Sensor Output Research Articles

Aluminium foil micro-defect detection method based on motion-induced eddy current using differential TMR sensor spatial position compensation

ABSTRACT Lithium-ion batteries are a key technology in the energy storage field, and aluminum foil, a common cathode current collector, can develop micro-defects that significantly affect battery performance, especially during high-speed manufacturing. Motion-induced eddy current (MIEC) sensing is effective for detecting defects in conductive materials. However, lift-off fluctuations degrade the signal-to-noise ratio (SNR), making it challenging to detect small or shallow defects. Traditional methods, such as differential sensor outputs, help reduce these fluctuations, but baseline signal deviations caused by varying relative positions between the sensor and the permanent magnet limit their effectiveness. To address this, this study proposes a spatial compensation method for differential tunnel magnetoresistance (TMR) sensors, which reduces baseline deviations caused by position differences, mitigating lift-off fluctuations and improving the SNR for micro-defect detection.

Comparison and performance optimization of two absolute circuit designs based on Multisim

Digital circuits are crucial in today's technology, underpinning infrastructure such as computing, communicating, and intelligent devices. Its high efficiency, accuracy and programmable capability drive the development of information processing, data transmission and automation systems. The application of digital circuits has promoted innovation in artificial intelligence, the Internet of Things and other fields, providing strong support for the intelligence and interconnection of modern society. The absolute value circuit is discussed in this paper. The absolute value circuit is implemented for converting the negative value of the input signal to a positive value, ensuring that the output signal is always non-negative. Its significance is to handle application scenarios that require non-negative values, such as signal processing, audio amplification, and sensor output. Through the absolute value circuit, negative value can avoid the negative effect on the subsequent circuit, and improve the stability and accuracy of the system. This paper first introduces all the gates used in the circuit, points out the functions of each gate circuit, and then puts forward two absolute value circuit designs, explaining their working principles, differences and advantages and disadvantages. Then the minimum delay is analyzed by the critical path logic and the sizes of each logic gate is given. Then the supply voltage is optimized after selecting the design with the smallest delay to reduce energy consumption.

Research on the Sensitivity Enhancement Method of Inductive Conductivity Sensors Based on Impedance Matching.

This paper presents the design and performance evaluation of an inductive conductivity sensor with a double tuning impedance matching network to enhance sensitivity and improve linearity. The sensor's equivalent circuit model is analyzed and verified through simulation, and impedance matching is shown to significantly increase the sensor's output signal, particularly at low conductivity measurements. Double tuning impedance matching expands the frequency response range and optimizes power transfer efficiency, achieving a higher power factor across a broader frequency range. Experimental results confirm that the sensor's sensitivity increases by approximately 30% after impedance matching with optimal performance at a frequency of 9865 Hz. Furthermore, while the impedance matching improves sensitivity, it also introduces some nonlinear errors, which are evaluated using a performance function that balances sensitivity and linearity. The results demonstrate that impedance matching enhances the sensor's measurement capability, making it more effective in practical applications where conductivity changes need to be accurately monitored across varying frequencies.

Experimental Validation of Virtual Torque Sensing for Wind Turbine Gearboxes Based on Strain Measurements

ABSTRACTIn efforts to reduce the operation and maintenance cost of wind turbines, there is an increasing interest to monitor key turbine quantities such as the torque load on the gearbox. Monitoring the torque paves the way for the calculation of remaining useful lifetime, leading to cost reductions through improved reliability and maintenance planning. In order to avoid expensive direct torque sensors, this paper investigates the potential of virtual torque sensing, a technique based on 3 basic components: first, a set of non‐intrusive sensors installed on the gearbox. Three groups of strain gauges on the gearbox as well an angular encoder are considered in this paper. Second is a physics‐based model, capable of predicting the response of aforementioned sensors. These models are constructed with a purposeful balance between accuracy and computational cost. Model validation and updating are performed to ensure efficient and accurate prediction of the sensor output. Finally, an augmented extended Kalman filter (AEKF) is used to combine the measured response with predictions from the model to infer the gearbox input torque. Since a key factor determining the performance of the AEKF is the tuning of the AEKF covariance matrices, multiple methods are introduced to systematically tune the covariance matrices. Experimental validation results show that the virtual torque sensor can detect the load torque with a normalized mean absolute error (NMAE) between 3.41% and 7.47%, depending on the sensor set. The influence of the amount of sensors used and the tuning method are also investigated.

