Real-time Strain Research Articles

Evaluating the effect of post-grouting on long bored pile based on ultra-weak fiber Bragg grating array

Fiber optic sensing has been widely acknowledged as a dependable tool to monitor pile performance. However, its applicability is restricted by the limited multiplexing capacity of fiber Bragg gratings (FBG) or the high equipment cost and poor real-time performance of distributed fiber optic sensing systems. In this paper, a novel sensing cable and system were developed based on ultra-weak FBG array for strain measurement of large-scale engineering structures. Through the laboratory calibration and field test of a post-grouting pile, the good strain measurement performance of the sensing cable and system were confirmed. Based on the distributed strain data obtained by the UWFBG array, the strengthening mechanism of the pile was well explained. The UWFBG array and sensing system could realize distributed, high-precision, and real-time strain measurement with a spatial resolution of 1 m within 1.5 km, which has great potentials in the monitoring of geo-environment and engineering structures.

Effect of Vulcanization on the Electro-Mechanical Sensing Characteristics of Multi-Walled Carbon Nanotube/Silicone Rubber Composites.

In order to realize effective monitoring for the working performance of seismic isolation structures, a multi-walled carbon nanotube (MWCNT)/methyl vinyl silicone rubber (VMQ) composite was prepared via mechanical blending using dicumyl peroxide (DCP) and 2,5-dimethyl-2,5-di(tert-butyl peroxy)hexane (DBPMH) as vulcanizing agents. The effects of the different vulcanizing agents on the dispersion of the MWCNT, electrical conductivity, mechanical properties, and resistance-strain response of the composites were investigated. The experimental results showed that the percolation threshold of the composites prepared with the two vulcanizing agents was low, while the DCP-vulcanized composites showed high mechanical properties and a better resistance-strain response sensitivity and stability, especially after 15,000 loading cycles. According to the analysis using scanning electron microscopy and Fourier infrared spectroscopy, it was found that the DCP contributed higher vulcanization activity, a denser cross-linking network, better and uniform dispersion, and a more stable damage-reconstruction mechanism for the MWCNT network during the deformation load. Thus, the DCP-vulcanized composites showed better mechanical performance and electrical response abilities. When employing an analytical model based on the tunnel effect theory, the mechanism of the resistance-strain response was explained, and the potential of this composite for real-time strain monitoring for large deformation structures was confirmed.

A self-healing magneto-responsive nanocellulose ferrogel and flexible soft strain sensor

Crosslinks are the building blocks of hydrogels and play an important role in their overall properties. They may either be reversible and dynamic allowing for autonomous self-healing properties, or permanent and static resulting in robustness and mechanical strength. Hence, a combination of crosslinks is often required to engineer the 3D network of hydrogels with both autonomous self-healing and required robustness for strain sensing application; however, this complicates the fabrication of such hydrogels. The facile, yet versatile, approach used in this study is to forgo the use of extra crosslinks and instead rely solely on the properties of magnetic nanocellulose to fabricate a tough, stretchy, yet magneto-responsive, ionic conductive ferrogel for strain sensing. The ferrogel also gives stimuli-free and autonomous self-healing capacity, as well as the ability to monitor real-time strain under external magnetic actuation. The ferrogel also functions as a touch-screen pen. Based on our findings, this study has the potential to advance the rational design of multifunctional hydrogels, with applications in soft and flexible strain sensors, health monitoring and soft robotics.

Sonoelastographic evaluation of salivary gland lesions with clinicopathological association

