High-Precision Sign Language Recognition Enabled by Self-Recoverable Near-Infrared Mechanoluminescent Materials

High-Precision Sign Language Recognition Enabled by Self-Recoverable Near-Infrared Mechanoluminescent Materials | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article High-Precision Sign Language Recognition Enabled by Self-Recoverable Near-Infrared Mechanoluminescent Materials Panlai Li, Xue Meng, Mingxin Zhou, Jinlong Wang, Guodong Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8664424/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Intelligent evolution of human-machine interaction technology demands core capabilities in flexible sensors, including high stability, anti-interference properties, and self-powering. Traditional electrical sensors usually struggle to adapt to complex and long-term application scenarios. Mechanoluminescence (ML) materials offer a novel solution to this challenge, while existing ML materials still face issues such as the requirement for pre-radiation charging and insufficient cycling stability. Herein, this work developed a series of self-recoverable near-infrared (NIR) ML materials - ZnGa 1 − m Al m InO 4 :Cr 3+ , which possess excellent piezoelectric properties, low cost and biocompatibility. By adjusting the doping concentration of Al 3+ ions, the crystal field strength of the material was precisely controlled, resulting in a 40.65-fold increase in photoluminescence intensity. Even after undergoing thousands of cycles of mechanical stimulation, the self-recoverable NIR ML material can still maintain 98% of its initial luminescence intensity. When integrated with photoelectric sensors, ZAIO:Cr 3+ @PDMS demonstrated outstanding performance in sign language recognition (achieving 99.46% accuracy) and intelligent highway monitoring through convolutional neural networks. This work provides novel insights for designing NIR ML materials and lays the foundation for integrating ML materials with intelligent neural networks. Physical sciences/Optics and photonics/Optical materials and structures Physical sciences/Optics and photonics/Optical techniques Phosphor Mechanoluminescence Intelligent neural networks Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction With the rapid development of artificial intelligence and the internet of things technologies, human-machine interaction (HMI) has emerged as a core technology driving the intelligent transformation of multiple fields, including wearable devices, industrial robots, virtual reality, and barrier-free communication. 1 – 3 So far, the mainstream hand motion capture technologies for HMI can be broadly categorized into two categories. The first category relies on visual sensing 4 , 5 or inertial measurement units (IMU). 6 , 7 These technologies feature high data transmission rates, but the visual methods are susceptible to occlusion and ambient light interference. In addition, IMU-based systems require the integration of multiple sensing modules and impose stringent demands on data processing capabilities, resulting in poor portability and high power consumption. 8 Among the flexible sensors adopted in the second category, 9, 10 traditional electrical sensors (resistive, 11 capacitive, 12 and piezoelectric types 13 ) are prone to electromagnetic interference in complex industrial or daily environments, leading to signal distortion (Fig. 1 ). By contrast, mechanoluminescent (ML) sensors transmit signals through optical channels, which can effectively avoid electromagnetic interference and ensure stable operation in electromagnetic-intensive scenarios. 14 , 15 Moreover, ML material-based sensors can achieve self-powering via the direct conversion of mechanical energy into light energy, eliminating the need for external power supplies or pre-irradiation charging. 16 , 17 Additionally, ML materials can be combined with a wide range of flexible substrates and integrated with photoelectric sensors (PES) for distributed pressure detection. These unique advantages render ML sensors highly compatible with the current requirements for HMI systems that demand low-power consumption, portability, and high adaptability. Since the ML phenomenon was first observed in the 17th century, extensive research has yielded numerous high-performance materials, such as SrAl 2 O 4 :Eu 2+ , Dy 3+ , ZnS:Mn 2+ , and CaZnOS:Mn 2+ . 18–20 However, most early-stage mechanoluminescent (ML) materials were defect-controlled types, which required energy to be stored in trap states via pre-irradiation charging. 21 , 22 This characteristic has severely restricted their applications in real-time and continuous human-machine interaction (HMI) scenarios. Recently, self-recoverable ML materials based on the piezoelectric effect have become a research hotspot. 23 Such materials can maintain stable luminescence intensity under cyclic mechanical stimulation without the need for pre-irradiation. For example, Xiong et al. reported a Cr 3+ -doped ML material, which exhibits a peak emission at 716 nm with a full width at half maximum (FWHM) of 40 nm, and possesses self-recoverable properties in the deep red to near-infrared (NIR) range. 