Semi-empirical models were developed to correlate the Soiling Coverage Index with losses in optical and electrical performance. Additionally, a comprehensive analysis of local dust, encompassing its chemical composition, morphology, and size distribution, was conducted. Results indicate quartz as the predominant mineral in Benguerir city’s dust particles, with diameters ranging from 0 μm to 26 μm. Additionally, following a 2-week exposure with no rainfall, a dust density of 2.5 g/m2 accumulated on the deployed glass coupons, resulting in a soiling ratio of 6.9 % and a relative transmittance loss of 15.19 %. The surface coverage index, as calculated by the outdoor microscope, was 9.16 %. Furthermore, the evaluation of this metric revealed strong positive correlations (r2 = 0.95 to 0.99) with key soiling indicators such as dust density, soiling ratio, and transmittance loss. These findings underscore the efficacy of the outdoor microscope as a fast, low-cost, and reliable soiling monitoring sensor, offering valuable insights for future monitoring and mitigation efforts.

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors.

Nonlinearity in sensor measurements reduces the sensor's accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors.

Anchor carbon dots inside NH2-MIL-88B via ship-in-a-bottle strategy for dual signal enhancement in colorimetric-fluorescent sensors

The increase in oxytetracycline (OTC) pollution has become a significant risk to ecological stability and human health because of excessive use. Therefore, developing a precise and reliable sensor for trace OTC detection is critical. In this work, a dual-mode sensor of colorimetric-fluorescent (CL-FL) with dual signal-on was developed for OTC detection. Firstly, a novel carbon dots encapsulating in cavities of MOFs composite (CDs@NH2-MIL-88B) was constructed by the ship-in-a-bottle approach, which enhanced peroxidase-like activity and fluorescent intensity. Further, combining with surface molecularly imprinted polymer (MIP), the dual-mode sensor was constructed to develop selectivity (CDs@NH2-MIL-88B@rMIP). Finally, the dual signal enhancement mechanism of the sensor comes from the interaction between OTC and identifying binding site involving strong hydrogen bonding and metal complexation from imprinted cavity and CDs@NH2-MIL-88B, respectively. The prepared sensor has a linear detection range of 1.00 × 10-9 - 1.00 × 10-5M with the limit of detection (LOD) of 1.47 × 10-10M for the CL mode and a detection range of 1.00 × 10-11 -1.00 × 10-7M with the LOD of 1.84 × 10-12M for the FL mode. Meanwhile, the sensor shows high sensitivity, accuracy and reliability in OTC detection. Overall, this work provides a promising CL-FL dual-mode sensing systems with signal-on in the detection of antibiotics and other hazardous pollutants.

A fast-aware multi-target response prediction approach and its application in aeronautical engineering

Response prediction is a fundamental yet challenging task in aeronautical engineering, requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction (FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation, we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods, the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime (3.514 s).

Highly sensitive and room-temperature operable carbon dioxide gas sensor based on spin-coated Sn-doped Co3O4 thin films with advanced recovery properties

The urgency to address climate change has highlighted the need for gas sensors capable of monitoring air quality at room temperature (RT) and accurately measuring the concentrations of carbon oxides (CO2 and CO) in the environment. This study details the development of a highly sensitive CO2 gas sensor using spin-coated Sn-doped Co3O4 thin films, operating at a room temperature of 30⁰C and a relative humidity (RH%) of 43 %. Extensive characterization employing XRD, SEM, EDX, FTIR, and UV–Vis optical techniques verified the impact of Sn doping on the surface morphology, phase purity, and a notable reduction in the dual-band gap of the thin films. Gas sensing measurements were conducted at RT using varying CO2 gas concentrations. A sensor response of 796.81 % was obtained for the optimally sensitive film, 10 % Sn-doped Co3O4, at a CO2 concentration of 9990 ppm. Additionally, a range of RH % levels was examined at a constant CO2 gas concentration of 9990 ppm, revealing an optimal humidity level of 43 % at RT. Further analysis revealed that the 10 % Sn-Co3O4 sensor displayed enhanced sensitivity to CO2, surpassing its response to N2, H2, and NH3 gases. The determined limits of detection and quantification underscore the sensor's precision and reliability in quantifying CO2 gas concentrations. Our findings demonstrate the excellent potential of Sn-doped Co₃O₄ thin films as highly sensitive CO₂ gas sensors. These films provide a promising solution for detecting elevated CO₂ levels at room temperature, aiding climate change mitigation efforts.

