Smart Sensors Research Articles

A multi-stimulus responsive nanogel actuator with the π-π stacking interaction self-assembly and application for bioinspired smart skin with light, heat and humidity responses and superb UV resistance

The fabrication and development of assemblies of bionic smart skin with multi-stimulus response and multi-color tunable properties is a major challenge in the field of advanced materials and smart wearable devices. In this work, an amphiphilic functional nanogel switch connected by long polyether chain segments containing bis-spiropyran with the π-π stacking interactions was designed. A multi-stimulus responsive color-changing nanogel actuator (SP-PEG-NA) was constructed using ionic liquid-assisted self-assembly. A composite bionic smart skin (WPU/SP-PEG@CF) was prepared by compositing SP-PEG-NA, waterborne polyurethane (WPU) and flexible cotton fabric based on physical cross-linking. The smart skin possesses multiple color-changing properties in response to light, heat and humidity. Even under ultra-low UV intensity (0.025 mW/cm2), WPU/SP-PEG@CF also exhibits significant photochromic properties. Surprising, the smart composite constructed with the nanogels possessed extremely high UV shielding performance (UPF 628.68) and photocontrolled hydrophobic-hydrophilic transition properties. The self-assembled nanogel actuators greatly improved the stimulus–response sensitivity. The multiple supramolecular noncovalent interactions of the nanogel actuator such as hydrophilic long polyether structures hydrogen-bonding and π-π stacking interactions endowed nanogel highly self-assembly properties. They showed excellent multi-stimulus response in color-changing, versatility and reversibility. The multi-stimulus responsive and multifunctional smart composites prepared using self-assembled nanogel has potential applications in smart wearable materials, sensors and information display.

Integration of Smart Sensor Technology in Wearable Masks for Real-Time Monitoring of Respiratory Health and Infectious Diseases

Integration of Smart Sensor Technology in Wearable Masks for Real-Time Monitoring of Respiratory Health and Infectious Diseases

Functionalized Face Masks as Smart Wearable Sensors for Multiple Sensing.

Wearable sensors provide continuous physiological information and measure deviations from healthy baselines, resulting in the potential to personalize health management and diagnosis of diseases. With the emergence of the COVID-19 pandemic, functionalized face masks as smart wearable sensors for multimodal and/or multiplexed measurement of physical parameters and biochemical markers have become the general population for physiological health management and environmental pollution monitoring. This Review examines recent advances in applications of smart face masks based on implantation of digital technologies and electronics and focuses on respiratory monitoring applications with the advantages of autonomous flow driving, enrichment enhancement, real-time monitoring, diversified sensing, and easily accessible. In particular, the detailed introduction of diverse respiratory signals including physical, inhalational, and exhalant signals and corresponding associations of health management and environmental pollution is presented. In the end, we also provide a personal perspective on future research directions and the remaining challenges in the commercialization of smart functionalized face masks for multiple sensing.

Heterogating Gel Iontronics: A Revolution in Biointerfaces and Ion Signal Transmission.

Currently, existing iontronic systems are limited and struggle to process electronic-to-multi-ionic transport, resulting in interchange inefficiencies and incompatibilities between artificial ion devices and biological tissue interfaces. The development of heterogating gel iontronics offers a significant advancement in bridging this gap, drawing inspiration from the complex ionic transmission mechanisms found in biological synapses within neural networks. These heterogating gels utilize a biphasic architecture, where the heterointerface effect constructs ionic transfer energy barriers, enabling distinct signal transmission among different ions. In systems with multiple ion species, heterogating gel iontronics allow for precise control of ion transmission, realizing hierarchical and selective cross-stage signal transmission as a neuromorphic function. This perspective highlights the vast potential of heterogating iontronics in applications such as biosensing, neuroprosthetics, and ion separation technologies. Meanwhile, it also addresses the current challenges, including scaling production, ensuring biocompatibility, and integrating with existing technologies, which are crucial for future development. The advancement of heterogating gels is expected to promote the integration between abiotic and biotic systems, with broad implications for smart sensors, bioneural devices, and beyond.