Highly sensitive strain sensors with ultra-low detection limit based on pre-defined serpentine cracks.

Flexible and stretchable strain sensors have garnered significant interest due to their potential applications in various fields including human health monitoring and human-machine interfaces. Previous studies have shown that strain sensors based on microcracks can exhibit both high sensitivity and a wide sensing range by manipulating the opening and closing of randomly generated cracks within conductive thin films. However, the uncontrolled nature of microcrack formation can cause a drift in the sensor's performance over time, affecting its accuracy and reliability. In this study, by pre-defining the cracks, we introduce a novel resistive strain sensor with high sensitivity, excellent linearity, an ultra-low detection limit, and robustness against off-axis deformation. The sensor operates on a simple mechanism involving the modulation of ohmic contact within intricately designed conductive serpentine curves, which are encapsulated by pre-stretched thin films. This design facilitates a high gauge factor of 495, exceptional linearity (R2 > 0.98), and an ultra-low detection threshold of 0.01% strain. Moreover, it maintains performance integrity during off-axis deformations such as bending and twisting, features that are indispensable for accurately monitoring human motion. To explore practical applications, a driving scenario was simulated where a sensor array was positioned on the driver's neck. The sensor output was analyzed using machine learning algorithms to successfully determine the presence of driver fatigue. This demonstration underlines the potential of our sensor technology in applications ranging from healthcare monitoring to wearable biomechanical systems and human-machine interfaces.

A Remote Monitoring System for Wind Speed and Direction Based on Non-Contact Triboelectric Nanogenerator

Wind speed and wind direction sensors are crucial sensor categories in the Internet of Things (IoT), particularly vital in fields such as meteorological monitoring, construction engineering, transportation engineering, and ocean engineering. However, power supply remains a key limiting factor for the widespread application of these sensors in large-scale sensor networks. In this paper, a self-powered, non-contact wind speed and direction sensor based on the triboelectric nanogenerator (SD-TENG) is proposed. The optimized wind speed measurement structure has a start-up wind speed as low as 0.2m/s and exhibits good linearity in the range of 0.2-29m/s. Additionally, the sensor demonstrates high temperature and humidity resistance, with a voltage attenuation of 2.4% at 45°C ambient temperature and 9.8% at 95% relative humidity. For wind direction measurement, the design of non-uniform electrodes enhances the recognition capability of different channels. To meet the demands of remote monitoring, we have designed an advanced signal processing circuit that can directly convert the raw output of a wind speed sensor into wind speed information and upload it to a cloud platform via a host. This system not only records and displays wind speed data in real-time but also features historical data storage and alarm functionalities, enhancing the intelligence and automation levels of wind speed monitoring. Additionally, users can access and analyze wind speed data through both computer and mobile devices, ensuring the system's efficiency and reliability. This work provides crucial technical support for the advancement of smart cities, clean energy, and environmental monitoring, thereby promoting the application and dissemination of IoT technology in the environmental field.

Night-time blood pressure dipping, but not 24-h blood pressure level, is linked to increased 24-h ocular volume slope.

This study investigated the relationship between blood pressure and intraocular pressure in treatmentnaive, non-glaucoma patients with different blood pressure statuses, focusing on the 24-h ocular volume and nocturnal blood pressure decline. Treatment-naive, non-glaucoma patients undergoing hypertension evaluation were enrolled as study participants. Simultaneous 24-h ambulatory blood pressure measurement and 24-h ocular volume recording with a contact lens sensor. We also compared ocular volume curve parameters between normotensive and hypertensive patients, as well as between those with and without nocturnal blood pressure decline. A total of 21 patients, including 7 normotensive and 14 treatment-naive hypertensive individuals, were included in the study. of them, 11 were dippers and 10 were non-dippers. No significant difference in the 24-h ocular volume slope was observed between the hypertensive and normotensive patients (p=0.284). However, dippers had a significantly higher 24-h ocular volume slope (p=0.004) and nocturnal contact lens sensor output (p=0.041) than non-dippers. Nocturnal blood pressure decline, rather than the blood pressure level, is associated with the increased 24-h ocular volume slope and nocturnal ocular volume. Further studies are required to determine whether the acceleration of glaucoma progression in dippers is primarily due to low blood pressure, high intraocular pressure, or a combination of both.

