Pressure Sensor Research Articles

Strain-Insensitive Supercapacitors for Self-Powered Sensing Textiles.

Yarn-based supercapacitors and sensors can be easily integrated into textiles to form flexible and lightweight self-powered wearable electronic devices, which enable stable and continuous signal detection without an external power source. However, most current supercapacitors for self-powered systems lack the stretchability to adapt to complex human body deformations, which restricts their application as a stable wearable power source. This study presents a high-performance strain-insensitive yarn supercapacitor via prestretching in situ polymerization strategy, which can be integrated into self-powered wearable sensing textiles. The supercapacitor delivers a high specific capacitance of 20.79 mF cm-1 (116.94 F g-1), a power density of 37.54 μW cm-1 (211.22 W kg-1), and an energy density of 1.85 μWh cm-1 (10.39 Wh kg-1). The strain-insensitive ability is demonstrated with nearly unchanged performance at a high static strain of 200%, dynamic strain rates of 10% s-1, and retains 96.46% of its capacitance after 3500 cycles under 50% strain. The pressure sensor, featuring a striped coating structure, shows a high sensitivity of 0.67 kPa-1 and a short response time of 100 ms. The strain-insensitive yarn supercapacitors with superior reliability serve as an energy source to power pressure sensors that efficiently recognize Morse code, showing great potential in truly wearable health monitoring and rehabilitation training applications.

Explainable deep learning approach for recognizing “Egyptian Cobra” bite in real-time

Abstract The Egyptian cobra is among the deadliest snake species, capable of causing death within a short span of 15 min. Also, every snake species has its own anti-venom type. So, a quick identifying the Egyptian Cobra bite from other snake species is a challenging and critical task. This research employs Internet of things (IoT) and deep learning methods to precisely recognize bites of Egyptian cobra, in the real-time, by analyzing images of the bite marks. We deploy IoT-enabled wearable devices equipped with sensors capable of detecting snake bites, whereas these sensors measure changes in physiological parameters indicative of a snakebite, such as heart rate, blood pressure, and temperature sensors based on our proposed mathematical algorithm. Also, we present a real case study in which we used our mathematical algorithm to determine based on its sensor readings whether the victim was exposed to a snake bite or not in the real-time. These wearable devices can be worn by individuals working or living in areas prone to snake encounters, such as farmers. When a snake bite occurs, the IoT sensors embedded in the wearable devices will immediately detect the bite and transmit real-time data, including vital information about the bite marks, to a central monitoring system or victim relative. Also, we assembled a dataset consisting of 500 images depicting Egyptian cobra bites and 600 images of bites from various other snake species indigenous to Egypt. To bolster the model’s trustworthiness and facilitate understanding of its decisions, we employed the contemporary method of explainable deep learning. Also, notably, our methodology yielded an accuracy of 90.9%.

Deep Neural Network for Valve Fault Diagnosis Integrating Multivariate Time-Series Sensor Data

Faults in valves that regulate fluid flow and pressure in industrial systems can significantly degrade system performance. In systems where multiple valves are used simultaneously, a single valve fault can reduce overall efficiency. Existing fault diagnosis methods struggle with the complexity of multivariate time-series data and unseen fault scenarios. To overcome these challenges, this study proposes a method based on a one-dimensional convolutional neural network (1D CNN) for diagnosing the location and severity of valve faults in a multi-valve system. An experimental setup was constructed with 17 sensors, including 8 pressure sensors at the inlets and outlets of 4 valves, 4 flow sensors, and 5 pressure sensors along the main pipe. Sensor data were collected to observe the sensor values corresponding to valve behavior, and foreign objects of varying sizes were inserted into the valves to simulate faults of different severities. These data were used to train and evaluate the proposed model. The proposed method achieved robust prediction accuracy (MAE: 0.0306, RMSE: 0.0629) compared to existing networks, performing on both trained and unseen fault severities. It identified the location of the faulty valve and quantified fault severity, demonstrating generalization capabilities.

