Sensors For Structural Health Monitoring Research Articles

On-demand fabrication of piezoelectric sensors for in-space structural health monitoring

Inflatable structures, promising for future deep space exploration missions, are vulnerable to damage from micrometeoroid and orbital debris impacts. Polyvinylidene fluoride-trifluoroethylene (PVDF-trFE) is a flexible, biocompatible, and chemical-resistant material capable of detecting impact forces due to its piezoelectric properties. This study used a state-of-the-art material extrusion system that has been validated for in-space manufacturing, to facilitate fast-prototyping of consistent and uniform PVDF-trFE films. By systematically investigating ink synthesis, printer settings, and post-processing conditions, this research established a comprehensive understanding of the process-structure-property relationship of printed PVDF-trFE. Consequently, this study consistently achieved the printing of PVDF-trFE films with a thickness of around 40 µm, accompanied by an impressive piezoelectric coefficient of up to 25 pC N−1. Additionally, an all-printed dynamic force sensor, featuring a sensitivity of 1.18 V N−1, was produced by mix printing commercial electrically-conductive silver inks with the customized PVDF-trFE inks. This pioneering on-demand fabrication technique for PVDF-trFE films empowers future astronauts to design and manufacture piezoelectric sensors while in space, thereby significantly enhancing the affordability and sustainability of deep space exploration missions.

Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon.

This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model's accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model's predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Crack Detection of Reinforced Concrete Structure Using Smart Skin.

The availability of carbon nanotube (CNT)-based polymer composites allows the development of surface-attached self-sensing crack sensors for the structural health monitoring of reinforced concrete (RC) structures. These sensors are fabricated by integrating CNTs as conductive fillers into polymer matrices such as polyurethane (PU) and can be applied by coating on RC structures before the composite hardens. The principle of crack detection is based on the electrical change characteristics of the CNT-based polymer composites when subjected to a tensile load. In this study, the electrical conductivity and electro-mechanical/environmental characterization of smart skin fabricated with various CNT concentrations were investigated. This was performed to derive the tensile strain sensitivity of the smart skin according to different CNT contents and to verify their environmental impact. The optimal CNT concentration for the crack detection sensor was determined to be 5 wt% CNT. The smart skin was applied to an RC structure to validate its effectiveness as a crack detection sensor. It successfully detected and monitored crack formation and growth in the structure. During repeated cycles of crack width variations, the smart skin also demonstrated excellent reproducibility and electrical stability in response to the progressive occurrence of cracks, thereby reinforcing the reliability of the crack detection sensor. Overall, the presented results describe the crack detection characteristics of smart skin and demonstrate its potential as a structural health monitoring (SHM) sensor.

Stiffness identification of reinforced concrete beams using rotation rate sensors

Monitoring flexural stiffness variations of cracked reinforced concrete (r/c) structures is one of the most challenging problems of the dynamics of these structures. In this article, the application of rotation rate sensors for structural health monitoring of reinforced concrete beams is proposed as an alternative SHM method to, e.g. those utilising more expensive and laborious fiber optics. This subject was investigated with progressively damaged 6 m long reinforced concrete beams. During each damage state, the cracks were located and described in detail. Experimental modal analyses were carried out. Translational and rotational vibration modes were acquired using translational accelerometers and rotation rate sensors. The stiffness distributions of the damaged reinforced concrete beams were identified from the modal properties using model-updating techniques. It is demonstrated that stiffness distributions calculated using rotational modes are more accurate than those obtained from the familiar translational natural modes of vibrations. Further improvement of stiffness identification of r/c beams from their vibrations was obtained by combining the directly measured rotational modes and their approximations from translational modes by the central difference method.

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda.