Dual parameter smart sensor for nitrogen and temperature sensing based on defect-engineered 1T-MoS2

In general, defects are crucial in designing the different properties of two-dimensional materials. Therefore large variations in the electric and optical characteristics of two-dimensional layered molybdenum disulphide might be attributed to defects. This study presents the design of a temperature and nitrogen sensor based on few-layer molybdenum disulfide sheets (FLMS), which was developed from bulk MoS2 (BMS) through an exfoliation approach. The produced sulfur defect, molybdenum defect, line defect, and plane defect were characterized by scanning transmission electron microscopy (STEM), which substantially impacts the sensing characteristics of the resulting FLMS. Our theoretical analysis validates that the sulfur vacancies of the MoS2 lattice improve sensing performance by promoting effective charge transfer and surface interactions with target analytes. The FLMS-based sensor showed a high sensitivity for detecting nitrogen gas with a detection limit (LOD) of ~ 0.18 ppm. Additionally, temperature-detecting capabilities were assessed over various temperatures, showing outstanding stability and repeatability. To the best of our knowledge, this material is the first of its kind, demonstrating visible N2 gas sensing with chromic behaviour.

Liquid Metal Aerogel With Janus Architecture for Selective Direction Recognition and High‐Efficiency Moisture Energy Harvesting

AbstractLiquid metal (LM) micro‐nano droplets show the superiority in reducing the high surface tension applicable in conductive inks for flexible electronics. However, the dynamic surface makes LM droplets susceptible to be oxidized, facing challenges in storage and applications. Herein, a bifunctional groups cross‐linking strategy is adopted to encapsulate LM droplets into a robust shell. During sonicating LM in carboxymethyl chitosan (CMCS) solution with bifunctional groups (i.e., carboxyl and amino groups), a robust interface shell is constructed by anchoring effect, thereby providing high chemical stability (>7d). When directionally freezing‐drying LM dispersions, aerogel with Janus architecture is produced driven by the gravity of LM droplets. Due to the unique heterogeneous structure, the resultant aerogel exhibits a selective multifunctional response toward directional bending. In addition, owing to the gradient distribution of CMCS within aerogel, moisture electricity generator can be built with a high output voltage of 460 mV. Thus, this bifunctional groups cross‐linking strategy not only improves the chemical stability of LM micro‐nano droplets, but also renders a new method for producing multifunctional aerogel with energy harvesting and selective directional recognition, and applicable in smart sensors and power supplying devices.

Nanostructured NiS2-based flexible smart sensors for human respiration monitoring.

The growing demand for wearable healthcare devices has led to an urgent need for cost-effective, wireless and portable breath monitoring systems. However, it is essential to explore novel nanomaterials that combine state-of-the-art flexible sensors with high performance and sensing capabilities along with scalability and industrially acceptable processing. In this study, we demonstrate a highly efficient NiS2-based flexible capacitive sensor fabricated via a solution-processible route using a novel single-source precursor [Ni{S2P(OPr)2}2]. The developed sensor could precisely detect the human respiration rate and exhibit rapid responsiveness, exceptional sensitivity and selectivity at ambient temperatures, with an ultra-fast response and recovery. The device effectively differentiates the exhaled breath patterns including slow, fast, oral and nasal breath, as well as post-exercise breath rates. Moreover, the sensor shows outstanding bending stability, repeatability, reliable and robust sensing performance and is capable of contactless sensing. The sensor was further employed with a user-friendly wireless interface to facilitate smartphone-enabled real-time breath monitoring systems. This work opens up numerous avenues for cost-effective, sustainable and versatile sensors with potential applications for Internet of Things-based flexible and wearable electronics.This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

Optical and electrical analysis of Rhodamine 110 chloride/PVA-PEG for smart and flexible optical sensor technology