Dual-Gate Modulation in a Quantum Dots/MoS2 Thin-Film Transistor Gas Sensor.

Mastering the surface chemistry of quantum dots (QDs) has enabled a remarkable gas-sensing response as well as impressive air stability. To overcome the intrinsic receptor-transducer mismatch of QDs, PbS QDs used as sensitive NO2 receptors are spin-coated on top of a few-layer MoS2 and incorporated into a thin-film transistor (TFT) gas sensor. This architecture enables the separation of the electron transduction function from the chemical reception function. A comparison study through size engineering of QDs combined with TFT device modeling suggests a unique dual-gate modulation related to the capacitance coupling effect of QDs. The favorable increase in sensor output current by 3 orders of magnitude is ascribed to the high mobility of the few-layer MoS2. The optimal sensor exhibits a sensitive (LOD ∼ 0.6 ppb), selective, and recoverable response at room temperature. Because of the dual-gate modulation, the sensor performance is further optimized by varying the gate voltage (a two-fold increase in response to 1 ppm of NO2).

Real-Time Information Acquisition and Processing Method for Penetration Information Based on Multi-Information Fusion

To improve the real-time performance and the target adaptability of penetration fuze detonation control systems, and to enhance the system fusion processing capability for multi-sensor information, this paper uses a modular design concept to construct a miniaturized (ø38[Formula: see text]mm[Formula: see text]×[Formula: see text]4[Formula: see text]mm) fuze detonation control system that is capable of real-time processing of data from multiple information sources. The core component of this system is the GD32E230 microcontroller, which features a high dominant frequency and low power consumption. This device is integrated with a ferroelectric memory and signal processing circuits that match the sensors. To address the issue of unclear traditional acceleration signal penetration and the difficulties associated with the identification of these signals, the approach in this paper improves feature recognition accuracy through rapid acquisition and fusion of multiple types of sensor output signal, and self-adaptive identification of multilayered targets and single-layer thick targets is achieved. During the programming of the embedded system, the hardware register is operated directly, the instruction execution sequence is optimized, and the program execution efficiency is improved by using the function characteristic that some microcontroller unit peripherals do not occupy the central processing unit when working, thus allowing the intended purpose of improving the system’s real-time performance to be achieved. A semi-physical simulation method is then used to verify the performance of the penetration fuze detonation control system. The results obtained show that the system has 100%-layer counting accuracy for multilayered targets and a relative error of less than 1% for the calculated residual velocities of single-layer thick targets, thus validating the effectiveness of the system.

Gas-compensated thermal flow sensor using an integrated velocity-independent gas properties meter

Abstract A gas-compensated thermal flow sensor is presented that measures the flow rate in real-time, independent of the type of gas, by simultaneously measuring and compensating for the thermal conductivity and volumetric heat capacity of the gas. The thermal flow sensor consists of two free-hanging, heated wires, forming a calorimetric flow meter. The temperature difference between the two wires is a function of the flow rate and the fluid thermal properties. An additional heated wire is integrated on the same chip and used to measure the gas properties. This wire is suspended over a shallow V-groove cavity, and oriented perpendicular to the flow direction, so that it is only affected by the gas properties and not by the flow. DC excitation is used to measure the thermal conductivity, and AC excitation with the 3$\omega$ method is used to determine the volumetric heat capacity. The output of the thermal flow sensor is automatically corrected for the medium using these measured parameters. Measurements were performed with 11 different gases and gas mixtures, and in all cases the deviation between the applied flow rate and measured flow rate is less than 10\%.

