Sensor Suite Research Articles

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Abstract Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100kHz and 20kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors

The quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle path pavement types, such as asphalt, cobblestone, concrete, and paving tiles, were analyzed across selected streets. A smartphone application (Physics Toolbox Sensor Suite) and SEE.SENSE smart bicycle lights were used to collect GPS and vertical acceleration data on the bicycle paths. The Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from the data collected through the Physics Toolbox Sensor Suite were calculated to quantify the vibrational comfort experienced by cyclists. In addition, the data collected from the SEE.SENSE smart bicycle light, DCI, and RMS computed results were categorized for a statistical comparison. The findings of the statistical tests revealed no significant difference in the comfort assessment among DCI, RMS, and SEE.SENSE. The study highlights the potential of integrating smartphone sensors and smart bicycle lights for efficient, large-scale assessments of bicycle infrastructure, contributing to more informed urban planning and improved cycling conditions. It also provides a low-cost solution for the city authorities to continuously assess and monitor the quality of their cycling paths.

Long-term observations of a sponge in situ reveal a rich repertoire of contractile behaviors, including winter dormancy

Sponges are remarkably active animals with a range of behaviors that are broadly termed sneezes. Little is known about what triggers sneezes or how sneezes propagate in an animal that lacks either muscular or nervous systems. We explored the triggers of sponge behavior in situ in the demosponge Suberites concinnus. The placement of this tennis ball-sized sponge at the focal point of a camera array on an instrument platform in Barkley Sound, BC, Canada, provided a unique opportunity for our investigation. The observatory has a suite of environmental sensors measuring parameters such as photosynthetically active radiation (PAR), oxygen, turbidity, salinity, pressure, current speed, and temperature. An array of 8 cameras designed for 3D imaging was used to capture hourly images. Our findings revealed both long-term (annual) and short-term (daily) behaviors in S. concinnus. The sponge contracted for 2 mo each winter. In February, it gradually expanded and became very active with short-term full-body contractions each lasting 11 h through the summer. A meta-analysis of contraction behaviors in other species of sponge showed that contraction duration is a factor of sponge size and that all sponges contract faster than they expand. Invertebrates and fish were frequently in contact with the sponge, but no single interaction was found to correlate with patterns of sponge behavior. However, significant correlations between sponge behaviors and chlorophyll and turbidity were found using generalized additive mixed models. Our study underscores the dynamic nature of these sessile filter feeders and their responses to their environment.

Detection resolving power of SiC Timepix3 detector to electrons, neutrons, ions and protons

Silicon Carbide is a suitable semiconductor sensor for radiation measurement and nuclear applications. It has been recently implemented as a position-sensitive and radiation imaging device coupled to the Timepix3 ASIC chip in the form of a miniaturized radiation camera MiniPIX-Timepix3 SiC. In this work we systematically evaluate the detection resolving power to different radiation species: electrons, fast neutrons, ions and protons. Experimental calibrations were made at well-defined reference fields in terms of particle type, energy and direction. The spectral-sensitive tracking response and characteristic morphology of the particle tracks are analyzed by pattern recognition algorithms. The low thickness of the radiation sensitive volume (65 μm) of the SiC sensor limits the directional tracking response and angular resolution. Three broad classes of particle-type events are resolved. The detector together with suitable data processing can be used for radiation dosimetry and particle tracking tasks in space, particle therapy, nuclear physics and nuclear reactors and particle accelerator environments.