24 Subsequently, many researchers successfully developed a series of Cr 3+ ion-activated NIR ML materials through screening of persistent luminescent phosphors. 25 , 26 Lu 3 Al 5 O 12 :Cr 3+ is also a NIR ML material with piezoelectric properties, and exhibits a NIR emission peaking at 700 nm. Introducing Ga 3+ ions via a chemical co-substitution strategy modulates the crystal field strength and defect distribution, enabling piezoelectric-induced ML luminescence. 27 The advent of these self-recoverable ML materials has laid a critical material foundation for developing a new generation of high-performance flexible sensing systems. However, how to fully integrate their inherent advantages into and revolutionize the existing sensing technology architectures remains a key challenge to be further explored. To address the limitations of existing HMI sensing technologies and further enhance the performance of flexible ML sensors, this study develops a series of self-recoverable NIR ML materials, namely ZnGa 1 − m Al m InO 4 :Cr 3+ , which exhibit excellent piezoelectric properties, low cost and biocompatibility. The luminescence performance is optimized by regulating the host composition and defect engineering. The piezoelectric effect induces local electric fields within the material, which can alter the band structure and trap depths, facilitating the detrapping of electrons and their subsequent recombination with holes to produce light emission. The interaction between the piezoelectric field and the crystal field strength can regulate the energy level of Cr 3+ ions, thereby influencing the NIR luminescence. This paper will systematically examine the luminescence characteristics of Cr 3+ ions across various matrices, correlates the luminescence and ML performance with the piezoelectric effect, crystal field strength and defect distribution, and experimentally verifies the proposed piezoelectric-induced ML mechanism. Ultimately, this study further explored the practical application potential of this material in the field of stress monitoring. By integrating it with PES and convolutional neural networks (CNN), it provides solid theoretical support and reliable experimental basis for the development of advanced sensing systems with high sensitivity and precise intelligent recognition capabilities. 2. Results and discussion The crystal structure of ZnGaInO 4 (ZGIO) belongs to the orthorhombic system, with a space group of R-3m. In this structure, metal ions form complex coordination environments with O 2− ions, featuring both pentacoordinated and hexacoordinated sites. By systematically replacing Ga 3+ ions with Al 3+ ions in ZGIO, ZnAlInO 4 (ZAIO) was successfully synthesized. This compound maintains an orthorhombic crystal structure, akin to that of ZGIO. However, the introduction of Al 3+ induces subtle changes in lattice parameters and local structure due to the smaller ionic radius of Al 3+ (0.48 Å) compared to Ga 3+ (0.55 Å). This substitution leads to lattice contraction, as the smaller Al 3+ ions occupy the metal ion sites (Table S1 ). Additionally, the higher electronegativity of Al 3+ compared to Ga 3+ results in stronger charge attraction to surrounding oxygen ions, altering the crystal field symmetry and strength. These modifications significantly influence the electronic transition energy levels, as illustrated in Figure S1 . ZGIO:Cr 3+ and ZAIO:Cr 3+ were synthesized through a high-temperature solid-state reaction at 1450 ℃. The raw materials were mixed in a chemical stoichiometric ratio and then placed in an alumina crucible, which was maintained at this temperature for 4 hours. Their ML properties enable their application in various fields, such as sign language recognition and intelligent highways. The crystal structure and phase purity of the synthesized materials were investigated. Figures 2 A and S2 present the XRD patterns of ZGIO: x Cr 3+ and ZnGa 1 − m Al m InO 4 :0.04Cr 3+ , respectively. Both patterns exhibit well-defined diffraction peaks that closely match the standard reference data, confirming the high phase purity and excellent crystallinity of the synthesized materials. When smaller ions are incorporated into the crystal lattice, the lattice will contract, resulting in the diffraction peaks shifting to larger angles. Notably, as the Al 3+ content increases, the main peak position of the diffraction pattern gradually shifts towards smaller angles, indicating that the material has transformed from ZGIO to ZAIO (Figure S2B). This shift demonstrates the successful incorporation of Al 3+ into the lattice and the formation of a solid solution. To further verify its purity, the Rietveld refinement was carried out on the ZGIO: x Cr 3+ and ZnGa 1 − m Al m InO 4 :0.04Cr 3+ phosphors and the refinement results were all within the confidence range (Fig. 2 B and Figure S3-S4). Table S2 provides detailed crystal cell parameters. As the concentration of Cr 3+ increases, the