The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels

This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode conversion due to the presence of features, such as stiffeners, and hence impair the detection accuracy of the overall system. Many paths will also contribute little to the overall coverage level or damage detection accuracy of the network and can be ignored, reducing complexity. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available. Furthermore, even when expert knowledge is considered, the optimum path selection might not be achieved. Therefore, this work proposes a novel automatic procedure for optimising the sensor paths of an SHM sensor network to maximise coverage level, maximise damage detection accuracy and minimise the overall signal noise in the network due to geometric features. This procedure was tested on a real-world large composite stiffened panel with many geometric features in the form of frames and stiffeners. Compared to using all of the available sensor pairs, the optimised network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide very similar performance, in terms of coverage level and overall signal noise, to a sensor path network designed based on prior expert knowledge but provided up to 35% higher damage detection accuracy. As a result, the novel procedure proposed in this work has the capability to design high-performing SHM sensor path networks for structures with complex geometries but without the need for prior expert knowledge, making SHM more accessible to the engineering community.

Distributed fiber optic sensors for structural health monitoring of composite pressure vessels

Abstract In this paper, we present a comprehensive overview of our research in the field of distributed fiber optic sensors for structural health monitoring of hydrogen composite pressure vessels. Specifically, we demonstrate how the integration of fiber optic sensors into composite pressure vessels enhances safety while simultaneously reducing maintenance costs. The small size of optical fibers enables their integration into composite structures during the manufacturing process, allowing continuous monitoring and precise detection and localization of structural damages during service life. We also discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. Our applications of fiber optic sensors demonstrate their potential to contribute significantly to the energy transition towards renewable sources.

Displacement Estimation via 3D-Printed RFID Sensors for Structural Health Monitoring: Leveraging Machine Learning and Photoluminescence to Overcome Data Gaps.

Monitoring object displacement is critical for structural health monitoring (SHM). Radio frequency identification (RFID) sensors can be used for this purpose. Using more sensors enhances displacement estimation accuracy, especially when it is realized through the use of machine learning (ML) algorithms for predicting the direction of arrival of the associated signals. Our research shows that ML algorithms, in conjunction with adequate RFID passive sensor data, can precisely evaluate azimuth angles. However, increasing the number of sensors can lead to gaps in the data, which typical numerical methods such as interpolation and imputation may not fully resolve. To overcome this challenge, we propose enhancing the sensitivity of 3D-printed passive RFID sensor arrays using a novel photoluminescence-based RF signal enhancement technique. This can boost received RF signal levels by 2 dB to 8 dB, depending on the propagation mode (near-field or far-field). Hence, it effectively mitigates the issue of missing data without necessitating changes in transmit power levels or the number of sensors. This approach, which enables remote shaping of radiation patterns via light, can herald new prospects in the development of smart antennas for various applications apart from SHM, such as biomedicine and aerospace.

Influence of temperature and preload force on capacitance and electromechanical impedance of lead zirconate titanate piezoelectric wafer active sensors for structural health monitoring of bolts

This study focuses on the effect of temperature and preload force on capacitance and electromechanical impedance of lead zirconate titanate piezoelectric wafer active sensors for structural health monitoring of bolts. We explain the influence of temperature on the basis of the phenomenological thermodynamic theory of ferroelectricity by Landau, Ginsburg and Devonshire. The article illustrates the effect of damping the radial deformation of piezoelectric sensors on the capacitance and electromechanical impedance spectra in structural health monitoring of bolts. We also explains the similarities between the effects of temperature and preload force on the electromechanical impedance spectra. We establish a clear correlation between the mechanical strain in the region of the sensor (here due to a preload force), the capacitance and the electromechanical impedance spectra and thus show that piezoelectric sensors made of lead–zirconate–titanate can be used excellently in areas of variable mechanical strain. The article enhances the understanding of the measurement method and facilitates the transfer of the measurement method to other problems in structural health monitoring. Furthermore, the acquired knowledge serves as a solid basis for verifying the plausibility of data sets containing electromechanical impedance spectra.

Improvement of the Piezoresistive Behavior of Poly (vinylidene fluoride)/Carbon Nanotube Composites by the Addition of Inorganic Semiconductor Nanoparticles.