Modelling heart rate dynamics in relation to speed and power output in sprint kayaking as a basis for training evaluation and optimisation.

With the development of power output sensors in the field of paddle sports and the ongoing advancements in dynamical analysis of exercise data, this study aims to model the measurements of external training intensity in relation to heart rate (HR) time-series during flat-water kayak sprint. Nine elite athletes performed a total of 47 interval training sessions with incremental intensity (light to (sub-) maximal effort levels). The data of HR, speed and power output were measured continuously and rating of perceived exertion and blood lactate concentration ([BLa]) were sampled at the end of each interval stage. Different autoregressive-exogenous (ARX) modelling configurations are tested, and we report on which combination of input (speed or power), model order (1st or 2nd), parameter estimation method (time-(in)variant) and training conditions (ergometer or on-water) is best suited for linking external to internal measures. Average model R2 values varied between 0.60 and 0.97, with corresponding average root mean square error values of 15.6 and 3.2 bpm. 1st order models with time-varying (TV) parameter estimates yield the best model performance (average R2=0.94). At the level of the individual athlete, the TV modelling features (i.e., the model parameters and derivatives such as time constant values) show significant repeated measure correlations in relation to measures of exercise intensity. In conclusion, the study provides a comprehensive description of how the dynamic relationship between external load and HR for sprint kayaking training data can be modelled. Such models can be used as a basis for improving training evaluation and optimisation.

Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data.

Axle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in the spatial extent of traffic survey data and axle load detection data in highway networks. Initially, using the Highway Asphalt Pavement Design Specification, it analyzes the demand for these calculations in road sections. Considering the current axle load detection coverage, a method supported by highway traffic data is proposed. For integrating multi-source data, a generalized regression neural network model is established, enabling deep learning calculations. The method is validated and applied to Xuzhou's highway network. Results show consistency between the calculated average axle load spectrum and actual data. Among validation samples, 3-axle vehicles exhibit the smallest deviation, while 6-axle vehicles show the largest. Calculating equivalent axle numbers reveals the distribution and grading of heavily loaded road sections, aiding maintenance decisions.

Driver Drowsiness Detection System for Accident Prevention

The abstract highlights the importance of driver drowsiness detection as a critical component of vehicle safety technology, with the goal of preventing accidents brought on by drowsy drivers. According to studies, driver fatigue possibly a factor in 20% of traffic accidents, underscoring the importance of developing efficient accident-avoidance strategies. The research focuses on a particular illustration of an automated tiredness detection system intended to improve driver safety by tracking unsafe driving practices. The primary objective of the research is to develop an automated system capable of accurate analysis the blink patterns of the driver's eyes. It detects modifications to the distance between the eyes and reflects eye blink events using an infrared-based eye blink sensor. The location of the visual is indicated by the sensor's output, which is high when the eye is closed and low when it is open. The project includes a circuit that, in the event that it detects indicators of tiredness, such closed eyes, while driving, sounds an alarm, namely a buzzer next to the driver. By offering a real-time alarm mechanism, this technology seeks to address the risks related to driver weariness and enhance overall road safety.

Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network.

Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. We design and develop an electronic nose system containing a bionic chamber that imitates the nasal-cavity structure of humans and canines. The sensors are positioned based on fluid simulation results. Response data for ammonia and ethanol gases and the response/ recovery times of an ammonia sensor under three concentrations are collected using the electronic nose system. Response data are classified and regressed using a sparrow search algorithm (SSA)-optimized backpropagation neural network (BPNN). The results show that the sensor has a relative mean deviation of 1.45%. The ammonia sensor's output voltage is 1.3-2.05 V when the ammonia concentration ranges from 15 to 300 ppm. The ethanol gas sensor's output voltage is 1.89-3.15 V when the ethanol gas concentration ranges from 8 to 200 ppm. The average response time of the ammonia sensor in the chamber is 13 s slower than that of the sensor directly exposed to the gas being measured, while the average recovery time is 19 s faster. In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. It also outperforms the other models at regression prediction, with smaller absolute, mean absolute, and root mean square errors. Its coefficient of determination (R2) is greater than 0.99, surpassing those of the SVM and RF models. The theoretical and experimental results both indicate that the proposed electronic nose system containing a bionic chamber, when used with the SSA-BPNN, offers a promising approach for detecting ammonia in livestock- and poultry-breeding facilities.