Sonoelastography is a comparatively new and developing technology in the field of salivary gland imaging. Nevertheless, it has the potential to distinguish between various types of lesions by calculating the degree of strain-related deformation under the externally applied force. With this background, the present study was undertaken to evaluate the role of sonoelastography in characterising salivary gland lesions as benign or malignant.
 The aim: To evaluate and characterize salivary Gland lesions on the Gray scale and Colour doppler ultrasonography and sonoelastography and to correlate these findings with the clinico-pathological diagnosis.
 Methodology: This prospective cross-sectional study was conducted in the Department of Radiodiagnosis, Teerthanker Mahaveer Medical College & Research Centre, Moradabad (U.P.), from Aug 2021 to Nov 2022. All patients referred to the radiology department for imaging with clinical suspicion of having salivary gland lesions were enrolled in the study and evaluated on the SIEMENS ACUSON S3000 machine. Gray scale USG was done first to assess various morphological features of lesions, and then a Doppler assessment was done to determine vascularity within the lesion. Subsequently, real-time strain elastography (eSie touch) was performed to assess the tissue stiffness. The elastogram image of the detected lesions was evaluated using colour coding ranging from blue (soft) through green (intermediate/average hardness) and red (hard). After strain elastography, shear wave elastography of the lesion was also performed using Virtual Touch Quantification (VTQ) and Virtual Touch Imaging Quantification (VTIQ) software. The sonographic findings were correlated with histopathological diagnosis. The acquired data were subjected to statistical analysis using the software SPSS version 20. Sensitivity, specificity, PPV and NPV were calculated for conventional ultrasound techniques alone & in combination with elastography.
 Results: Out of the 50 salivary gland lesions included in the study, 44 (88 %) were benign, whereas 6 (12 %) were malignant on cytology. The age of the study population ranged from 16 to 75 years, with a mean age of 38.82 years. Pleomorphic adenoma (60 %) was the most frequent lesion, followed by Warthin's tumour (28 %). The Conventional USG showed 66.67 %, sensitivity, 52.27 %, specificity, 16.00 %, PPV, 92.00 % NPV and 54.00 % accuracy in differentiating benign from malignant lesions while USG- Elastography combined showed higher diagnostic performance with 83.33 %, sensitivity, 79.55 %, specificity, 35.71 % PPV, 97.22 % NPV and 80.00 %, accuracy. The specific cut-off scores for the sonoelastography score, eSie touch, VTQ, and VTIQ were also determined to diagnose a lesion as malignant or benign, and the difference was found to be statistically significant.
 Conclusions: Sonoelastography alone cannot be solely relied upon to distinguish between malignant & benign salivary gland abnormalities. However, it can be combined with conventional USG for better differentiation and characterization of these lesions

A collection of machine learning assisted distributed fiber optic sensors for infrastructure monitoring

Abstract In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures.

Swallow Neural Network-Empowered High-Speed Brillouin Optical Correlation-Domain Reflectometry: Optimization and Real-Time Operation

In this article, a fiber-optic sensor based on Brillouin optical correlation-domain reflectometry (BOCDR) with a high repetition rate and real-time signal processing function is demonstrated with the assistance of swallow neural networks (NNs) constituted of no more than three hidden layers. In the proposed scheme, the signal processing time for every single Brillouin spectrum is compressed to less than the acquisition period of the spectrum by designing the structure of the NNs, on the basis of the knowledge of the relationship between the implementation time and the preliminary calculation count involved in the NNs. In the experiments with both simulated data and the real-world system, the performances of NNs with different sizes are studied from the perspectives of timing and accuracy. By training NN models with the data acquired from the experiments, real-time dynamic strain measurement is realized with a repetition rate of up to 20 kHz and a dynamic range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4000 \mu \varepsilon $ </tex-math></inline-formula> . Different from other works regarding machine learning-empowered measurement acceleration in distributed optical fiber sensors with an offline signal processing phase, the method proposed in this article enables consecutive monitoring of the parameters under test.

Sonoelastographic Evaluation of Pelvic Adnexal Masses and its Association with Clinicopathological Findings at a Tertiary Care Centre in Western Uttar Pradesh, India

Introduction: Despite advancements in cross-sectional aging techniques, ultrasonography continues to remain the first-line imaging modality for the preoperative assessment of pelvic adnexal masses. Ultrasound strain elastography is a novel technique that can characterise adnexal lesions based on their tissue stiffness and when used in conjunction with conventional USG, it may increase the precision of diagnosis and can act as a cost-effective viable ancillary tool. Aim: To assess the role of strain sonoelastography in characterising the adnexal lesion as benign or malignant. Materials and Methods: This cross-sectional study was conducted in the Department of Radiodiagnosis, Teerthankar Mahaveer Medical College and Research Centre (TMMC&amp;RC), Moradabad, Uttar Pradesh, India, for a period of 18 months (January 2020 to June 2021) and consisted of 110 patients with clinical suspicion of adnexal mass, who were evaluated on SIEMENS Acuson S 3000 scanner (from the total of 130 cases). Various morphological features of mass (size, laterality, consistency, echogenicity and internal contents) were assessed on Gray scale and vascularity was assessed on colour doppler Ultrasonography (USG). Subsequently, real time strain elastography (eSie touch) was performed to assess the tissue stiffness. Elasticity was indicated on a colour-coded elastogram map, with blue areas denoting hard tissue, green areas suggesting intermediate tissue, and red portions denoting soft tissues. The sonographic findings were compared with histopathological diagnosis. Sensitivity, specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were calculated for Gray scale sonography in combination with Doppler (conventional ultrasound techniques) and in conjunction with elastography. The Chi-square test was applied for comparing the frequency and p-value less than 0.05 was considered to be significant. Results: The mean age of study population was 36.35±14.82 years. On histopathology out of 110 patients, 95 (86.36%) had benign adnexal lesions and 15 (13.64%) had malignant adnexal lesions. The youngest patient was 16-year-old and eldest was 70 years and the mean age of the study population was 36.35 years. When conventional ultrasound technique was used alone for differentiating benign and malignant adnexal masses the sensitivity was 96.7%, specificity was 83%, PPV was 96.7% and NPV was 83% while on addition of sonoelastography the sensitivity increased to 98.9%, specificity increased to 93%, PPV increased to 98.9% and NPV increased to 93.7%. Conclusion: Conventional ultrasound techniques should be combined with sonoelastography in a diagnostic system to achieve better characterisation and differentiation of benign and malignant adnexal masses.

