Piezoelectric Materials and Sensors for Structural Health Monitoring: Fundamental Aspects, Current Status, and Future Perspectives

A Soft Robot Tactile Finger Using Oxidation-Reduction Graphene-Polyurethane Conductive Sponge

Currently, intelligent robotics is supplanting traditional industrial applications. It extends to business, service and care industries, and other fields. Stable robot grasping is a necessary prerequisite for all kinds of complex application scenarios. Herein, we propose a method for preparing an elastic porous material with adjustable conductivity, hardness, and elastic modulus. Based on this, we design a soft robot tactile fingertip that is gentle, highly sensitive, and has an adjustable range. It has excellent sensitivity (~1.089 kpa-1), fast response time (~35 ms), and measures minimum pressures up to 0.02 N and stability over 500 cycles. The baseline capacitance of a sensor of the same size can be increased by a factor of 5-6, and graphene adheres better to polyurethane sponge and has good shock absorption. In addition, we demonstrated the application of the tactile fingertip to a two-finger manipulator to achieve stable grasping. In this paper, we demonstrate the great potential of the soft robot tactile finger in the field of adaptive grasping for intelligent robots.

The Health Monitoring of Bonded Composite Joints Using Both Thickness and Radial Impedance Resonance Responses

With the advent of bonded composites in today's aircraft, there is a need to verify the structural integrity of the bonded joints that comprise their structure. To produce adequate joint integrity, strict process control is required during bonding operations. The latest non-destructive joint inspection techniques cannot quantify the strength of the bond and only indicate the presence of disbonds or delaminations. Expensive and timely proof-load testing of the joints is required to demonstrate structural performance. This work focuses on experimentally evaluating joint-health monitoring with piezoelectric sensors exposed to repeated loadings until failure. Single-lap-shear composite joints are structurally tested while using sensor electromechanical impedance response as a health indicator. Based on these experiments, validation of this novel method is achieved through detailed evaluation of the sensor impedance response characteristics during loading, which enable initial and prognostic joint health assessments. The experimental results indicate that the embedded piezoelectric sensors are able to measure the sensor impedance radial and thickness resonance response changes prior to joint failure, without sacrificing the joints' structural performance.

Integration Technology with Thin Films Co-Fabricated in Laminated Composite Structures for Defect Detection and Damage Monitoring

Despite the well-established nature of non-destructive testing (NDT) technologies, autonomous monitoring systems are still in high demand. The solution lies in harnessing the potential of intelligent structures, particularly in industries like aeronautics. Substantial downtime occurs due to routine maintenance, leading to lost revenue when aircraft are grounded for inspection and repairs. This article explores an innovative approach using intelligent materials to enhance condition-based maintenance, ultimately cutting life-cycle costs. The study emphasizes a paradigm shift toward structural health monitoring (SHM), utilizing embedded sensors for real-time monitoring. Active thin film piezoelectric materials are proposed for their integration into composite structures. The work evaluates passive sensing through acoustic emission (AE) signals and active sensing using Lamb wave propagation, presenting amplitude-based and frequency domain approaches for damage detection. A comprehensive signal processing approach is presented, and the damage index and damage size correlation function are introduced to enable continuous monitoring due to their sensitivity to changes in material properties and defect severity. Additionally, finite element modeling and experimental validation are proposed to enhance their understanding and applicability. This research contributes to developing more efficient and cost-effective aircraft maintenance approaches through SHM, addressing the competitive demands of the aeronautic industry.

Exploiting volatile fingerprints for bladder cancer diagnosis: A scoping review of metabolomics and sensor-based approaches

Bladder cancer (BC) represents a significant global health concern, for which early detection is essential to improve patient outcomes. This review evaluates the potential of the urinary volatile organic compounds (VOCs) as biomarkers for detecting and staging BC. The methods used include gas chromatography-mass spectrometry (GC-MS)-based metabolomics and electronic-nose (e-nose) sensors. The GC-MS studies that have been published reveal diverse results in terms of diagnostic performance. The sensitivities range from 27 % to an impressive 97 %, while specificities vary between 43 % and 94 %. Furthermore, the accuracies reported in these studies range from 80 to 89 %. In the urine of BC patients, a total of 80 VOCs were discovered to be significantly altered when compared to controls. These VOCs encompassed a variety of chemical classes such as alcohols, aldehydes, alkanes, aromatic compounds, fatty acids, ketones, and terpenoids, among others. Conversely, e-nose-based studies displayed sensitivities from 60 to 100 %, specificities from 53 to 96 %, and accuracies from 65 to 97 %. Interestingly, conductive polymer-based sensors performed better, followed by metal oxide semiconductor and optical sensors. GC-MS studies have shown improved performance in detecting early stages and low-grade tumors, providing valuable insights into staging. Based on these findings, VOC-based diagnostic tools hold great promise for early BC detection and staging. Further studies are needed to validate biomarkers and their classification performance. In the future, advancements in VOC profiling technologies may significantly contribute to improving the overall survival and quality of life for BC patients.