Impact of Nitro Substituents on Dopamine Sensing and Nanostructure Morphology: A Machine Learning Approach for PANI:2- and 3-Nitro-1H-Pyrrole Nanocomposite Sensors

Abstract In this study, we explore the effects of nitro substituents on the morphology and dopamine (DOP) sensing performance of polyaniline (PANI) nanocomposites (NCs). The novelty of the study is the unique integration of 2-nitro-1H-pyrrole (D9A) and 3-nitro-1H-pyrrole (D9B) into PANI to develop advanced non-enzymatic voltammetric sensors, combined with machine learning for DOP sensitivity and morphology analysis. Structural and morphological insights were obtained through comprehensive characterization techniques including ¹H NMR, ¹³C NMR, Fourier transform infrared spectroscopy, scanning electron microscopy, and artificial intelligence-enhanced SEM analysis. The PANI: D9B NCs sensor demonstrated superior DOP detection in the range of 0.625–5 µM, with exceptional sensitivity (329.72 µAµM⁻¹ cm⁻²) and an ultra-low limit of detection of 0.078 µM. Its rapid sensing capability within 1 minute indicates potential for use in biomedical diagnostics. In contrast, the PANI NCs sensor exhibited lower sensitivity, which was linked to higher Zreel values and space charge effects. To further enhance DOP prediction accuracy, we employed machine learning (ML) models—ANN, SVM, XGBoost, and Linear Regression—to analyze sensor outputs, with a focus on feature extraction and multivariate data analysis. Our combined approach provides a robust framework for optimizing nitro-substituted PANI NCs for high-performance sensing applications.

StressFit: a hybrid wearable physicochemical sensor suite for simultaneously measuring electromyogram and sweat cortisol

This study introduces StressFit, a novel hybrid wearable sensor system designed to simultaneously monitor electromyogram (EMG) signals and sweat cortisol levels. Our approach involves the development of a noninvasive skin patch capable of monitoring skin temperature, sweat pH, cortisol levels, and corresponding EMG signals using a combination of physical and electrochemical sensors integrated with EMG electrodes. StressFit was optimized by enhancing sensor output and mechanical resilience for practical application on curved body surfaces, ensuring accurate acquisition of cortisol, pH, body temperature, and EMG data without sensor interference. In addition, we integrated an onboard data processing unit with Internet of Things (IoT) capabilities for real-time acquisition, processing, and wireless transmission of sensor measurements. Sweat cortisol and EMG signals were measured during cycling exercises to evaluate the sensor suite’s performance. Our results demonstrate an increase in sweat cortisol levels and decrease in the EMG signal’s power spectral density following exercise. These findings suggest that combining sweat cortisol levels with EMG signals in real-time could serve as valuable indicators for stress assessment and early detection of abnormal physiological changes.