A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks.

The measurement and prediction of breast skin deformation are key research directions in health-related research areas, such as cosmetic and reconstructive surgery and sports biomechanics. However, few studies have provided a systematic analysis on the deformations of aging breasts. Thus, this study has developed a model order reduction approach to predict the real-time strain of the breast skin of seniors during movement. Twenty-two women who are on average 62 years old participated in motion capture experiments, in which eight body variables were first extracted by using the gray relational method. Then, backpropagation artificial neural networks were built to predict the strain of the breast skin. After optimization, the R-value for the neural network model reached 0.99, which is within acceptable accuracy. The computer-aided system of this study is validated as a robust simulation approach for conducting biomechanical analyses and predicting breast deformation.

High-Performance Strain Sensors Based on Organohydrogel Microsphere Film for Wearable Human-Computer Interfacing.

Stretchable hydrogel-based strain sensors suffer from limited sensitivity, which urgently requires further breakthroughs for precise and stable human-computer interaction. Here, an efficient microstructural engineering strategy is proposed to significantly enhance the sensitivity of hydrogel-based strain sensors by sandwiching an emulsion-polymerized polyacrylamide organohydrogel microsphere membrane between two Ecoflex films, which are accompanied by crack generation and propagation effects upon stretching. Consequently, the as-developed strain sensor exhibits ultrahigh sensitivity (gauge factor (GF) of 1275), wide detection range (100% strain), low hysteresis, ultralow detection limit (0.05% strain), good fatigue resistance, and low fabrication cost. In addition, the sensor features good water, dehydration, and frost resistance, enabling real-time strain monitoring in various complex conditions due to the encapsulation of Ecoflex film and the addition of glycerol and KCl. Through further structural manipulation, the device achieves superior response to tiny strains, with a GF value of 98.3 in the strain range of less than 1.5%. Owing to the high strain sensing performance, the sensor is able to detect various human activities from swallowing to finger bending even under water. On this basis, a wireless sensing system with apnea warning and single-channel gesture recognition capabilities is successfully demonstrated, demonstrating its great promise as wearable electronics.

In-process real-time probiotic phenotypic strain identity tracking: The use of Fourier transform infrared spectroscopy.

Probiotic bacteria, capable of conferring benefits to the host, can present challenges in design, development, scale-up, manufacturing, commercialization, and life cycle management. Strain identification is one of the main quality parameters; nevertheless, this task can be challenging since established methodologies can lack resolution at the strain level for some microorganisms and\or are labor-intensive and time-consuming. Fourier transform infrared spectroscopy (FTIRS) has been largely used for the investigation of pathogenic species in the clinical field, whereas only recently has been proposed for the identification of probiotic strains. Within the probiotic industrial production, bacterial strains can be subjected to stressful conditions that may affect genomic and phenotypic characteristics; therefore, real-time monitoring of all the sequential growth steps is requested. Considering the fast, low-cost, and high-throughput features, FTIRS is an innovative and functional technology for typing probiotic strains from bench-top experiments to large-scale industrial production, allowing the monitoring of stability and identity of probiotic strains. In this study, the discriminatory power of FTIRS was assessed for four Lactiplantibacillus plantarum probiotic strains grown under different conditions, including temperatures (30 and 37°C) and medium (broth and agar), after consecutive sub-culturing steps. A comparison between the generated spectra with pulsed-field gel electrophoresis (PFGE) profiles was also performed. FTIRS was not only able to distinguish the strains of L. plantarum under different growth conditions but also to prove the phenotypic stability of L. plantarum type strain LP-CT after six growing steps. Regardless of the growth conditions, FTIRS spectra related to LP-CT constituted a unique hierarchical cluster, separated from the other L. plantarum strains. These results were confirmed by a PFGE analysis. In addition, based on FTIRS data, broth cultures demonstrated a higher reproducibility and discriminatory power with respect to agar ones. These results support the introduction of FTIRS in the probiotic industry, allowing for the step-by-step monitoring of massive microbial production while also guaranteeing the stability and purity of the probiotic strain. The proposed novel approach can constitute an impressive improvement in the probiotic manufacturing process.