Conductive polymer composites (CPCs), obtained by incorporating conductive fillers into a polymer matrix, are suitable for producing strain sensors for structural health monitoring (SHM) in infrastructure. Here, the effect of the addition of inorganic semiconductor nanoparticles (INPs) to a poly (vinylidene fluoride) (PVDF) composite filled with multi-walled carbon nanotubes (MWCNTs) on the piezoresistive behavior is investigated. INPs with different morphologies and sizes are synthesized by a hydrothermal method. The added inorganic oxide semiconductors showed two distinct morphologies, including different phases. While particles with flower-like plate morphology contain phases of orth-ZnSnO3 and SnO, the cauliflower-like nanoparticles contain these metal oxides and ZnO. The nanoparticles are characterized by field-emission scanning electron microscopy (FE-SEM) and X-ray diffraction (XRD), and the nanocomposites by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). Cyclic tensile testing is applied to determine the strain-sensing behavior of PVDF/1 wt% MWCNT nanocomposites with 0-10 wt% inorganic nanoparticles. Compared to the PVDF/1 wt% MWCNT nanocomposite, the piezoresistive sensitivity is higher after the addition of both types of nanoparticles and increases with their amount. Thereby, nanoparticles with flower-like plate structures improve strain sensing behavior slightly more than nanoparticles with cauliflower-like structures. The thermogravimetric analysis results showed that the morphology of the semiconductor nanoparticles added to the PVDF/MWCNT matrix influences the changes in thermal properties.

Directionally sensitive cement-based sensor using carbon nanotube and carbonyl iron powder (CNT@CIP)-based nanohybrid clusters

Cement-based sensors have been highlighted for using as structural health monitoring sensors; however, the conventional cement-based sensors can only detect the levels of applied loading not the direction of the loading. Therefore, this study proposes a new method for developing cement-based sensors which can detect the levels of applied loadings with their direction. The proposed method involves using carbon nanotube and carbonyl iron powder (CNT@CIP)-based nanohybrid clusters, which are added to the cement-based sensors during fabrication, and controlling their conductive networks through magnetization curing. The fabricated cement-based sensors are then tested for piezoresistive sensing. The experimental outcomes indicated directional sensitivity values of 3.12%, 2.47%, and 0.98%/MPa stress sensitivity in horizontal, random, and vertical sensors. In addition, their long-term sensing capabilities are predicted using a long short-term memory (LSTM) model. The findings of this study could be useful in developing multi-directional cement-basd sensors and predicting their long-term sensing capabilities.

Experimental study on strain of SFRC tunnel lining segments using a comprehensive embedded optical fiber sensing system

In comparison to current techniques, optical fiber sensors (OFS) for structural health monitoring (SHM) of steel fibre reinforced concrete (SFRC) tunnel lining segments is an emerging technology that could enhance tunnel safety and reliability. However, the utilizing many measurement techniques in a single OFS technology has pros and cons, making it difficult to meet the demands for a comprehensive and reliable assessment for long-term monitoring inside tunnels. We present comprehensive OFS system for strain monitoring inside concrete tunnel lining segments. The system involves distributed optical fiber sensing (DOFS) cable, fiber Bragg grating (FBG) sensors, and chirped fiber Bragg grating (CFBG) sensors. The proposed system is particularly useful for high-risk or difficult-to-access locations, such as inside tunnel structures. We provide the deployment design after introducing the sensing principle of the system. To validate its effectiveness and reliability, we conduct a scaled-down laboratory experiment to evaluate the strain behavior. The proposed comprehensive OFS system accurately captures the strain evolution over time in various stages of tunnel lining segments, including casting, curing, loading, and even after failure. This system demonstrates high-resolution, and reliable monitoring of internal strain in tunnel lining segments, making it suitable for practical application in full-scale and long-term SHM during the construction and operation stages of shield tunnels with connection expansion for segments.

3D Printed and Embedded Strain Sensors in Structural Composites for Loading Monitoring and Damage Diagnostics

The development of novel embedded sensors for structural health monitoring (SHM) is crucial to provide real-time assessments of composite structures, ensuring safety, and prolonging their service life. Early damage diagnostics through advanced sensors can lead to timely maintenance, reducing costs and preventing potential catastrophic failures. This paper presents the synthesis, 3D printing, and characterization of novel embedded strain sensors using multi-walled carbon nanotube (MWCNT) -enhanced nanocomposites in fiberglass reinforced composites for potential damage diagnostics and SHM applications. MWCNTs are dispersed within structural epoxy for the additive manufacturing of nanocomposites with piezoresistive sensing capability. The 3D-printed nanocomposite sensors are embedded in fiberglass-reinforced composite laminates. The piezoresistive sensing capabilities of the 3D-printed sensors within composites are characterized by applying different levels of maximum loads and load rates under three-point bending loads. Additionally, the long-term reliability of the developed strain sensors is evaluated up to 1000 cycles. The recorded piezoresistive sensing signals show high sensitivity for the externally applied bending loads with advanced gauge factor up to 100, resulting in potential load sensing capability for in-situ damage diagnostics and real-time SHM for structural composites.