A Comprehensive Review on Fabrication and Structural Design of Polymer Composites for Wearable Pressure Sensors

A Comprehensive Review on Fabrication and Structural Design of Polymer Composites for Wearable Pressure Sensors

Concept Protocol for Developing a DAid® Smart Socks-Based Biofeedback System: Enhancing Injury Prevention in Football Through Real-Time Biomechanical Monitoring and Mixed Reality Feedback

Football players, particularly in youth leagues, face a high risk of lower limb injuries due to improper movement patterns. While programs like FIFA 11+ help reduce injuries, they lack real-time, personalized feedback for biomechanical correction. This concept protocol outlines the development of a DAid® smart socks-based biofeedback system that integrates biomechanical monitoring with mixed reality (MR) feedback to enhance injury prevention. The DAid® ️ smart socks, equipped with pressure sensors and inertial measurement units (IMUs), track plantar pressure distribution and the center of pressure (COP). Real-time feedback is delivered via a Meta Quest 3 MR headset, enabling athletes to adjust movement patterns instantly. This protocol establishes a framework for evaluating the system’s feasibility and effectiveness in optimizing biomechanics and reducing injury risks. By combining wearable technology with MR-based feedback, this study advances injury prevention strategies, with potential applications in rehabilitation and performance training.

Characterizing Six Percolation Cases in Flexible Electronic Composites: A Monte Carlo-Based 3D Compressive Percolation Model for Wearable Pressure Sensors

This study employs a Monte Carlo-based 3D compressive percolation model to systematically analyze the electrical behavior of flexible electronic composites under compressive deformation. By simulating the spatial distribution and connectivity of conductive particles, this study identifies six distinct percolation cases, each describing a unique connectivity evolution under strain. The model reveals that excessive initial connectivity leads to saturation effects, reducing sensitivity, while a high Poisson’s ratio (≥0.3) causes connectivity loss due to shear plane expansion. Notably, asymmetric particle shapes, such as cylinders and rectangles, exhibit superior percolation behavior, forming infinite clusters at lower strain thresholds (~0.4) compared to spherical particles (~0.5). Monte Carlo simulations with 3000 particles validate these findings, showing consistent trends in percolation behavior across different deformation states. By classifying and quantifying these six connectivity scenarios, this research provides a structured framework for optimizing flexible sensor designs, ensuring an optimal balance between conductivity and sensitivity. These findings contribute to advancing flexible electronics, particularly in wearable health monitoring, robotics, and smart textiles.

Roll‐to‐Roll (R2R) Processing of Large‐Area Flexible Piezoresistive Films with Magnetically Z‐Aligned Nickel Microcolumnar Morphology

AbstractFlexible pressure sensors are highly sought after for applications like rehabilitation healthcare, advanced wearable electronics, automotive safety, etc. This work demonstrates a scalable method for fabricating functional piezoresistive films using external magnetic fields to engineer the columnar microstructures. The continuous roll‐to‐roll processing involves directed self‐assembly of ferromagnetic Nickel particles within a flexible elastomer to achieve quasi 1–3 composites with low particle concentrations. In this process, polymer precursor‐microparticle system is subjected to a spatiotemporal evolution of temperature and magnetic fields simultaneously. Offline techniques like in‐plane alignment, through‐thickness light transmission, and viscosity evolution determine the influence of transient process conditions on final column morphology. Real‐time visualization in the X‐Y plane quantified by image analysis allowed tracking of morphological evolution where columns are formed separated by particle depletion zones leading to optical transparency. This characteristic is used to determine the kinetics of column formation via out‐of‐plane light transmission during Z‐alignment. Isothermal and non‐isothermal rheokinetics identified the gel point required to “freeze” the column structures. The scalability of the process is demonstrated by producing a 5.18 m long and 15.24 cm wide piezoresistive film with columns in thickness direction. The piezoresistive performance of produced anisotropic films is demonstrated under continuous uniaxial loading‐unloading cycles.

Multifunctional Strain/Pressure Sensor Based on Ag@Polydopamine Nanohybrid Methacrylamide Chitosan/Polyacrylamide Hydrogel for Healthcare Monitoring.

Hydrogels have emerged as promising candidates for flexible sensors due to their softness, biocompatibility, and tunable physicochemical properties. However, achieving synchronous satisfaction of conformality, conductivity, and diverse biological functions in hydrogel sensors remains a challenge. Here, we proposed a multifunctional hydrogel sensor by incorporating silver-loaded polydopamine nanoparticles (Ag@PDA) into a thermally cross-linked methacrylamide chitosan (CSMA) and acrylamide network, namely, Ag@PDA/(CSMA-PAM). The Ag@PDA/(CSMA-PAM) hydrogel showed the capability to respond effectively to both strain and pressure, enabling its independent application as either a strain sensor or a pressure sensor. The sensitivity of the hydrogel can reach 2.13 within the strain range of 65 to 150%, exhibiting a response and recovery time of 550 ms when utilized as a strain sensor. In contrast, its sensitivity was 0.07 kPa-1 during pressures ranging from 0 to 2.15 kPa, with a response and recovery time of 136 ms when employed as a pressure sensor. Additionally, the hydrogel sensor demonstrated high linearity (0.998 for strain and 0.98 for pressure), stable cycling ability (500 cycles), and low detection limit (0.5% for strain and 150 Pa for pressure). Moreover, the Ag@PDA/(CSMA-PAM) hydrogel exhibited good stability and reliability for a variety of practical applications, including the detection of subtle and large deformations, as well as real-time physiological activity monitoring. Further, owing to the bioactive components of chitosan and Ag@PDA present in the hydrogel, the Ag@PDA/(CSMA-PAM) sensor exhibited satisfactory biocompatibility along with excellent antioxidant and antibacterial activities, making it highly promising for applications as wearable sensors in personalized healthcare.