Effects of large strain reverse loading on the strain rate dependence and dynamic strain localization of ductile metallic rods

The dynamic necking of ductile metallic rods with large strain reverse loading history has received little attention in the published literature. A novel bespoke real time strain control setup is constructed to apply the reverse loading directly to the specimen gauge section up to a maximum strain level of ± 0.16. 304L stainless steel is used as a model material in this study. The subsequent tensile tests of the reverse loaded specimens are performed from quasi-static to high strain rates of 1000/s, using a Zwick 050 Machine, hydraulic Instron 8854, and a bespoke split Hopkinson tension bar with high speed photography equipment. The initial flow stress of the 304L rods shows similar strain rate dependence, regardless of the reverse loading history. The local strain rate during strain localization increases dramatically and eventually reaches one order of magnitude higher than the nominal strain rate. A higher strain reverse loading significantly influences the development of necking instabilities, with smaller strain to necking inception, higher local stress in the necking zone, and higher local strain rate up to failure. Instead of evaluating the impact energy absorption up to necking, an analysis of the local stress–strain relationship indicates that the reverse loaded 304L shows good impact energy absorption up to failure. This agrees with the ductile fracture surfaces of the 304L materials with reverse loading.

In-situ monitoring of internal temperature and strain of solid-state battery based on optical fiber sensors

As an emerging battery, solid-state battery has broad development and application prospects. The safety and stability of the battery cannot be monitored in real-time during the work process. In this work, we fabricated solid electrolytes, assembled solid-state battery and proposed a method to monitor internal real-time temperature and strain information by embedding short fiber Bragg gratings in the battery. The experimental results show that the maximum temperature change is 0.42 °C and the maximum strain change is 11.5 με during the charging and discharging processes of the battery, and the formation of lithium dendrites is observed. This method enables real-time online in-situ monitoring of temperature and strain in solid-state battery and explains the causes of strain at the microscopic level, which is critical for the safe use of the battery. • Optical fiber sensors embedded in solid-battery for in-situ measurements. • Real-time monitoring of temperature and strain. • To explain the formation of strain at the microscopic level. • To optimise of battery designs and to promote solid-battery safety improvement.

Strain self-sensing capability of a tidal turbine blade fabricated of PU-foam/glass fiber/epoxy composites using MWCNTs

A novel marine composite structure (experimental tidal turbine blade) made up of a polyurethane (PU) foam/glass fiber/epoxy resin composite with multiwall carbon nanotubes (MWCNTs) is proposed herein to self-sense its strain under a structural test scenario. To achieve this, MWCNTs were deposited onto glass fiber fabric by spray-coating technique in order to form an effective electrical percolation network onto the external skin surface of the tidal turbine blade which enables piezoresistive capability. After MWCNT deposition, the blade was manufactured by means of one-shot resin transfer molding (RTM) in a closed and heated metallic mold specially designed with a blade geometry of 67 cm length. The results confirm that the spray coating technique is a viable method to deposit MWCNTs onto glass fiber surface and form electrical networks into the blade at a relatively low MWCNT concentration. Finite element analysis (FEA) predicted a suitable structural blade design with a maximum failure index value of 0.9 attained in the blade shear web. The measured longitudinal strains of the tidal turbine blade were in good agreement with the numerical strain values predicted with FEA. Electromechanical tests carried out on a structural test rig designed and instrumented for tidal turbine blades showed that the electrical resistance change response of carbon nanotube (CNT) network integrated into the blade was capable of following the mechanical curve response up to the blade limit load, confirming its ability to self-sense its strain in real time.