Adsorption of sarin on zigzag boron-nitride nanotubes (BNNTs) via DFT

To find a suitable sensitivity sensor for sarin molecule (GB) as a nerve agent, we have compared and contrasted the adsorption behavior of it on the exterior surfaces of the (4,0), (5,0), (6,0), (7,0) and (8,0) BNNTs, at B3LYP/6-311+G*. The equilibrium distances (RGB-BNNT), Eads., FMOs, Eg, DOS, D.M. (μ) and reactivity index are estimated for the adsorption process of GB on these BNNTs. The RGB-BNNT, Eads. and the electronic properties of the BNNTs are depend on the BNNT diameter and orientation of the GB molecule outside BNNTs. The shorter RGB-BNNT of 1.61 Å and Eads. of −12.58 kcal/mol reveals that the 40O conformer is the most favorable configuration in which oxygen atom of PO in GB molecule is situated above a boron atom of (4,0) BNNT. The longer RGB-BNNT of 3.81 Å and Eads. of −2.31 kcal/mol exhibits that the 80F conformer is the least favorable configuration in which florin atom of PF in GB molecule is situated above a boron atom of (8,0) BNNT. However, adsorption process is exothermic. By increasing of the BNNT’s diameter and in going from (4،0) to (8،0) GB-BNNT complex either for the orientated PO or PF site; Eg and D.M. values are increased via the adsorption process. Henceforth, the designed 40O and 80F systems energetically are anticipated as the most and the least favorable complex, via the strong chemisorption and physisorption, respectively.

Research on on-site real-time detection technology of transmission line pulling force based on sensors

During the transmission process, the stay wire tower plays an important role in supporting and stabilizing. By monitoring the tension of the stay wire, the deformation and abnormal force of the stay wire tower structure can be detected in a timely manner, and measures can be taken to adjust and repair it. In terms of wire tension monitoring, the main methods currently used include various monitoring technologies such as electronic, mechanical, and optical. However, traditional cable force monitoring methods have problems such as low monitoring accuracy and poor real-time performance, so it is necessary to develop a real-time detection technology based on sensors. This article uses computer technology to achieve real-time detection of transmission line pulling force based on sensors. Firstly, select a suitable sensor for measuring the pulling force. Then, the sensor data is transmitted to the computer system for real-time processing and analysis. Finally, the results are displayed through the interface, facilitating real-time monitoring and control by monitoring personnel. After experimental verification, the real-time detection technology based on sensors proposed in this article can accurately monitor the pulling force of transmission lines. The monitoring results show that this technology has high accuracy and good real-time performance, and can timely detect and handle abnormal wire force situations.

Development of 6DOF Hardware-in-the-Loop Ground Testbed for Autonomous Robotic Space Debris Removal

This paper presents the development of a hardware-in-the-loop ground testbed featuring active gravity compensation via software-in-the-loop integration, specially designed to support research in autonomous robotic removal of space debris. The testbed is designed to replicate six degrees of freedom (6DOF) motion maneuvering to accurately simulate the dynamic behaviors of free-floating robotic manipulators and free-tumbling space debris under microgravity conditions. The testbed incorporates two industrial 6DOF robotic manipulators, a three-finger robotic gripper, and a suite of sensors, including cameras, force/torque sensors, and tactile tensors. Such a setup provides a robust platform for testing and validating technologies related to autonomous tracking, capture, and post-capture stabilization within the context of active space debris removal missions. Preliminary experimental results have demonstrated advancements in motion control, computer vision, and sensor fusion. This facility is positioned to become an essential resource for the development and validation of robotic manipulators in space, offering substantial improvements to the effectiveness and reliability of autonomous capture operations in space missions.

Photodiode-based focus monitoring in ultrashort-pulsed laser structuring of graphite anodes for lithium-ion batteries

In recent years, there has been an increased demand for elaborate monitoring techniques in laser material processing. This has been driven by the need for fast and cost-efficient quality assurance processes. At the same time, ultrashort-pulsed (USP) laser radiation has emerged as a promising technology for creating intricate microstructures in lithium-ion battery graphite anodes due to its high precision and negligible thermal impact. However, the integration of process monitoring in USP laser applications for graphite anode structuring is still unexplored. There is a lack of clarity on suitable sensors, observable parameters, and extractable process-relevant insights. The presented study addressed this gap by demonstrating the capability of state-of-the-art photodiode-based monitoring systems in collecting process-relevant data and deriving valuable insights. A sensor equipped with three photodiodes was employed to address these challenges. Exploratory data analysis and machine learning methodologies were leveraged to develop a data pipeline for processing the acquired information. The data were used to train convolutional neural networks that could accurately predict the focal position. At the same time, the limitations of traditional regression approaches could be shown. The findings advanced the understanding of the possibilities of process monitoring in USP laser applications and emphasized the significance of data-driven approaches in optimizing manufacturing processes.