Machine Learning-Driven Soft Sensor Implementation for Real-Time Fault Detection in CDU of Oil Refinery

Soft sensors in oil refineries provide operators with important insights into the behavior and performance of processes using real-time and historical data to generate predictions. This data-driven strategy makes it easier to make wise decisions for detecting faults, thus improving process optimization and control. The Crude Distillation Unit (CDU) imposes very harsh working environments for measuring instruments, imposing both the use of a very robust sensory system and periodic maintenance procedures, which are time-consuming and costly. Notwithstanding such precautions, faults in those measuring devices, such as temperature and pressure sensors, still occur, and the presence of a sensor fault deteriorates the efficiency, productivity, and reliability of the refinery process. Recent works focused only on some fault types (e.g., bias and drift), ignoring others. This study presents the design of a soft sensor to detect all possible fault types in the real-time processing of an oil refinery. This method used actual data collected from the Salahuddin oil refinery in Iraq, several preprocessing methods, and a machine-learning approach. The proposed soft sensor was designed using several stages, including data collection, preprocessing, clustering, and classification. In the classification stage, an approach based on a Bagged Decision Tree (BDT) and Support Vector Machine (SVM) was implemented to classify the detected faults. The proposed soft sensor was trained and tested using actual data, achieving a high fault detection and classification result of 99.96%.

Mechanically Robust 3D Flexible Electrodes via Embedding Conductive Nanomaterials in the Surface of Polymer Networks.

3D flexible electrodes are essential to implement flexible pressure sensors in various flexible electronic applications. Conventional methods for fabricating these electrodes include electroless deposition, spray coating, and incorporating conductive nanomaterials into a polymer matrix. However, the electrodes fabricated using these methods are characterized by poor adhesion between the conductive layer and polymer surface and fail to maintain intrinsic mechanical properties of the polymer, such as elastic modulus and ductility. Herein, a transfer method in which conductive nanomaterials are embedded into the surface of polymer networks via optimal surface energy control is proposed, such as reducing adhesion between the mold and nanomaterials. This method induces mechanical interlocking between the surface of polymer networks and conductive nanomaterials, firmly anchoring them onto the polymer network surface. Moreover, the intrinsic mechanical properties of the fabricated 3D flexible electrodes remain unchanged. Flexible capacitive sensors prepared using the resulting electrodes exhibit a stable sensing performance (ΔC0,5000/C0 = 0.169%) even under repetitive pressure conditions (5000 cycles at 70kPa). The proposed robust 3D flexible electrode fabrication method presents a promising strategy for the future development of flexible pressure sensors.

Haptic Glove Guided Robotic Arm

The integration of haptic glove technology with robotic arms provides a leap in the field of teleoperation, offering users a precise interaction with robotic systems. This technology combines feedback mechanisms with a wearable glove interface, allowing users to manipulate objects in remote or hazardous environments through a robotic arm. The haptic glove is embedded with advanced sensors and actuators that capture finger movements, which are then processed in real-time to control the corresponding actions of the robotic arm. Simultaneously, haptic feedback provides a sense of pressure, enabling operators to perform tasks with increased control. The advancements in technology have expanded the applications of haptic glove-controlled robotic arms across various fields.

A novel skin temperature estimation system for predicting pressure injury occurrence based on continuous body sensor data: A pilot study.