Development of Additively Manufactured Embedded Ceramic Temperature Sensors via Vat Photopolymerization

Current additive manufacturing (AM) techniques and methods, such as liquid-crystal display (LCD) vat photopolymerization, offer a wide variety of surface-sensing solutions, but customizable internal sensing is both scarce in presence and narrow in scope. In this work, a fabrication process for novel customizable embedded ceramic temperature sensors is investigated. The fabrication techniques and materials are evaluated, followed by extensive characterization via spectral analysis and thermomechanical testing. The findings indicate that LCD-manufactured ceramic sensors exhibit promising sensing properties, including strong linear thermal sensitivity of 0.23% per °C, with an R2 of at least 0.97, and mechanical strength, with a hardness of 570 HV, making them suitable for adverse environmental conditions. This research not only advances the field of AM for sensor development but also highlights the potential of LCD technology in rapidly producing reliable and efficient ceramic temperature sensors.

A Comprehensive Review of State-of-the-art Optical Methods for Methane Gas Detection

Methane (CH<sub>₄</sub>), a potent greenhouse gas, significantly contributes to climate change and global warming. Its impact over 100 years surpasses carbon dioxide (CO<sub>₂</sub>) by 28 times. Addressing methane emissions, particularly from oil and gas production activities such as transmission pipelines, is imperative. One promising avenue is the development of reliable sensors to detect and mitigate methane leaks and prevent hazardous issues. Optical-based methods present notable advantages, including versatility and remote operation, making them pivotal in this endeavor. This review article provides a concise overview of optical-based methane identification technologies, encompassing sensing materials, absorption spectra, operational mechanisms, and recent advancements. Potential perspectives are explored, and inferences from this assessment are also derived. Emphasizing the significance of optical fiber-based methane detection methods, the authors advocate for further research to support ongoing efforts and foster innovation in this critical area.

Test-retest reliability and construct validity of Inosen’s Silat Momentum Test

Problem Statements: The kick in Pencak Silat has a magnitude called momentum, which requires a tool capable of measuring several variables based on the International System of Units (SI) in real-time. Nowadays, there are affordable and reliable sensor technologies to meet these needs. Purpose: The research aimed to design a test instrument that utilizes sensor technology to measure kick momentum in the sparring category of Pencak Silat following the applicable SI, and to test its reliability and validity. The resulting instrument is named Inosen’s Silat Momentum Test. Approach: This study followed Johnson and Nelson’s seven-test construction process. Twenty-four Pencak Silat athletes, consisting of 12 elite and 12 non-elite participants, were involved in the evaluation process. The test’s reliability was assessed by examining the correlation between the test and retest results. The construct validity test was conducted by comparing the results of the front kick tests of elite and non-elite Pencak Silat athletes. Data analysis was performed using Pearson correlation and independent t-tests using Minitab 21.4.1 software. Results: The results of the test and retest indicate a very high correlation (r = 0.923), demonstrating the consistent ability of Inosen’s Silat Momentum Test to measure kick momentum. The study found a statistically significant difference in the Inosen’s Silat Momentum Test scores (p < 0.05) between the elite fighter group (average score of 463.3 kg.m/s) and the non-elite fighter group (average score of 375 kg.m/s). The results indicate that Inosen’s Silat Momentum Test is an effective instrument for differentiating kick momentum based on fighter performance. Conclusions: Based on the findings, it is inferred that Inosen’s Silat Momentum Test is a reliable and valid instrument for measuring Pencak Silat kick momentum according to SI standards. Keywords: biomechanics, kick momentum, pencak silat, performance test, sensor technology.

Chemoresistive Gas Sensors Based on Electrospun 1D Nanostructures: Synergizing Morphology and Performance Optimization.

Gas sensors are essential for safety and quality of life, with broad applications in industry, healthcare, and environmental monitoring. As urbanization and industrial activities intensify, the need for advanced air quality monitoring becomes critical, driving the demand for more sensitive, selective, and reliable sensors. Recent advances in nanotechnology, particularly 1D nanostructures like nanofibers and nanowires, have garnered significant interest due to their high surface area and improved charge transfer properties. Electrospinning stands out as a promising technique for fabricating these nanomaterials, enabling precise control over their morphology and leading to sensors with exceptional attributes, including high sensitivity, rapid response, and excellent stability in harsh conditions. This review examines the current research on chemoresistive gas sensors based on 1D nanostructures produced by electrospinning. It focuses on how the morphology and composition of these nanomaterials influence key sensor characteristics-sensitivity, selectivity, and stability. The review highlights recent advancements in sensors incorporating metal oxides, carbon nanomaterials, and conducting polymers, along with their modifications to enhance performance. It also explores the use of fiber-based composite materials for detecting oxidizing, reducing, and volatile organic compounds. These composites leverage the properties of various materials to achieve high sensitivity and selectivity, allowing for the detection of a wide range of gases in diverse conditions. The review further addresses challenges in scaling up production and suggests future research directions to overcome technological limitations and improve sensor performance for both industrial and domestic air quality monitoring applications.