Polyvinyl alcohol (PVA)/Polyethylene glycol (PEG) (90:10 wt%) polymeric nanocomposite films were prepared via a simple casting technique with Rhodamine 110 chloride (Rh-110) dye for optoelectronics with various weight ratio percentages (0, 0.06, 0.1,0.2, 0.4, 0.6 1.25, 2 and 4.0 wt%) of Rh-110 doped PVA/PEG polymeric blend composite films. The produced films have been studied with instruments like (XRD and FT-IR) spectroscopic methods, UV-visible-NIR spectrophotometer, and dielectric spectroscopy. The absorbance (Abs) and transmittance T(λ) were measured and analyzed. Absorption spectra in multiple bands are generated between 215 and 620 nm when Rh-110 is added to the PVA-PEG matrix, according to the examination of transmittance curves of Rh-110 /PVA-PEG. Rh-110/PVA-PEG has a transmittance CUT-OFF in the 200–550 nm wavelength range, making it ideal for He–Ne lasers with wavelengths of 532.8 nm. The optical properties of the Rh-110/PVA-PEG polymeric composite films were computed. Moss, Reddy, Anani, and Kumar-Singh relationships have been employed to determine the refractive index values of the materials. Analyses were performed on various electrical properties, including AC conductivity, dielectric constant, and dielectric loss. As a result, the synthesized Rh-110/PVA-PEG polymeric films may be used in various promising and practical optoelectronic applications, including lasers, optical filters, optical communication, light-emitting diodes, and optical switching.

Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

Energy Analysis & Optimization of A Residential Structure

This project delves into the critical realm of energy analysis and optimization for residential buildings. With global energy demand surging and environmental concerns mounting, the need for efficient energy consumption is paramount. The study employs a multidisciplinary approach, combining architectural design, data-driven insights, and advanced technologies. Real-world data collection, including smart meter readings and sensors, forms the foundation for evaluating energy usage trends, peak demands, and seasonal fluctuations. Building Energy Modeling (BEM) software aids in simulating diverse scenarios, enabling accurate predictions of energy performance. Key Words: Autodesk Revit, Autodesk Insight, (EUI) Energy Use Intensity. BIM (Building Information Modelling).

Minimizing THD Using Integrated Multilevel Maximum Power Tracking Inverters

This work proposes a new Modified Multilevel Inverter (MMLI) and provides a comprehensive comparison with Conventional Cascaded H-bridge Inverters. The MMLI features fewer switching devices compared to the conventional H-Bridge Inverter for 9-level voltages and higher. Maximum Power Point Tracking (MPPT) incorporated with a Boost converter ensures a constant output from photovoltaic (PV) arrays, which is then fed to the inverter to achieve the desired number of voltage levels. To enhance the performance and efficiency of the system, IoT technologies were integrated for real-time monitoring and control. Smart sensors and cloud-based platforms were utilized for data collection and analysis, enabling precise control of the MPPT and inverter systems. The integration of IoT resulted in significant improvements in the system's dynamic response, energy conversion efficiency, and overall reliability. The results were validated through simulations in Simulink, with outcomes presented and compared for voltage waveform and harmonic spectrum. The integration of IoT technologies provided substantial benefits, showcasing the interdisciplinary approach of this research in reducing Total Harmonic Distortion (THD) while optimizing inverter operations

Highly sensitive colorimetric detection of ascorbic acid using molecularly imprinted photonic crystal hydrogel sensor

Molecular imprinted photonic crystal hydrogel (MICPH) sensors have gained recognition as an efficient platform for selectively detecting analyte molecules. In the present work, we report the development and implementation of a colorimetric sensor based on MIPCH for the selective detection of Ascorbic acid (AA). The fabrication process of the MIPCH sensor involves the photo-polymerization of a hydrogel precursor solution containing AA molecules, which is encapsulated in the interstitial spaces of photonic crystal (PC) opal film. Following the encapsulation, these molecules are selectively removed from the hydrogel network, forming an AA-MIPCH sensor. The sensor exhibits a vivid structural color that redshifts upon rebinding with different concentrations of AA molecules. This alteration in structural coloration serves as a straightforward and visually discernible indicator of the sensor response and enables the quantitative measurement of the target molecule. The sensor exhibits remarkable sensitivity, featuring a low limit of detection (LoD) of 0.011 µM with high selectivity, minimal response time, and promising long-term stability. In addition, the sensor demonstrates its capability to detect the AA even from the complex matrix. A CNN-based deep learning algorithm in MATLAB® was employed to quantitatively predict the concentration of the target molecule. The integration with the machine learning algorithm yields a highly efficient and accurate smart sensor system that is well-suited for real-time sensing of ascorbic acid.

Highly Sensitive, Degradable, and Rapid Self-Healing Hydrogel Sensor with Semi-Interpenetrating Network for Recognition of Micro-Expressions.