DESIGN AND FABRICATION OF SMART ENVIRO GUARDIAN

This project tackles the challenge of maintaining a comfortable and healthy indoor environment in the face of unpredictable weather conditions. The primary objective is to develop an automated weather monitoring system integrated with a humidifier, leveraging Internet of Things (IoT) technology and a suite of sensors including the DHT11 for temperature and humidity, a rain sensor, an LDR sensor, and a BMP180 barometric pressure sensor. The system aims to monitor real-time weather data and adjust indoor conditions accordingly, enhancing comfort and reducing health risks such as respiratory issues, allergies, and mold growth. Additionally, it provides remote monitoring and real-time alerts through IoT connectivity, improving user convenience and energy efficiency. The methodology includes detailed requirement analysis, hardware and software development, testing, and deployment. This project promises to significantly improve indoor environmental quality, especially in regions with erratic weather patterns, by providing a robust, adaptable solution. Keywords: Automated Weather Monitoring, Climate Control, IoT Technology, Humidifier, DHT11 Sensor, Rain Sensor, LDR Sensor, Energy Efficiency

Validation of sound source localization quality in binaural audio synthesis with head-tracking

Binaural synthesis with head tracking has become a highly important tool in creating an auditory experience for users that is as similar as possible to their perception of sound in the real world. This is crucial in many different applications that rely on accurate representation of real or virtual sound environments, ranging from scientific research to gaming applications. The quality of the binaural synthesis process and the accuracy and naturalness of the produced result depend on numerous factors, ranging from the selection of suitable IMU sensor technology used for head tracking to the choice of methods for implementing binaural synthesis using generic or individual sets of Head-Related Transfer Functions (HRTF). This is especially significant in Virtual Reality (VR) systems with three or six degrees of freedom that fully immerse the users in the virtual world through visual and auditory stimuli. This paper presents the authors' experiences in addressing a variety of challenges that arose during the design of such spatial audio systems and the testing of sound source localization quality. Strong emphasis is put on the testing of sound source localization quality, where the position of the source dynamically changes in relation to the listener.

Performance of Soil Moisture Sensors at Different Salinity Levels: Comparative Analysis and Calibration.

Soil dielectric sensors have been widely used to obtain real-time soil moisture data, which are important for water resource management. However, soluble salts in the soil significantly affect the accuracy of these sensor measurements. Therefore, it is crucial to select suitable soil dielectric sensors for soil moisture measurements at different salinity levels. Eight mainstream sensors (EC-5, 5TE, Teros12, Hydra-probe II, TDR315L, TDR315H, TDR305H, and CS655) were selected and tested at four different soil salinity levels (EC1:5 = 3.0, 1.5, 1.0, and 0.75 dS·m-1). The measured values using the factory calibration formulas were compared at six soil moisture levels. The results showed that the measured soil moisture values from various sensors exhibited varying degrees of overestimation, which increased with increasing salinity. Only EC-5 did not exhibit distortion at high-salinity levels, with the measured values showing a good linear trend compared to the standard values. Mutational distortion of the measured apparent dielectric permittivity occurred in TDR315L, TDR315H, Hydra-probe II, and 5TE at EC1:5 = 3.0 dS·m-1. Insensitive distortion of the measured apparent dielectric permittivity occurred in Teros12 and TDR305H at EC1:5 = 3.0 dS·m-1 as well as in Teros12, TDR305H, 5TE and Hydra-probe II at EC1:5 = 1.5 dS·m-1. All tested sensors performed reasonably well at EC1:5 ≤ 1.0 dS·m-1. Seven sensors (excluding CS655) were calibrated within the distortion threshold. The soil moisture accuracy using the calibrated formulas could reach ±0.02 cm3·cm-3. At EC1:5 ≤ 1.0 dS·m-1, most sensors in this study could be applied with the factory calibration formulas. TDR series, EC-5, 5TE and Teros12 were recommended after calibration for EC1:5 > 1.0 dS·m-1. For extremely high soil salinity levels, the TDR series and EC-5 may be the best choices.