Modification of the CSIRO method in the long-term monitoring of slope-induced stress

The in situ stress measurement technique can be used in long-term monitoring of induced stress to obtain three-dimensional stress states in slope engineering. In the monitoring, the ambient temperature fluctuation, “zero” drift of the logging data, and stable power supply should be considered. A dual temperature compensation circuit and experimental calibration techniques are proposed to increase accuracy based on the principle of the CSIRO method and considering the effect of a long-term disturbance of temperature in slope engineering. In view of the characteristics of the stress field distribution of the slope rock, the strain gauge layout scheme for induced stress monitoring of slope rock is modified to achieve the acquisition of three parallel measurements at one measurement and a stress–strain relationship equation for multilayer elastic layers is derived. An in situ stress logging system with the function of transient logging and data-connection in power cut condition is introduced to achieve a continuous correlation of measurement data under power cut conditions. An online cloud platform is established for the slope engineering of Au Mountain Iron Mine in Maanshan City. The results show that the data deviation of logging system is ±14 με with 24 days’ power off, and errors with amount of 15% were corrected by the dual temperature compensation method. The remotely transmitted data reflect the effect of temperature changes and the evolution of strain (induced stress) in real time to satisfy the demand of long-term monitoring of slope engineering.

Machine learning and image processing to monitor strain and tensile forces with mechanochromic sensors

A computer vision (CV) system is proposed for real-time prediction of strain by monitoring the color-changing feature of mechanochromic sensors. Pictures of the sensors subjected to calibration tensile tests were treated with standard image processing methods and analyzed using supervised machine learning (ML) algorithms. Visual strain sensing was demonstrated by linear regression models capable of learning a relation between the applied strain and the reflected structural color. The ElasticNet regression model provided the highest accuracy in the strain prediction task, with a remarkable performance in monitoring real-time strain variation of sensors during a tensile-relaxion cycle. Using calibration curves, the predicted strain can also be employed for estimating the tensile force applied on the mechanochromic sensors. Taken together these results point to potential intelligent systems for noninvasive in-situ visual monitoring of deformations and tensions.

Surveillance diagnostic algorithm using real-time PCR assay and strain typing method development to assist with the control of C. auris amid COVID-19 pandemic

Candida auris continues to be a global threat for infection and transmission in hospitals and long-term care facilities. The emergence of SARS-CoV-2 has rerouted attention and resources away from this silent pandemic to the frontlines of the ongoing COVID-19 disease. Cases of C. auris continue to rise, and clinical laboratories need a contingency plan to prevent a possible outbreak amid the COVID-19 pandemic. Here, we introduce a two-tier Candida auris surveillance program that includes, first, a rapid qualitative rt-PCR for the identification of high-risk patients and, second, a method to analyze the isolated C. auris for strain typing using the Fourier-Transform Infrared spectroscopy. We have performed this two-tier surveillance for over 700 at-risk patients being admitted into our hospital and have identified 28 positive specimens (4%) over a 1-year period. Strain typing analysis by the IR spectrum acquisition typing method, supplemented by whole genome sequencing, has shown grouping of two significant clusters. The majority of our isolates belong to circulating African lineage associated with C. auris Clade III and an isolated strain grouping differently belonging to South Asian lineage C. auris Clade I. Low numbers of genomic variation point to local and ongoing transmission within the Los Angeles area not specifically within the hospital setting. Collectively, clinical laboratories having the ability to rapidly screen high-risk patients for C. auris and to participate in outbreak investigations by offering strain typing will greatly assist in the control of C. auris transmission within the hospital setting.

A strain-sensitivity-enhanced and asymmetric fiber-optic sensor based on anti-resonance hollow core fiber

We proposed a novel strain-sensitivity-enhanced optical fiber sensor with high strain sensitivity realized by anti-resonance hollow core fiber. The coreless fiber and the anti-resonance hollow core fiber were connected between the two single-mode fibers forming the sensor. Due to the unique structure, the sensor can be operated in transmission and reflection types. The results show that the strain sensitivity of the sensor can reach 23.02 dB/mε operating at the transmission type by welding with 18 mm anti-resonance hollow core fiber. Given a power resolution of 0.01 dBm, the strain resolution of the sensor can reach ∼0.43 με. The sensor has the advantages of high strain sensitivity, outstanding repeatability, excellent time responsiveness and temperature insensitivity which can be used for real-time and high-precision strain measurements in large temperature difference environments. Besides, it was significant for the strain-sensitivity-enhancement effect studying and the development of a new optical fiber sensor with anti-resonance hollow core fiber.