Biofluorometric Ear-Gas Sensing for Non-Invasive Monitoring of Blood EtOH

Exhaled breath and transcutaneous gas contain volatile organic compounds (VOCs) from blood [1]. Some of these blood VOCs are the resultant products of diseases or metabolisms, which has been attracting significant attention to utilize these VOCs for non-invasive and simple disease screening and metabolism assessment [2,3]. Transcutaneous gas is more suitable to real-time and continuous assessment than breath. Especially, the external ear region has skin with a lower density of sweat glands (140 spots/cm2) compared to other regions of the body, such as the palm, forearm, and cheek. Also, gas from the external ear is not only from an outer part of the external ear but also from the external ear canal. In addition, the external ear has a relatively large space to incorporate devices to measure. In this study, a headset-type ear-gas sensing system including a bio-fluorometric gas sensor has developed using ADH (alcohol dehydrogenase) for external ear-derived EtOH, then the system was applied for continuous measurement of EtOH at the external ear after drinking.The gas monitoring system was developed by combining an over-ear gas collection cell and a biofluorometric gas sensor for EtOH vapor. Alcohol dehydrogenase (ADH) catalyzes the oxidation of EtOH to produce acetaldehyde. Simultaneously, a coenzyme, oxidized form of β-nicotinamide adenine dinucleotide (NAD+), accepts the electron to become the reduced form (NADH) that exhibits autofluorescence (λex=340 nm, λfl=490 nm); therefore, EtOH can be measured by detecting the increase in the autofluorescence from NADH. An enzyme-immobilized membrane (ADH membrane) attached to the flow-cell worked as a gas-liquid diaphragm. When the EtOH vapor reaches the ADH membrane, the catalyzed redox reaction occurs at the membrane together with NAD+ in the buffer solution running above the membrane, which produces NADH in the buffer solution. The resultant NADH was excited by the UV light from the optical fiber, then the emitted fluorescence was collected by the same fiber probe to be detected. In the monitoring of external ear-gas, the sample gas from a subject was collected by the headset type gas collection cell, and then simultaneously transported to the biofluorometric gas sensor for real-time measurement. The gas sensor was composed of excitation and detection units which were connected to a bifurcated optical fiber. The excitation unit has a UV-LED (340 nm) and a bandpass filter (BPF) to make the peak wavelength of the excitation light 340 nm. The detection unit has a photomultiplier tube (PMT) and another BPF to extract fluorescence light from NADH. The flow cell made of PMMA is attached to an optical fiber probe end, that is connected to the bifurcated optical fiber. ADH membrane was attached and fixed at the end of the flow cell by an o-ring. ADH was immobilized by entrapping in a hydrophilic PTFE membrane with poly[2-methacryloyloxyethyl phosphorylcholine (MPC)-co-2-ethylhexyl methacrylate (EHMA)] (PMEH). The sensitivity of the ADH gas sensor and the reproducibility of the sensor output for various ethanol concentrations were investigated. The sensor output was dependent on the concentration of gaseous EtOH. The dynamic range of the ADH gas sensor is 10 ppb-554 ppm of EtOH vapor. The biofluorometric gas sensor indicated the high selectivity, relying on ADH specificity, to gaseous EtOH.The biofluorometric EtOH gas sensor was applied for ear gas sensing.The over-ear gas collection cell was fabricated by modifying a commercial earmuff with two connectors for filtered air (carrier gas) and for sample gas containing ear gas, respectively. Real-time and continuous measurement of external ear-derived EtOH was conducted for healthy subjects with alcohol intake (Approval No. M2018-160 at Tokyo Medical and Dental University). As the results of the ear gas monitoring for the subjects. The EtOH concentration from the external ear started to rise a few minutes after drinking and reached the peak (approx. 100 ppb) 50 minutes later, then the EtOH concentration gradually decreased. This temporal change has a similarity to that of breath EtOH concentration with a few minutes time-delay. According to other reports proving that EtOH concentrations in blood and breath correlate, the concentration of the external ear’s EtOH is most likely correlated to that in blood. In this study, the headset type ear gas sensor successfully measured the transcutaneous ear EtOH-related blood one after drinking. In the future, the ear-gas sensor will be applied to other blood VOCs monitoring for non-invasive assessment of metabolisms and disease conditions that require detailed and real-time information.

