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Mechanoluminescent (ML) materials can exhibit visible-to-near-infrared mechanoluminescence when responding to the fracture or deformation of a solid under mechanical stimulation. Transforming mechanical energy into light demonstrates promising applications in terms of visual mechanical sensing. In this work, we synthesized the phosphor CaZnOS:Tb[sup.3+], Sm[sup.3+], which exhibited intense and tunable multicolor mechanoluminescence without pre-irradiation. Intense green ML materials were obtained by doping Tb[sup.3+] with different concentrations. Tunable multicolor mechanoluminescence (such as green, yellow-green, and orange-red) could be realized by combining green emission (about 542 nm), attributed to Tb[sup.3+], and red emission (about 600 nm) generated from the Sm[sup.3+] in the CaZnOS substrate. The tunable multicolor ML materials CaZnOS:Tb[sup.3+], Sm[sup.3+] exhibited intense luminance and recoverable mechanoluminescence when responding to mechanical stimulation. Benefiting from the excellent ML performance and multicolor tunability in CaZnOS:Tb[sup.3+], Sm[sup.3+], we mixed the phosphor with PDMS and a curing agent to explore its practical application. An application for visual mechanical sensing was designed for handwriting identification. By taking a time-lapsed shot while writing, we easily obtained images of the writer’s handwriting. The images of the ML intensity were acquired by using specific software to transform the shooting data. We could easily distinguish people’s handwriting through analyzing the different ML performances.

Monitoring structural health using mechanoluminescent (ML) effects is widely considered as a potential full‐field and direct visualizing optical method with high spatial and temporal resolution and simple setup in a noncontact manner. The challenges and uncertainties in the mapping of ML field to effective strain field, however, tend to limit significant commercial ML applications for structural health monitoring systems. Here, however, quantification problems are resolved using the digital image correlation (DIC) method. Specifically, an image containing mechanically induced photon information is processed using a DIC algorithm to measure the strain field components, which enables the establishment of a calibration curve when the ML field is mapped onto the effective strain field using pixel level information. The results show a linear relationship between effective strain and ML intensity despite the plastic flow in ML skin. Furthermore, the calibration curve allows for easy conversion of ML field to effective‐strain field at the crack‐tip plastic zone of the alloy structure, retaining its spatial resolution. The compatibility of ML skin with the DIC algorithm not only enables the quantification of the ML effects of several organic/inorganic ML materials, but may also be useful in elucidating the fundamentals of the trap‐controlled mechanism. The compatibility of mechanoluminescent (ML) skin with the digital image correlation under UV exposure is leveraged to quantify the ML effects in terms of effective strain. The calibration can be executed using the same photographic image. Interestingly, a linear correlation exists despite the plastic flow in ML skin. The quantification extends the ML application from visualizing to quantitative measurement.

Highlights The sandwich-structured flexible mechanoluminescent sensor (SFLC) film shows great application potential as wireless wearable strain sensor and encryption device. System-level integration of SFLC film with deep learning-based artificial intelligence enables fast and accurate interpretation of color data to strain values with automatic correction of errors caused by varying color temperatures. The smart glove wearable sensor based on the SFLC film combined with deep learning neural network enables fast and accurate hand gesture recognition. The complex wiring, bulky data collection devices, and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices. To tackle these challenges, this work develops an artificial intelligence-assisted, wireless, flexible, and wearable mechanoluminescent strain sensor system (AIFWMLS) by integration of deep learning neural network-based color data processing system (CDPS) with a sandwich-structured flexible mechanoluminescent sensor (SFLC) film. The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication. The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature, which significantly improves the accuracy of the predicted strain. A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition. Moreover, the versatile SFLC film can also serve as a encryption device. The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the “color to strain value” bottleneck that hinders the practical application of flexible colorimetric strain sensors, which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.