Radiation-Hardened and Flexible Pb(Zr0.53Ti0.47)O3 Piezoelectric Sensor for Structural Health Monitoring.

Piezoelectric sensors are excellent damage detectors that can be applied to structural health monitoring (SHM). SHM for complex structures of aerospace vehicles working in harsh conditions is frequently required, posing challenging requirements for a sensor's flexibility, radiation hardness, and high-temperature tolerance. Here, we fabricate a flexible and lightweight Pb(Zr0.53Ti0.47)O3 piezoelectric film on flexible KMg3(AlSi3O10)F2 substrate via van der Waals (vdW) heteroepitaxy, endowing it with robust ferroelectric and piezoelectric properties under low energy-high flux protons (LE-HFPs) radiation (1015 p/cm2). More importantly, the Pb(Zr0.53Ti0.47)O3 film sensor maintains highly stable damage monitoring sensitivity on an aluminum plate under harsh conditions of LE-HFPs radiation (1015 p/cm2, flat structure), high temperature (175 °C, flat structure), and mechanical fatigue (bending 105 cycles under a radius of 5 mm, curved structure). All these superior qualities are suggested to result from the outstanding film crystal quality due to vdW epitaxy. The flexible and lightweight Pb(Zr0.53Ti0.47)O3 film sensor demonstrated in this work provides an ideal candidate for real-time SHM of aerospace vehicles with flat and complex curve-like structures working in harsh aerospace environments.

3D printed vorticella-kirigami inspired sensors for structural health monitoring in Internet-of-Things

Kirigami is an ancient paper art that involves cutting slit strategies, which could provide exotic and controllable engineering properties. Especially, kirigami can be combined with bio-inspired designs to improve its mechanical behaviors. A highly sensitive 3D vibration sensor has been developed using a 3D-printed structure inspired by vorticella-kirigami (VK). VK-inspired kirigami exhibits 2D-to-3D morphing and shape-programmable properties. Specifically, the VK-inspired vibrational structure has been optimized for a frequency of 2 Hz, with high sensitivity of 0.80 pF/ms−2. Structural health monitoring (SHM) is an essential application for ensuring the safety and reliability of civil infrastructure. Therefore, the tools for widely and easily accessible monitoring systems are highly demanded, considering a monitoring system with Internet-of-Things (IoT) as a promising application in this paper. The VK-inspired vibrational sensors can detect the impact delivering from the epicenter on fragile components of the structural structures through the message queuing telemetry transport (MQTT) protocol. Moreover, it allows seamless access to real-time or saved vibration data via multiple smartphones and personal computers. These SHM technologies hold the potential to significantly enhance their adoption in safety–critical domains.

Preliminary Multiphysics Modeling of Electric High-Voltage Cable of Offshore Wind-Farms

During manufacture, handling, transportation, installation and operation, mechanical overstress can affect the electrical and thermal properties of the conductor. As the wires in general are made of copper, which is a very plastically deforming material, these stresses will gradually generate plastic deformations of the copper until the wires start to fail. The objective of this article is to study, by numerical modeling (using Comsol and Abaqus), the impact of damage mechanisms on the electrical and thermal properties of a submarine cable phase. The influence of plasticity and gradual copper wire failure on the physical behavior (electric and thermal) of the phase was assessed. The heat differences between a healthy conductor vs. a damaged one (either deformed plastically and/or with failed wires) derived from the numerical model may be an accurate indicator of the level of damage of wires, thus furthering advanced warning before being obliged to stop the exploitation because a mandatory heavy maintenance of the cables must be scheduled. Note that this can also be achieved by using an optical fiber as a sensor for structural health monitoring. This study will then make it possible to evaluate the impact of the modification of the resistance on the thermal behavior of the cable. All of these simulations will be carried out on one phase of a 36 kV 120 mm² copper submarine cable. Colloquially these are called “copper cables”, meaning cables with Cu conductors (120 mm2 is the smallest conductor cross-section for array cables, which are usually 3-phase cables).