Electromechanical Properties of Smart Vitrimers Reinforced with Carbon Nanotubes for SHM Applications

The Structural Health Monitoring (SHM) capabilities of a well-studied self-healing epoxy resin based on disulfide bonds, through the addition of carbon nanotubes (CNTs), are studied. Since these materials demonstrated, in recent works, a high dependency of the dynamic hardener content on the repair performance, this study aimed to analyze the effect of the vitrimeric chemistry on the electromechanical properties by studying different 2-aminophenyl disulfide (2-AFD) hardener and CNT contents. The electrical conductivity increases with both the CNT and AFD contents, in general. Moreover, an excess of AFD close to the stoichiometric ratio with a low CNT content improved the tensile strength by 45%, while higher AFD contents promoted its detriment by 41% due to a reduced crosslinking density. However, no significant difference in the mechanical properties was observed at a higher CNT content, regardless of the AFD ratio. The developed materials demonstrate a robust electromechanical response at quasi-static conditions. The sensitivity significantly increases at higher AFD ratios, from 0.69 to 2.22 for the 0.2 wt.%. CNT system, which is advantageous due to the enhanced repair performance of these vitrimeric materials with a higher hardener content. These results reveal the potential use of self-healing vitrimers as integrated SHM systems capable of detecting damages and self-repairing autonomously.

Ceramic Stress Sensor Based on Thick Film Piezo-Resistive Ink for Structural Applications

This paper presents a ceramic stress sensor with the dimension of a coin, able to measure the compressive force (stress) applied to its two round faces. The sensor is designed and engineered to be embedded inside concrete or masonry structures, like bridges or buildings. It provides good accuracy, robustness, and simplicity of use at potentially low cost for large-scale applications in civil structures. Moreover, it can be calibrated temperature compensated, and it is inherently hermetic, ensuring the protection of sensitive elements from the external environment. It is, therefore, suitable for operating in harsh and dirty environments like civil constructions. The sensor directly measures the internal stress of the structure, exploiting the piezo resistivity of thick film ink based on ruthenium oxide. It is insensitive with respect to the stiffness of the embedding material and the variation of the surrounding material properties like concrete hardening, shrinkage, and creep as it decouples the two components of stress.

Monitoring of Composite Structures for Re-Usable Space Applications Using FBGs: The Influence of Low Earth Orbit Conditions

Fiber Bragg grating sensors (FBGs) are promising for structural health monitoring (SHM) of composite structures in space owing to their lightweight nature, resilience to harsh environments, and immunity to electromagnetic interference. In this paper, we investigated the influence of low Earth orbit (LEO) conditions on the integrity of composite structures with embedded optical fiber sensors, specifically FBGs. The LEO conditions were simulated by subjecting carbon fiber-reinforced polymer (CFRP) coupons to 10 cycles of thermal conditioning in a vacuum (TVac). Coupons with embedded optical fibers (OFs) or capillaries were compared with reference coupons without embedded OFs or capillaries. Embedded capillaries were necessary to create in situ temperature sensors. Tensile and compression tests were performed on these coupons, and the interlaminar shear strength was determined to assess the influence of TVac conditioning on the integrity of the composite. Additionally, a visual inspection of the cross-sections was conducted. The impact on the proper functioning of the embedded FBGs was tested by comparing the reflection spectra before and after TVac conditioning and by performing tensile tests in which the strain measured using the embedded FBGs was compared with the output of reference strain sensors applied after TVac conditioning. The measured strain of the embedded FBGs showed excellent agreement with the reference sensors, and the reflection spectra did not exhibit any significant degradation. The results of the mechanical testing and visual inspection revealed no degradation of the structural integrity when comparing TVac-conditioned coupons with non-TVac-conditioned coupons of the same type. Consequently, it was concluded that TVac conditioning does not influence the functionality of the embedded FBGs or the structural integrity of the composite itself. Although in this paper FBG sensors were tested, the results can be extrapolated to other sensing techniques based on optical fibers.

Recent Advances in Flexible Multifunctional Sensors

Wearable electronics have received extensive attention in human-machine interactions, robotics, and health monitoring. The use of multifunctional sensors that are capable of measuring a variety of mechanical or environmental stimuli can provide new functionalities for wearable electronics. Advancements in material science and system integration technologies have contributed to the development of high-performance flexible multifunctional sensors. This review presents the main approaches, based on functional materials and device structures, to improve sensing parameters, including linearity, detection range, and sensitivity to various stimuli. The details of electrical, biocompatible, and mechanical properties of self-powered sensors and wearable wireless systems are systematically elaborated. Finally, the current challenges and future developmental directions are discussed to offer a guide to fabricate advanced multifunctional sensors.