Flexible electro‐optical dual‐mode sensor fibers with capability of the perceiving and converting mechanical stimuli into digital‐visual signals show good prospects in smart human‐machine interaction interfaces. However, heavy mass, low stretchability, and lack of non‐contact sensing function seriously impede their practical application in wearable electronics. To address these challenges, a stretchable and self‐powered mechanoluminescent triboelectric nanogenerator fiber (MLTENGF) based on lightweight carbon nanotube fiber is successfully constructed. Taking advantage of their mechanoluminescent‐triboelectric synergistic effect, the well‐designed MLTENGF delivers an excellent enhancement electrical signal of 200% and an evident optical signal whether on land or underwater. More encouragingly, the MLTENGF device possesses outstanding stability with almost unchanged sensitivity after stretching for 200%. Furthermore, an extraordinary non‐contact sensing capability with a detection distance of up to 35 cm is achieved for the MLTENGF. As application demonstrations, MLTENGFs can be used for home security monitoring, intelligent zither, traffic vehicle collision avoidance, and underwater communication. Thus, this work accelerates the development of wearable electro‐optical textile electronics for smart human‐machine interaction interfaces. Stretchable and self‐powered mechanoluminescent triboelectric nanogenerator fiber (MLTENGF) is successfully assembled for smart human‐machine interaction applications. Benefiting from their mechanoluminescent‐triboelectric synergistic effect, the resulting MLTENGF demonstrates prominent electro‐optical signals in response to mechanical stimulus in amphibious environments. Furthermore, such MLTENGF possesses remarkable non‐contact capability with a detection distance of as high as 35 cm.

Mechanoluminescent (ML) materials that directly convert mechanical energy into photon emission have emerged as promising candidates for various applications. Despite the recent advances in the development of both novel and conventional ML materials, the limited access to ML materials that simultaneously have the attributes of high brightness, low cost, self‐recovery, and stability, and the lack of appropriate designs for constructing ML devices represent significant challenges that remain to be addressed to boost the practical application of ML materials. Herein, ML hybrids derived from a natural source, waste eggshell, with the aforementioned attributes are demonstrated. The introduction of the eggshell not only enables the preparation of the hybrid in a simple and cost‐effective manner but also contributes to the homochromatism (red, green, or blue emission), high brightness, and robustness of the resultant ML hybrids. The significant properties of the ML hybrids, together with the proposed structural design, such as porosity or core–shell structure, could expedite a series of mechanic‐optical applications, including the self‐luminous shoes for the conversion of human motions into light and light generators that efficiently harvest water wave energy. The fascinating properties, versatile designs, and the efficient protocol of “turning waste into treasure” of the ML hybrids represent significant advances in ML materials, promising a leap to the practical applications of this flouring material family. Mechanoluminescence materials, converting the mechanical energy into photons directly, emerge to be of broad interest in the materials community. Here, the conversion of waste shells to high‐performance mechanoluminescence hybrids in a simple, eco‐friendly, and cost‐efficient manner is reported, enabling the versatile applications of mechanoluminescence materials in sensing, displaying, and energy harvesting.

To break through the bottleneck in the mapping of the mechanoluminescent (ML) intensity field to the strain field, a quantification method for full-field strain measurement based on pixel-level data fusion is proposed, integrating ML imaging with digital image correlation (DIC) to achieve precise reconstruction of the strain field. Experiments are conducted using aluminum alloy specimens coated with ML film sensor on their surfaces. During the tensile process, ML images of the films and speckle images of the specimen backsides are simultaneously acquired. Combined with DIC technology, high-precision full-field strain distributions are obtained. Through spatial registration and region matching algorithms, a quantitative calibration model between ML intensity and DIC strain is established. The research results indicate that the ML intensity and DIC strain exhibit a significant linear correlation (R2 = 0.92). To verify the universality of the model, aluminum alloy notched specimen tests show that the reconstructed strain field is in good agreement with the DIC and finite element analysis results, with an average relative error of 0.23%. This method enables full-field, non-contact conversion of ML signals into strain distributions with high spatial resolution, providing a quantitative basis for studying ML response mechanisms under complex loading.