Pressure injury prevention is important in older patients with immobility. This requires an accurate and efficient prediction of the development of pressure injuries. We aimed to develop a method for estimating skin temperature changes due to ischemia and inflammation using temperature sensors placed under bedsheets to provide an objective, non-invasive, and non-constrained risk assessment tool. This study consisted of a thermal skin simulation study and a descriptive correlation study in healthy participants. A thermal skin simulation study was conducted using a model reproducing the body surface (underwear, diaper, or wet diaper conditions) and bed environment. In a descriptive-correlational study, the participants lay supine on a mattress with a temperature sensor attached to their sacral skin. The thermal skin simulation study showed that temperature changes in the skin can be estimated under the sheets by inputting time-shifted temperature data into machine learning (R2=0.9967 for underwear, 0.9950 for diapers, and 0.9869 for wet diapers). It was also demonstrated that the absolute skin temperature of a healthy individual (N=17) could be estimated with the best accuracy by inputting time-shifted data into an extra-tree regressor (R2=0.8145). A combination of interface pressure and temperature sensors can be used to estimate skin temperature changes. These findings contribute to the development of a skin temperature measurement method that can capture temperature changes over time in clinical settings.

Stretchable snake electrodes and porous dielectric layers for advanced flexible pressure sensors

Stretchable snake electrodes and porous dielectric layers for advanced flexible pressure sensors

Mitigating public hygiene anxiety in waste material applications: Development of an antibacterial and high performance triboelectric nanogenerator from recycled PET

The development of triboelectric nanogenerators using waste materials (WM-TENGs) has gained significant attention for advancing a low-carbon economy and enhancing renewable energy utilization. However, consumer concerns about hygiene and safety hinder their acceptance in human-related applications, particularly due to fears of residual bacteria on reused materials from previous users and the potential for new bacterial growth from new users. To address these concerns, we developed an antibacterial and high-performance triboelectric nanogenerator from waste PET materials (AW-TENG). By incorporating small antibacterial polyhexamethylene guanidine hydrochloride (PHMG) molecules into the PET molecular chains, the resultant material, PET-PHMG, not only retains the excellent antibacterial properties of PHMG but also exhibits significantly enhanced triboelectric properties. The PET-PHMG nanofiber based AW-TENG achieved a maximum output voltage and current of 120.2V and 2.9 μA, while demonstrating effective antibacterial activity against S. aureus and E. coli. The corresponding charge density of 22.1nC/cm² stands out as one of the highest among WM-TENGs. Given these attractive characteristics, the AW-TENG is well-suited for applications such as self-powered pressure sensors and fire alarm systems. This study highlights the advanced utilization of discarded PET-derived antibacterial material in TENG technology, which can effectively foster public confidence in the use of wasted materials.

Constructing a high-power self-powered electrochemical pressure sensor for multimode pressure detections

Constructing a high-power self-powered electrochemical pressure sensor for multimode pressure detections

Modeling and Simulation of Microstructure Geometry and Material Type on Flexible Capacitive Pressure Sensors by Finite Element Analysis

Modeling and Simulation of Microstructure Geometry and Material Type on Flexible Capacitive Pressure Sensors by Finite Element Analysis

Simulation for regulatable assembly of large-scale photonic crystal: Application in flexible pressure sensor with visual sensing

Photonic crystal (PC) sensors have attracted increasing attention for their visual signal output. However, their applications are hindered by the small-scale, defective arrays, and the time-consuming self-assembly. Herein, an efficient self-skinning method for regulating assembly of polystyrene (PS) microspheres and a large-scale PC sensor have been developed. The flowability and rapid reconfigurability of pre-crystallized PS dispersions were investigated through numerical simulation, allowing for control of the assembly through the colloid skin. The simulation and SEM characterization verify that the regulation of colloid skin suppresses the uneven evaporation flux and thus the large-scale and crack-free PC arrays (100 × 400 mm2) are fabricated within 150 s. The PC sensor shows reversible visual signal output with mechanochromic sensitivity of 4.95–12.1 nm∙kPa−1. The scalable PC sensor achieves sensitive-monitoring of human plantar pressure through visual signal output, suggesting a promising potential in gait recognition, health analysis, and so on.

Ultrathin flexible pressure sensors using microbead embedded nanofibrous membrane for wearable applications

Ultrathin flexible pressure sensors using microbead embedded nanofibrous membrane for wearable applications

Highly accurate deformation adaptive pressure sensor integrated with programmatically fabricated strain localizing yarn

Highly accurate deformation adaptive pressure sensor integrated with programmatically fabricated strain localizing yarn

Flexible capacitive pressure sensor with high sensitivity and wide detection range applying solvent-exchanged porous lignin-cellulose hydrogel as the dielectric layer

Flexible capacitive pressure sensor with high sensitivity and wide detection range applying solvent-exchanged porous lignin-cellulose hydrogel as the dielectric layer