Road Pavement Structural Health Monitoring by Embedded Fiber-Bragg-Grating-Based Optical Sensors

Fiber Bragg grating (FBG) optical sensors are state-of-the-art technology that can be integrated into the road structure, providing real-time traffic-induced strain readings and ensuring the monitoring of the road’s structural health. By implementing specific FBG sensors, it is possible to detect each vehicle’s axle count and the induced strain changes in the road structure. In this study, FBG sensors are embedded at the top of the 240-mm-thick cement-treated reclaimed asphalt pavement mixture layer of the road (specifically, 25 mm deep within the road). Optical sensors’ signal interrogation units are used to measure the strain and temperature and collect data of the road’s passing vehicles, starting from passenger cars that have two axles and up to heavy trucks that have six axles. Passenger cars with 2 axles generate a typical (90% events) strain of 0.8–4.1 μm/m, the 2-axle minibus 5.5–8.5 μm/m, 2–3-axle trucks 11–26 μm/m, but 4–6-axle trucks 14–36 μm/m per each axle. A large number of influencing parameters determine the pavement design leading to the great uncertainty in the prediction of the strain at the boundary between the asphalt surface and cement-treated base layers. Real-time strain and temperature measurements help to understand the actual behavior of the pavement structure under an applied load, thus assisting in validating the proposed pavement design.

Electroluminescent strain sensing on carbon fiber reinforced polymer

The present paper demonstrates an inexpensive and less labor-intensive machine vision-based strain and damage visualization technique compared to traditional carbon fiber reinforced polymer material inspection methods. This technique is used to characterize the fracture and failure properties upon progressive axial loading in textile carbon fiber reinforced polymer (CFRP) lamina with non-intrusive smart electroluminescent (EL) integration. The smart EL structure utilizes CFRP's unique conductive and piezoresistive properties to visualize deformation and cracks through brightness change. Two fiber orientations (±45° and 0°/90°) of twill-weave CFRP lamina are fabricated according to a modified ASTM D3039 for linear and fatigue loading in uniaxial mechanical tests to analyze both fiber and matrix-induced EL responses. The EL-strain visualization method is validated with standardized digital image correlation (DIC) utilizing the natural speckling pattern of the EL CFRP lamina. The axial EL-strain results for both static and cyclic loading highly agree with the strain extracted planar conventional DIC strain field on standard and damaged CFRP samples in terms of spatial resolution and sensitivity. This novel strain and damage visualization method aims to be a portable, low-cost, and real-time alternative to non-destructive testing methods commonly used in the composite industry. Instead of periodic checkups, EL architecture allows for real-time strain and damage visualization of composite structure. • Fabrication and tensile testing of strain sensing EL illuminating woven CFRP in [±45°] and [0°/90°] fiber orientations. • Visual damage and strain detection are achieved with EL CFRP structure. • Planar DIC strain fields produced on EL illuminating and conventional airbrushed patterns show strong correspondence. • Variations in piezoresistive and luminance response exhibit correlation with strain.

Polymer Mechanochromism from Force-Tuned Excited-State Intramolecular Proton Transfer.

Real-time monitoring of strain/stress in polymers is a big challenge to date. Herein, we for the first time report an ESIPT (excited-state intramolecular proton transfer)-based mechanochromic mechanophore (MM). The synthesis of target MM PhMz-4OH [(2-hydroxyphenyl)benzimidazole with four aliphatic hydroxyls] is quite facile. PhMz-4OH possesses characteristic dual emissions, and its ESIPT activity is greatly affected by steric hindrance. Then, PhMz-4OH was covalently linked into polyurethane chains (PhMz-4OH@PU). Upon stretching, the PhMz-4OH@PU films showed fluorescence color change and spectral variation with the increase in enol emission and blueshift of keto emission due to the force-induced torsion of the dihedral angle between the proton donor and the proton acceptor. The PhMz-4OH@PU films with high mechanophore concentrations (>0.36 mol %) might undergo a two-stage force-responsive process, including torsion of the dihedral angle via force-induced disaggregation and direct chain-transduced force-induced torsion of the dihedral angle. The intensity ratio of enol emission to keto emission (IE/IK) shows a quantitative correlation with elongation, and real-time strain sensing is achieved. PhMz-4OH is a successful type II MM (without covalent bond scission) and displays high sensitivity and excellent reversibility to stress. Two control structures PhMz-NH2 and PhMz-2OH were also embedded into PU but no spectral or color changes were detected, further confirming that mechanochromism of PhMz-4OH@PU films arises from the chain-transduced force. Density function theory (DFT) calculation was performed to study the force-tuned ESIPT process theoretically and rationalize the experimental results. This study might lay the foundation for real-time stress/strain sensing in practical applications.