A High-Reliability Photoelectric Detection System for Mars Sample Return’s Orbiting Sample

The Mars Sample Return campaign is an endeavor of unprecedented technological complexity and coordination that attempts to answer fundamental questions about the habitability of Mars by returning the first samples of Martian material to Earth for analysis. The third mission in the campaign consists of the NASA-provided Capture, Containment, and Return System (CCRS) onboard the European Space Agency’s Earth Return Orbiter, which will retrieve the Orbiting Sample (OS) container from its orbit around Mars. Retrieving a passive sample container from a planetary orbit has never been attempted by any spacecraft and requires the development of new technology to succeed in this ambitious task. This paper introduces the high-reliability Capture Sensor Suite (CSS), a novel optical detection system that provides CCRS with the capability to autonomously detect the OS as it is captured. This article will discuss the challenges and requirements for the fault-tolerant design of the CSS.

A novel sensor preference method for proton exchange membrane fuel cell flooding fault diagnosis based on multi-algorithm

ABSTRACT During periods of high-power operation, proton exchange membrane fuel cell (PEMFC) produce water, which gradually accumulates at the cathode. It causes flooding faults and significantly impacts on PEMFC safety. The data collected by the sensors is a fundamental prerequisite for the flooding faults diagnosis in PEMFC. Consequently, it is critical to select sensors. However, the conventional methods for selecting sensors consider sensitivity and noise immunity, the number of sensors selected and the flood diagnostic performance are not optimized. To solve the above problems, this paper proposes a sensor preference method that combines the Mantel test and Spearman coefficient (MS method). This method first uses the Mantel test to analyze the correlation between two flooding evaluation indicators and the sensors, it identifies suitable sensors for diagnosing flooding faults. Subsequently, the Spearman coefficient eliminates redundant sensors. Additionally, a one-dimensional convolutional neural network is employed to construct a flooding diagnosis model. The MS method is contrasted with two traditional sensor selection methods. The results show that the MS method selected sensors yield superior model training and enhanced accuracy in diagnosing flooding, thereby ensuring PEMFC safety.

Signal Processing Method to Synthesize Eddy Current Sensors

Abstract This paper focuses on the development of a novel method for synthetizing an eddy current sensor using post-processing techniques based on the linear superposition of multiple parallel acquisition signals. The proposed method will be demonstrated in the context of a cross-wound sensor (CWS) made of a flexible printed circuit board (PCB). Traditionally, the configuration and detection orientation of an eddy current sensor are determined during the fabrication process, which limits the flexibility and versatility of the sensor. However, the method developed in this research enables the post-processing fabrication of the sensor, increasing the customization potential and adaptability. Furthermore, the proposed method also enables the production of a suitable lift-off sensor (LOS) for dynamic lift-off compensation (DLOC). DLOC is a crucial aspect of eddy current sensing as it helps to account for the distance between the sensor and the target surface, which can affect measurement accuracy. The ability to configure the CWS detection orientation with DLOC capabilities in post-processing offers significant advantages in terms of sensor performance and versatility. Experimental results will be presented that demonstrate the effectiveness of the proposed method in enabling post-processing fabrication of an eddy current sensor featuring configurable detection orientation and DLOC capabilities.

IoT based Solar Panel Cleaning Rover

The primary objective of this proposed study is to design a system for effectively maintaining the cleanliness of solar panels, thereby enhancing their performance and longevity through an Internet of Things-based solar panel cleaning rover equipped with a sensors, and manual automation capabilities. A suite of sensors, including dust sensors, enables the rover to gather relevant data about the cleanliness status and ambient conditions surrounding the solar panels. Manual automation features empower users to intervene and control the rover's movements and cleaning actions as needed, providing flexibility and customization in operation. Furthermore, the rover is designed to connect directly with water tanks, facilitating an autonomous water supply for the cleaning process. A sponge brush mechanism is integrated into the rover's design to effectively remove dust from the solar panel surfaces. The proposed work has developed a prototype of the IoT-based solar panel cleaning rover, which has been tested to demonstrate its effectiveness in improving solar panel efficiency and reducing maintenance efforts.