Piezoelectric Glass‐Fiber‐Reinforced Polymer Sensor for Structural Health Monitoring and Stiffness Sensing

Strengthening and repairing infrastructure with fiber‐reinforced polymers while achieving low‐cost, real‐time monitoring of cracks in engineering structures is highly challenging. Herein, a piezoelectric glass‐fiber‐reinforced polymer (piezo‐GFRP) sensor is used to monitor the mechanical behavior of cement beams with cracks under cyclic bending. The output voltage and frequency of the sensor are shown to be effective feedback signals for determining the mechanical behavior. The thin piezo‐GFRP sensor shows sufficient piezoelectric sensitivity to monitor the slight variations in the mechanical behavior of the cement beams during the early stages of cracking. It is indicated in this result that such sensors have the potential for the multifunctional structural health monitoring of engineering structures.

Review of Wireless RFID Strain Sensing Technology in Structural Health Monitoring.

Strain-based condition evaluation has garnered as a crucial method for the structural health monitoring (SHM) of large-scale engineering structures. The use of traditional wired strain sensors becomes tedious and time-consuming due to their complex wiring operation, more workload, and instrumentation cost to collect sufficient data for condition state evaluation, especially for large-scale engineering structures. The advent of wireless and passive RFID technologies with high efficiency and inexpensive hardware equipment has brought a new era of next-generation intelligent strain monitoring systems for engineering structures. Thus, this study systematically summarizes the recent research progress of cutting-edge RFID strain sensing technologies. Firstly, this study introduces the importance of structural health monitoring and strain sensing. Then, RFID technology is demonstrated including RFID technology's basic working principle and system component composition. Further, the design and application of various kinds of RFID strain sensors in SHM are presented including passive RFID strain sensing technology, active RFID strain sensing technology, semi-passive RFID strain sensing technology, Ultra High-frequency RFID strain sensing technology, chipless RFID strain sensing technology, and wireless strain sensing based on multi-sensory RFID system, etc., expounding their advantages, disadvantages, and application status. To the authors' knowledge, the study initially provides a systematic comprehensive review of a suite of RFID strain sensing technology that has been developed in recent years within the context of structural health monitoring.

Multifunctional Graphene–Polymer Nanocomposite Sensors Formed by One-Step In Situ Shear Exfoliation of Graphite

Graphene nanocomposites are a promising class of advanced materials for sensing applications; yet, their commercialization is hindered due to impurity incorporation during fabrication and high costs. The aim of this work is to prepare graphene–polysulfone (G−PSU) and graphene–polyvinylidene fluoride (G−PVDF) nanocomposites that perform as multifunctional sensors and are formed using a one-step, in situ exfoliation process whereby graphite is exfoliated into graphene nanoflakes (GNFs) directly within the polymer. This low-cost method creates a nanocomposite while avoiding impurity exposure since the raw materials used in the in situ shear exfoliation process are graphite and polymers. The morphology, structure, thermal properties, and flexural properties were determined for G−PSU and G−PVDF nanocomposites, as well as the electromechanical sensor capability during cyclic flexural loading, temperature sensor testing while heating and cooling, and electrochemical sensor capability to detect dopamine while sensing data wirelessly. G−PSU and G−PVDF nanocomposites show superior mechanical characteristics (gauge factor around 27 and significantly enhanced modulus), thermal characteristics (stability up to 500 °C and 170 °C for G−PSU and G−PVDF, respectively), electrical characteristics (0.1 S/m and 1 S/m conductivity for G−PSU and G−PVDF, respectively), and distinguished resonant peaks for wireless sensing (~212 MHz and ~429 MHz). These uniquely formed G−PMC nanocomposites are promising candidates as strain sensors for structural health monitoring, as temperature sensors for use in automobiles and aerospace applications, and as electrochemical sensors for health care and disease diagnostics.