Multi-Damage Detection in Composite Space Structures via Deep Learning

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples.

Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms

Multilayer piezocomposite transducers are widely used in many applications where broad bandwidth is required for tracking and detection purposes. However, it is difficult to operate these multilayer transducers efficiently under frequencies of 100 kHz. Therefore, this work presents the modeling and optimization of a five-layer piezocomposite transducer with ten variables of nonuniform layer thicknesses and different volume fractions by exploiting the strength of the genetic algorithm (GA) with a one-dimensional model (ODM). The ODM executes matrix manipulation by resolving wave equations and produces mechanical output in the form of pressure and electrical impedance. The product of gain and bandwidth is the required function to be maximized in this multi-objective and multivariate optimization problem, which is a challenging task having ten variables. Converting it into the minimization problem, the reciprocal of the gain-bandwidth product is considered. The total thickness is adjusted to keep the central frequency at approximately 50-60 kHz. Piezocomposite transducers with three active materials, PZT5h, PZT4d, PMN-PT, and CY1301 polymer, as passive materials were designed, simulated, and statistically evaluated. The results show significant improvement in gain bandwidth compared to previous existing techniques.

Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection

With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the Bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy.

Electrospun Polyvinylidene Fluoride Piezoelectric Fiber Glass/Carbon Hybrid Self-Sensing Composites for Structural Health Monitoring

In this study, a polyvinylidene fluoride (PVDF)/graphene nanoplatelet (GNP) micro-nanocomposite membrane was fabricated through electrospinning technology and was employed in the fabrication of a fiber-reinforced polymer composite laminate. Some glass fibers were replaced with carbon fibers to serve as electrodes in the sensing layer, and the PVDF/GNP micro-nanocomposite membrane was embedded in the laminate to confer multifunctional piezoelectric self-sensing ability. The self-sensing composite laminate has both favorable mechanical properties and sensing ability. The effects of different concentrations of modified multiwalled carbon nanotubes (CNTs) and GNPs on the morphology of PVDF fibers and the β-phase content of the membrane were investigated. PVDF fibers containing 0.05% GNPs were the most stable and had the highest relative β-phase content; these fibers were embedded in glass fiber fabric to prepare the piezoelectric self-sensing composite laminate. To test the laminate's practical application, four-point bending and low-velocity impact tests were performed. The results revealed that when damage occurred during bending, the piezoelectric response changed, confirming that the piezoelectric self-sensing composite laminate has preliminary sensing performance. The low-velocity impact experiment revealed the effect of impact energy on sensing performance.

Intraoperative Beat-to-Beat Pulse Transit Time (PTT) Monitoring via Non-Invasive Piezoelectric/Piezocapacitive Peripheral Sensors Can Predict Changes in Invasively Acquired Blood Pressure in High-Risk Surgical Patients

BACKGROUND

Non-invasive tracking of beat-to-beat pulse transit time (PTT) via piezoelectric/piezocapacitive sensors (PES/PCS) may expand perioperative hemodynamic monitoring. This study evaluated the ability for PTT via PES/PCS to correlate with systolic, diastolic, and mean invasive blood pressure (SBP_IBP, DBP_IBP, and MAP_IBP, respectively) and to detect SBP_IBP fluctuations.

METHODS

PES/PCS and IBP measurements were performed in 20 patients undergoing abdominal, urological, and cardiac surgery. A Pearson's correlation analysis (r) between 1/PTT and IBP was performed. The predictive ability of 1/PTT with changes in SBP_IBP was determined by area under the curve (reported as AUC, sensitivity, specificity).

RESULTS

Significant correlations between 1/PTT and SBP_IBP were found for PES (r = 0.64) and PCS (r = 0.55) (p < 0.01), as well as MAP_IBP/DBP_IBP for PES (r = 0.6/0.55) and PCS (r = 0.5/0.45) (p < 0.05). A 7% decrease in 1/PTT_PES predicted a 30% SBP_IBP decrease (0.82, 0.76, 0.76), while a 5.6% increase predicted a 30% SBP_IBP increase (0.75, 0.7, 0.68). A 6.6% decrease in 1/PTT_PCS detected a 30% SBP_IBP decrease (0.81, 0.72, 0.8), while a 4.8% 1/PTT_PCS increase detected a 30% SBP_IBP increase (0.73, 0.64, 0.68).

CONCLUSIONS

Non-invasive beat-to-beat PTT via PES/PCS demonstrated significant correlations with IBP and detected significant changes in SBP_IBP. Thus, PES/PCS as a novel sensor technology may augment intraoperative hemodynamic monitoring during major surgery.