Mechanoluminescent (ML) materials emit light by trapping and releasing charge carriers under mechanical stress. However, previous studies do not fully reveal the relationship between emitting light intensity and mechanical stress, thereby affecting the accuracy of stress measurement. This study addresses this gap by systematically investigating ML cylinders with various sizes and loading paths using theoretical analysis and simulations, focusing on the maximum contact stress, equivalent stress distribution, and the relationship between the strain energy density and light intensity at the point of maximum contact stress. In combination with experiments, the mechanical behavior and optical responses of ML cylinders under normal compressive forces reveal that the luminescence intensity is closely related to cylinder size and loading path, effectively reflecting stress distributions in objects of different sizes under complex stress conditions. Particularly, within the elastic range and under ideal conditions where lateral stress is ignored, the maximum contact stress is nearly equal to the equivalent stress. The equivalent stress is linearly related to the light intensity, while the strain energy density at the maximum contact stress point is proportional to the square root of the light intensity. This work promotes the application of ML materials in structural health monitoring and stress visualization.

Strain sensors that can work sustainably and continuously without any external power supply are highly desirable for future wearable and implantable devices. Herein, a self‐powered stretchable strain sensor based on the integration of mechanoluminescent phosphors with an elastomer optical fiber is proposed and developed. This mechanoluminescent optical fiber is capable of emitting light just driven by external strain, without the need of an external light source or electric power. The strain‐induced emitted light can be collected and guided along the mechanoluminescent optical fiber. The sensor exhibits linear strain response up to 50% and high‐accuracy strain measurement (±1%). Moreover, this optical fiber strain sensor displays consistent signals over 10 000 stretch–release motion cycles, which demonstrates the good durability of the sensor. Due to the excellent light confinement of the elastomer optical fiber, this strain sensor is demonstrated in both bright‐ and dark‐field measurements, wearable gloves, and an implantable sensing device, thereby demonstrating potential as a promising technology for future self‐powered optical sensor systems. A self‐powered stretchable mechanoluminescent optical fiber for strain sensing is shown. When the sensing section of the fiber is stretched, without the need of an external light source for irradiation, this fiber is capable of converting the mechanical stimuli into visible light emission, and the strain‐induced light signal can be collected and guided along the optical fiber for measurements.

The mechanoluminescent (ML) technology that is being developed as a new and substitutive technology for structural health monitoring systems (SHMS) comprises stress/strain sensing micro-/nanoparticles embedded in a suitable binder, digital imaging system, and digital image processing techniques. The potential of ML technology to reveal the fracture process zone (FPZ) that is commonly found in structural materials like concrete and to calculate the stress intensity factor (SIF) of concrete, which are crucial for SHMS, has never been done before. Therefore, the potential of ML technology to measure the length of the FPZ and to calculate the SIF has been demonstrated in this work by considering a single-edge notched bend (SENB) test of the concrete structures. The image segmentation approach based on the histogram of an ML image as well the skeletonization of an ML image have been introduced in this work to facilitate the measurement of the length of ML pattern, crack, and FPZ. The results show ML technology has the potential to determine fracture toughness, to visualize FPZ and cracks, and to measure their lengths in structural material like concrete, which makes it applicable to structural health monitoring systems (SHMS) to characterize the structural integrity of structures.

The ability to locate and quantify large strains will significantly improve the real‐world application scenario of flexible and stretchable strain sensors. However, current methods for implementing stretchable distributed strain sensing still face challenges such as complicated demodulation, multisensor crosstalk, and high power consumption. Herein, a self‐powered and stretchable optical fiber strain sensor is reported with distributed sensing capability based on mechanoluminescent optical fiber, where mechanoluminescent phosphors with different emission color light are discretely integrated onto the outer cladding of the elastomer optical fiber. Based on the wavelength coding technique and time‐domain filtering comparison method, the capability of strain magnitude quantification (10–60%) and strain location identification together in a single stretchable optical fiber is successfully realized, even at multiple positions simultaneously in the strain‐applied situation. Moreover, this stretchable optical fiber strain sensor shows insensitivity to bending, compression, and temperature disturbances and outstanding durability (>8000 cycles). Due to the excellent light confinement of the elastomer optical fiber, demonstrations such as bright‐field measurement, saline water operation, and wearable glove application exhibit its potential as a promising technology for future self‐powered distributed optical sensing systems.