Signal‐Amplifying Biohybrid Material Circuits for CRISPR/Cas‐Based Single‐Stranded RNA Detection

AbstractThe functional integration of biological switches with synthetic building blocks enables the design of modular, stimulus‐responsive biohybrid materials. By connecting the individual modules via diffusible signals, information‐processing circuits can be designed. Such systems are, however, mostly limited to respond to either small molecules, proteins, or optical input thus limiting the sensing and application scope of the material circuits. Here, a highly modular biohybrid material is design based on CRISPR/Cas13a to translate arbitrary single‐stranded RNAs into a biomolecular material response. This system exemplified by the development of a cascade of communicating materials that can detect the tumor biomarker microRNA miR19b in patient samples or sequences specific for SARS‐CoV. Specificity of the system is further demonstrated by discriminating between input miRNA sequences with single‐nucleotide differences. To quantitatively understand information processing in the materials cascade, a mathematical model is developed. The model is used to guide systems design for enhancing signal amplification functionality of the overall materials system. The newly designed modular materials can be used to interface desired RNA input with stimulus‐responsive and information‐processing materials for building point‐of‐care suitable sensors as well as multi‐input diagnostic systems with integrated data processing and interpretation.

Alkali metal-doped C20 fullerene sensors for COVID-19 biomarker detection: DFT insights into naked-eye and infrared techniques

Prompt diagnosis and prevention from coronavirus disease (COVID-19), a highly contagious infection, are critically required. While PCR testing is effective, it is costly, painful, and time-consuming. Alternatively, breath analysis for detecting COVID-19 provides a cost-effective and instant diagnostic approach. COVID-19 infected patients exhale ethyl butyrate (EB) as a biomarker of the infection. This study explores the doping of C20 fullerene with alkali metals to enhance its ability to detect ethyl butyrate. EB was weakly adsorbed onto C20 and strong adsorbed on doped fullerenes (M@C20) with maximum adsorption energies of −27.66 kcal/mol for Li@C20. QTAIM and IRI analyses confirmed Van der Waals interactions between EB and M@C20. NBO and MEP analyses provided evidence of charge transfer from the biomarker towards the M@C20. This charge transfer decreased the carbonyl bond stretching frequency from 1838 cm−1 to 1772, 1782, and 1788 cm−1 in three complexes (EB−Li@C20, EB−Na@C20, and EB−K@C20), respectively. This IR analysis supports the potential application of doped fullerenes in infrared-based detectors. Absorption maxima (λmax) were calculated as 534 nm (green), 594 nm (yellowish orange), and 587 nm (yellow) for three complexes. These shifts indicate potential application of M@C20 in naked-eye detectors. The shortest recovery time was 2.9 × 10−11 s for K@C20, suggesting it a suitable reusable sensor. These findings enable the development of fullerene-based sensors for both naked-eye and infrared-based detection of COVID-19.

Highly selective optical and naked-eye recognition of L-cystine through spectroscopy and development of cellulose paper nano biosensor test strips for the early diagnosis of cystinuria

Accurate sensing of small biomolecules from complex biofluid media remains a challenge due to adverse interaction of metallic salts and other biofluid components. L-cystine is the most important biomarker for the disease cystinuria. Recognition of L-cystine in urine is thus fundamental for the timely detection of cystinuria and related chronic kidney disease (CKD). Poor solubility of L-cystine offer limits to design suitable sensors. Herein, we report the synthesis of thioglycolated-β-CD (TG-β-CD) anchored silver nanoparticles (AgNP) as a potential receptor (TG-β-CD⊃AgNP) for the effective optical recognition and quantification of L-cystine through UV–Vis and SERS (surface enhanced Raman spectroscopy) spectroscopic fingerprinting and suitable colourimetric naked-eye detection of L-cystine by indicator displacement assay. Methyl orange (Met-O) indicator was precomplexed with the receptor to generate the probe solution (Met-O@TG-β-CD⊃AgNP). Indicator has been exchanged from Met-O@TG-β-CD⊃AgNP by the addition of L-cystine, with an instantaneous visible colour change from yellow to red. The method is tolerable to other interfering abundant ions and biomolecules. Based on the innovative sensing assay a cellulose paper-dye test strip is developed for point-of-care detection and quantification of the biomarker. Structures of TG-β-CD, TG-β-CD⊃AgNP and Met-O@TG-β-CD⊃AgNP were elucidated by UV–Vis, FT-IR, PXRD, 1H NMR, etc. spectroscopy. Scanning electron microscopy (SEM) was used to examine the morphology of TG-β-CD, AgNP and TG-β-CD⊃AgNP. The chemical changes during the assay were evaluated by conductance, UV–Vis and SERS. The competitive displacement of the indicator and UV turn-on at ppm level analyte concentration made the process compatible for day-to-day point-of-care units.