Catalytic Combustion Type H2 Gas Sensor Based on Cerium Oxide

Hydrogen gas is the most promising clean energy source, but its use carries an explosion hazard (LEL: 4 vol%). Therefore, the real-time and accurate detection of H2 gas leakage at every emitting site is required with the compact sensor to prevent a critical accident. Among the compact H2 gas sensors proposed, the catalytic combustion type gas sensor using precious metal-loaded γ-Al2O3 such as Pt/Al2O3 as the catalyst is one of the sensors in practical use because of its long life and low-cost features. However, platinum is an expensive and scarce resource, and its use is desired to be reduced. Therefore, for the realization of the coming hydrogen society, the development of novel catalytic combustion type sensor using a noble-metal-free catalysts is important.In this study, we focused on CeO2, Fe2O3, Co3O4, NiO as H2 oxidation catalyst and investigated the sensing performance of these metal oxides. In addition, we applied CeNbO4 +δ [1], having high oxide ion conductivity and redox property, as a promoter for CeO2 catalyst, and the H2 gas sensing performance of the sensor with 4 wt% CeO2/CeNbO4 +δcatalyst was investigated.Among the sensors applied metal oxide (CeO2, Fe2O3, Co3O4, NiO), the sensor with CeO2 showed highest sensor output which is over 2 times higher than the sensors with other metal oxides. This is due to high specific surface area and low heat capacity of CeO2. Although the sensor using CeO2 showed good sensing performance, quantitative detection of H2 required a temperature of 105 °C or higher, which is still high for practical use.To improve the H2 oxidation activity of CeO2, we applied CeNbO4 +δ as a promoter and the sensor with 4 wt% CeO2/CeNbO4 +δcatalyst. Figure 1(a) displays the response curves to H2 gas concentration change for the sensor applied 4 wt% CeO2/CeNbO4 +δ and CeO2. The sensor with 4 wt% CeO2/CeNbO4 +δcatalyst showed about 2.6 times higher sensor output compared to the sensor with only CeO2, indicating that the combination of CeNbO4 +δ improves H2 oxidation activity. Furthermore, the sensor with 4 wt% CeO2/CeNbO4 +δcatalyst was able to detect H2 gas concentration quantitatively even at 75 °C (Fig. 1(b)) with a 90% response time of about 14 seconds. The sensitivity to other gases such as carbon monoxide, methane, ethanol, and acetaldehyde was examined, and the sensor showed excellent selectivity for H2 at 75 °C.[1] Y. Jin, Y. Quan, J. Liu, C. Qui, P. Pan, B. Shan, H. Luo, and P. Yang, J. Solid State Chem., 313, 123318 (2022). Figure 1

Advancing Pouch Cell Monitoring: The Promise of Surface-Applied and Referenceless Fiber Bragg Grating Sensors

In the rapidly evolving field of lithium-ion battery technology, ensuring safety and optimizing performance have become paramount concerns, especially in applications that demand high performance and reliability, such as electric vehicles and portable electronics [1]. Established and state-of-the-art monitoring of voltage, current, and a sparse distribution of surface temperature sensors are often insufficient for early fault detection [2,3].To address this challenge, this study presents an innovative approach to battery monitoring using referenceless fixed Fiber Bragg Grating (FBG) sensors mounted on the surface of pouch cells. This method takes advantage of the dual functionality of FBG sensors to measure both strain and temperature directly on the exterior of the cell, offering a promising avenue for real-time battery health and safety monitoring [4].Pouch cells, favored for their lightweight and flexible design, present unique challenges for adding robust strain monitoring technologies due to the anisotropic nature of their functionally optimized foil enclosures. Our research addresses these challenges by detailing an innovative and systematic surface application method for FBG sensors that minimizes intrusion while providing stable conditions on the pouch foil. This method's small form factor and cost-effectiveness are significant advantages, providing a non-invasive means of gathering high-density information about the cell's operational state without compromising its structural integrity.A critical aspect of this study is the on-cell calibration technique developed for the FBG sensors, which enables the unambiguous sensor reading across different positions without the usually required additional reference sensors. This breakthrough addresses a common limitation in FBG monitoring systems for batteries by ensuring that sensor outputs are reliably correlated with specific strain and temperature parameters, respective of their placement on the cell. The calibration process, thoroughly documented in our experiments, underpins the sensors' capability to provide accurate, real-time data crucial for predictive maintenance and fault detection. Furthermore, the study explores the limitations imposed by the pouch foil's mechanical properties, presenting a comprehensive analysis of sensor performance concerning these constraints. Through extensive testing, we establish guidelines for optimizing sensor placement and adhesion techniques to ensure reliable measurements without affecting cell performance.Our experimental findings highlight the FBG sensors' dynamic temperature measurement capabilities, demonstrating sufficient sensitivity to detect abnormal strain developments indicative of potential battery faults. The dual-measurement approach not only allows for the early detection of issues such as thermal runaway but also provides valuable insights into the development of mechanical stresses experienced by the cell during operation. Such insights are instrumental in advancing our understanding of battery behavior under various load conditions, contributing to the development of safer and more efficient battery systems.In conclusion, the improved surface application of reference-less fixed FBG sensors on pouch cells represents a significant advancement in battery monitoring technology. The small form factor, cost-efficiency, and high information density offered by this setup make it an attractive solution for manufacturers and researchers alike. By providing a means to accurately monitor strain and temperature in real-time, our approach facilitates improved safety protocols and performance optimization for lithium-ion batteries. Due to the possible omission of an additional sensor, and higher information density compared to just temperature or strain sensors, this study lays the groundwork for industrially used FBG sensor applications and calibration techniques, paving the way for the broader adoption of FBG sensors in battery safety and performance monitoring systems.