Flexible conductive hydrogels have revolutionized the lives and are widely applied in health monitoring and wearable electronics as a new generation of sensing materials. However, the inherent low mechanical strength, sensitivity, and lack of rapid self-healing capacity results in their short life, poor detection accuracy, and environmental pollution. Inspired by the molecular structure of bone and its chemical characteristics, a novel fully physically cross-linked conductive hydrogel is fabricated by the introduction of nanohydroxyapatite (HAp) as the dynamic junction points. In detail, the dynamically cross-linked network, including multiple physical interactions, provides it with rapid self-healing ability and excellent mechanical properties (elongation at break (>1200%), tensile strength (174kPa), and resilience (92.61%)). Besides, the ions (Cl-, Li+, Ca2+) that move freely within the system impart outstanding electrical conductivity (2.46±0.15 S m-1), high sensitivity (gauge factor, GF>8), good antifreeze (-40.2°C), and humidity properties. The assembled sensor can be employed to sensitively detect various large human motions and subtle changes in behavior (facial expressions, speech recognition). Meanwhile, the hydrogel sensor can also degrade in phosphate-buffered saline solution without causing any environmental pollution. Therefore, the designed hydrogels may become a promising candidate material in the future potential applications for smart wearable sensors and electronic skin.

Malaysian Rainwater Harvesting System for In-House Power Generation

Energy harvesting monitoring systems have become more important as the Internet of Things (IoT) have grown. An intelligent system to monitor rainwater harvesting at UNITEN COE BN is being designed and developed in this study. Rainwater harvesting operations will be improved by developing an intelligent system. Monitoring techniques are studied, and sensors are designed for simulation. Smart rainwater harvesting systems are designed and implemented in this study, contributing to the field of smart monitoring systems. Rainwater collection, storage and usage are monitored and analyzed with smart sensors and data acquisition systems. Water turbine speed, voltage, and rainfall intensity are monitored by sensors in the developed system. Data from sensors is processed in Python GUIs. Visual displays allow users to monitor the rainwater harvesting system remotely. Durability and infrastructure compatibility are considered when selecting materials. It is found that smart rainwater harvesting system performance and reliability can be improved through simulation testing and validation. The study concluded that, storm water resources can be optimized by accessing real-time information.

Enhancing Tennis Practice: Sensor Fusion and Pose Estimation with a Smart Tennis Ball.

This article demonstrates the integration of sensor fusion for pose estimation and data collection in tennis balls, aiming to create a smaller, less intrusive form factor for use in progressive learning during tennis practice. The study outlines the design and implementation of the Bosch BNO055 smart sensor, which features built-in managed sensor fusion capabilities. The article also discusses deriving additional data using various mathematical and simulation methods to present relevant orientation information from the sensor in Unity. Embedded within a Vermont practice foam tennis ball, the final prototype product communicates with Unity on a laptop via Bluetooth. The Unity interface effectively visualizes the ball's rotation, the resultant acceleration direction, rotations per minute (RPM), and the orientation relative to gravity. The system successfully demonstrates accurate RPM measurement, provides real-time visualization of ball spin and offers a pathway for innovative applications in tennis training technology.

Sensor Systems for Measuring Force and Temperature with Fiber-Optic Bragg Gratings Embedded in Composite Materials

Currently, there is a lot of interest in smart sensors and integrated composite materials in various industries such as construction, aviation, automobile, medical, information technology, communication, and manufacturing. Here, a new conceptual design for a force and temperature sensor system is developed using fiber-optic Bragg grating sensors embedded within composite materials, and a mathematical model is proposed that allows one to estimate strain and temperature based on signals obtained from the optical Bragg gratings. This is important for understanding the behaviors of sensors under different conditions and for creating effective monitoring systems. Describing the strain gradient distribution, especially considering different materials with different Young’s modulus values, provides insight into how different materials respond to applied forces and temperature changes. The shape of the strain gradient distribution was obtained, which is a quadratic function with a maximum value of 1500 µ, with a maximum value at the center of the lattice and a symmetrically decreasing strain value with distance from the central part of the fiber Bragg grating. With the axial strain at the installation site of the Bragg grating sensor under applied force values ranging from 10 to 11 N, the change in strain was linear. As a result of theoretical research, it was found that the developed system with fiber-optic sensors based on Bragg gratings embedded in composite materials is resistant to external influences and temperature changes.