Using Physiological Biomarkers to Optimize Management of TBI in Austere Environments.

Multimodal monitoring is the use of data from multiple physiological sensors combined in a way to provide individualized patient management. It is becoming commonplace in the civilian care of traumatic brain-injured patients. We hypothesized we could bring the technology to the battlefield using a noninvasive sensor suite and an artificial intelligence-based patient management guidance system. Working with military medical personnel, we gathered requirements for a hand-held system that would adapt to the rapidly evolving field of neurocritical care. To select the optimal sensors, we developed a method to evaluate both the value of the sensor's measurement in managing brain injury and the burden to deploy that sensor in the battlefield. We called this the Value-Burden Analysis which resulted in a score weighted by the Role of Care. The Value was assessed using 7 criteria, 1 of which was the clinical value as assessed by a consensus of clinicians. The Burden was assessed using 16 factors such as size, weight, and ease of use. We evaluated and scored 17 sensors to test the assessment methodology. In addition, we developed a design for the guidance system, built a prototype, and tested the feasibility. The resulting architecture of the system was modular, requiring the development of an interoperable description of each component including sensors, guideline steps, medications, analytics, resources, and the context of care. A Knowledge Base was created to describe the interactions of the modules. A prototype test set-up demonstrated the feasibility of the system in that simulated physiological inputs would mimic the guidance provided by the current Clinical Practice Guidelines for Traumatic Brain Injury in Prolonged Care (CPG ID:63). The Value-Burden analysis yielded a ranking of sensors as well as sensor metadata useful in the Knowledge Base. We developed a design and tested the feasibility of a system that would allow the use of physiological biomarkers as a management tool in forward care. A key feature is the modular design that allows the system to adapt to changes in sensors, resources, and context as well as to updates in guidelines as they are developed. Continued work consists of further validation of the concept with simulated scenarios.

Piezoresistive-piezocapacitive hybrid pressure sensor based on synergetic MXene porous conducting and ion trapping effects for operando battery state-of-charge monitoring

Battery pressure evolution provides essential information for battery state diagnosis. However, challenges still exist for achieving suitable pressure sensors to realize precise operando battery pressure monitoring. Herein, we develop a flexible hybrid pressure sensor by sandwiching one ionic piezocapacitive layer between two piezoresistive electrode networks. By taking advantage of the synergistic resistance and capacitance variation effects of the sensor upon compression by the interwoven conductive MXene fibers and the ionic nanofibers with MXene ion trappers, a remarkably improved sensing performance is achieved with a low detection limit of 4 Pa, an excellent durability under both small and ultrahigh pressures for over 20 000 cycles, and high sensitivities over an ultrawide detection range of 0–1 MPa. Meanwhile, the developed sensor is soft and stretchable, ensuring a convenient and conformal attachment to batteries of various shapes without in-plane strain interference. Towards practical application, the hybrid sensor is used to monitor the pressure evolution of lithium-ion battery pouch cells under different constrained pressures, and highly sensitive response is gained, through which the state of charge (SoC) of the battery during normal operation can be precisely estimated. When the battery is slightly overcharged, an instant warning can be made upon the diagnosis of SoC of even a few percentages over 100 %. Its application can be expanded to other areas where high sensitivity across an ultrawide detection range is demanded.