(Invited) Design of Three-Dimensional Reaction Sites of Solid-Electrolyte Gas Sensors for Sensitive Detection of VOCs

The exhaled breath of patients contains a higher concentration of specific volatile organic compounds (VOCs) than that of healthy peoples, and thus the development of highly sensitive gas sensors for these gases enables the early detection of these diseases. Among the various gas sensors, we focused on solid-electrolyte gas sensors consisting of yttria-stabilized zirconia (YSZ) as an oxide-ion conductor, and we previously reported that the toluene response of YSZ-based gas sensors improved by the addition of CeO2 to the Au sensing electrode (SE) [1].The toluene-response mechanism of the YSZ-based gas sensors is explained on the basis of the mixed potential theory. The sensor output voltage (E) is the potential difference between the SE and the counter electrode (CE). The SE potential of the sensor negatively shifted by the concurrent electrochemical reactions of toluene oxidation (eq. 1) and oxygen reduction (eq. 2) at the interface between YSZ and SE (triple-phase boundaries, TPBs) with the same rate upon exposure to toluene balanced with air.C7H8 + 18O2− → 7CO2 + 4H2O + 36e− (1)2O2− → O2 + 4e− (2)On the other hand, a given amount of toluene is oxidized during gas diffusion in the SE, which decreases the concentration of toluene reaching the TPBs. Since this reduces the toluene response of the sensor, thin-film SEs were fabricated in the thickness range of 30–250 nm by using spin-coating method employing a solution containing Au and Ce components in order to increase toluene concentration at the TPBs. However, the toluene response of the fabricated sensors largely increased with an increase in the SE thickness when the additive amount of CeO2 was relatively high [2–3]. The obtained results suggested that the electrochemical reactions of the toluene proceeded not only at the interface (SE/YSZ/gas) but also at the interface between SE and gas (SE(Au/CeO2)/gas) in the SE, because CeO2 worked as an oxide-ion conductor at elevated temperatures.In this study, we focused on the design of three-dimensional reaction sites of YSZ-based gas sensors for sensitive detection of VOCs. We fabricated Au SEs containing a sufficient amount of CeO2 (n) by spin-coating method. The obtained SEs were denoted as Au-nCeO2(x) SEs (n: an additive amount of CeO2, 24, 32 (wt%), x: the number of spin-coating cycles, 5–15).Figure 1(a) shows the response transient to various concentrations of toluene of the Au-24CeO2(5) sensor in dry air. The E value decreased upon exposure to toluene, and the magnitude of the response (ΔE) negatively increased with an increase in the toluene concentration. Figure 1(b) shows the thickness (spin-coating cycle) dependences of ΔE to 50 ppm toluene of the fabricated sensors in dry air. The ΔE value of the Au-24CeO2(x) sensor increased with a decrease in the SE thickness, and the Au-24CeO2(5) sensor showed the largest response (ca. 256 mV). In contrast, the ΔE of the Au-32CeO2(x) sensor increased with an increase in the SE thickness. In addition, we performed electrochemical impedance measurements in order to quantify the number of TPBs, and we discussed the effects of the number of TPBs on the toluene response. The details of this will be explained in the presentation.