Artificial intelligence enabled biodegradable all-textile sensor for smart monitoring and recognition

Artificial intelligence (AI) enabled electronic textiles inherit the advantages of traditional textiles, such as softness, flexibility, and wearable convenience, and demonstrate significant potential for wearable applications. However, the existence of metallic electrodes and polymer thin films sensitive/encapsulating layers in current textile- or fiber-based pressure sensors significantly reduces the unique advantages of textiles, particularly in terms of renewability and biodegradability. Here, an all-textile biodegradable AI enabled pressure sensor is demonstrated using tunable conductivity cotton as the electrode/sensitive layer, incorporating real-time deep learning-based data analysis. The metal electrode and polymer thin film widely adopted in smart textile-based pressure sensors are avoided, and the all-textile components allow the sensors to be freely cut and reassembled like Lego. Based on these Lego-like smart textiles, intelligence applications such as real-time health monitoring, game control, gait analysis, and authentication systems are demonstrated. After completing the functions, the whole device can be rapidly degraded in the cellulase solution and broken down into reducing sugars, thus effectively reducing the environmental pollution. This work provides a new strategy for creating renewable and sustainable textile pressure sensors with significant application potential in next-generation smart green electronics.

A polyester- stainless steel based smart wristband sensor for skin temperature monitoring

This study presents the development and characterization of a novel textile-based wristband sensor for continuous temperature monitoring. The sensor, woven with a blend of polyester-stainless steel and polyester-cotton yarns, exhibited high sensitivity (0.0315/°C) and a rapid response time (∼35 sec). Stainless steel was selected for its excellent electrical conductivity and biocompatibility, crucial for accurate and safe skin contact applications, while polyester and cotton contribute its lightweight, breathable, and durable properties, ensuring prolonged comfort and wearability. Through systematic testing and optimization, the sensor’s sensitivity was fine-tuned by adjusting the number and length of conductive yarns. The study also introduced a theoretical resistance equivalent model, comparing its theoretical sensitivity with experimental findings. The sensor, lightweight and cost-effective, proved comfortable during 8–10 h of continuous wear. This study addresses challenges faced by existing textile temperature sensors and offers a reliable alternative with high linearity, repeatability, and suitability for individuals with sensitive skin.

Temperature‐Responsive Anisotropic Bilayer Hydrogel Actuators with Adaptive Shape Transformation for Enhanced Actuation and Smart Sensor Applications

Temperature‐Responsive Anisotropic Bilayer Hydrogel Actuators with Adaptive Shape Transformation for Enhanced Actuation and Smart Sensor Applications

Sea Horse Optimization-Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition.

Medication adherence is an essential aspect of healthcare for patients and is important for achieving medical objectives. However, the lack of standard techniques for measuring adherence is a global concern, making it challenging to accurately monitor and measure patient medication regimens. The use of sensor technology for medication adherence monitoring has received much attention lately since it makes it possible to continuously observe patients' medication adherence behavior. Sensor devices or smart wearables utilize state-of-the-art machine learning (ML) methods to analyze intricate data patterns and provide predictions accurately. The key aim of this work is to develop a sensor-based hand gesture recognition model to predict medication activities. In this research, a smart sensor device-based hand gesture prediction model is developed to recognize medication intake activities. The device includes a tri-axial gyroscope, geometric, and accelerometer sensors to sense and gather data from hand gestures. A smartphone application gathers hand gesture data from the sensor device, which is then stored in the cloud database in a .csv format. These data are collected, processed, and classified to recognize the medication intake activity using the proposed novel neural network model called Sea Horse Optimization-Deep Neural Network (SHO-DNN). The SHO technique is implemented to update the biases and weights and the number of hidden layers in the DNN model. By updating these parameters, the DNN model is improved in classifying the samples of hand gestures to identify the medication activities. The research model demonstrates impressive performance, with an accuracy of 98.59%, sensitivity of 97.82%, precision of 98.69%, and an F1 score of 98.48%. Hence, the proposed model outperformed the most available models in all the aforementioned aspects. The results indicate that this model is a promising approach for medication adherence monitoring in healthcare applications, instilling confidence in its effectiveness.