crystal cell parameters show a decreasing trend, which is consistent with the lattice contraction caused by the difference in ionic radii. As the content of Al 3+ ions increases, the crystal cell parameters (a, b, c) and the crystal cell volume (V) of the refined sample gradually decrease (Fig. 2 C and Table S3). This is because the smaller Al 3+ ions (CN = 5, 0.48 Å) gradually replace the Ga 3+ ions (CN = 5, 0.55 Å). As shown in Fig. 2 D, the scanning electron microscope (SEM) image reveals a uniform distribution of particles, with an average size of approximately 5 micrometers, and all constituent elements are uniformly distributed throughout the sample. This uniform particle morphology is beneficial for enhancing the optical properties of the material, as it can reduce light scattering and promote efficient luminescence. The surface elemental composition and chemical states of the materials were analyzed using X-ray photoelectron spectroscopy (XPS). Figures S5A and S5B display the XPS survey spectra of ZGIO:Cr 3+ and ZAIO:Cr 3+ , respectively, providing comprehensive information on the elemental composition of the material surfaces. To further clarify the chemical environments of Cr 3+ ions in these two matrices, Figs. 2 E and 2 F display the XPS spectra of Cr 2p 3/2 and Cr 2p 1/2 , respectively. The Cr 2p 3/2 peaks of ZnGaInO 4 :Cr 3+ and ZnAlInO 4 :Cr 3+ are located at 576 eV and 577 eV, while the Cr 2p 1/2 peaks are situated at 586 eV and 587 eV, respectively. In this case, the smaller ionic radius of Al 3+ ions may lead to an increase in the crystal field strength around Cr 3+ ions, thereby enhancing the binding energy of Cr 2p electrons. 28 For ZnGa 0.9 Al 0.1 InO 4 :Cr 3+ , the Ga 3d 5/2 and Ga 3d 3/2 peaks are at 20.4 eV and 18.4 eV, respectively, and the Al 2p peak is at 74.9 eV (Figs. 2 G and 2 H). These results indicate that adjusting the composition of the material can modify the electronic structure and energy bands, thereby influencing their optical and electrical properties. This further verifies the substitution of Ga 3+ by Al 3+ in the lattice. As presented in Fig. 2 I, low-wavenumber peaks corresponding to the stretching and bending vibrations of Zn-O bonds reflect the local structure of ZnO and the vibrational environment of Zn in the crystal lattice. For example, the peak around 200 cm − 1 is related to the bending vibration mode of Zn-O bonds. Ga and Al ions have similar ionic radii and chemical properties, and they often occupy similar sites in the lattice. The vibrational peaks of the bonds they form with O (Ga-O, Al-O) are difficult to completely distinguish and appear in the medium-low wavenumber range (such as 300–600 cm − 1 ). The peak values of the vibration mode of the In-O bond usually occur in the mid-wave number region (such as 600–800 cm − 1 ) and may have characteristic peaks, which reflect the vibration coordination structure of indium in the crystal lattice. 29 Subsequently, the interlayer spacing in ZnGa 0.9 Al 0.1 InO 4 were further observed through high-resolution transmission electron microscopy (HRTEM) images. As displayed in Fig. 2 J, lattice fringes can be clearly observed in ZnGa 0.9 Al 0.1 InO 4 . Among them, the interlayer spacing of approximately 0.419 nm can be attributed to the (006) plane of ZnGa 0.9 Al 0.1 InO 4 . Figure S6A presents the excitation spectra of ZGIO: x Cr 3+ monitored at the emission peak of 797 nm, which consists of three broad excitation bands with peak positions located at 352 nm, 416 nm, and 581 nm, respectively. These peaks correspond to the transition absorptions of the d-electron energy levels of Cr 3+ ions, namely 4 A 2 → 4 T 1 ( 2 E), 4 A 2 → 4 T 1 ( 4 F), and 4 A 2 → 4 T 2 ( 4 F). 30 Accordingly, Figure S6A displays the PL spectra of ZGIO: x Cr 3+ exhibit a broadband emission in the range of 600–1100 nm, which is attributed to the 4 T 2 → 4 A 2 transition of Cr 3+ ions. As the doping concentration of Cr 3+ ions increases, the emission intensity first increases and then decreases (Figure S6B). Figure S6C illustrates the diffuse reflectance spectra at different Cr 3+ doping concentrations, where three distinct absorption peaks align with the excitation bands in the excitation spectrum, further confirming the energy level transition characteristics of Cr 3+ . Figure S6D illustrates the variation in decay curves at different Cr 3+ doping concentrations. As the Cr 3+ concentration increases, the lifetime progressively shortens. This can be attributed to the larger interatomic spacing of Cr 3+ ions, which weakens the intensity of non-radiative transitions. However, as the Cr 3+ concentration rises, the number of interionic non-radiative transition pathways gradually increases, accelerating the relaxation rate of excited-state electrons and ultimately leading to a sustained decrease in lifetime. 31 Thermoluminescence (TL) analysis depicted in Figures S6E and S6F indicates that an increase in the Cr 3+ ion doping concentration is significantly positively correlated with the increase in trap depth and the enhancement of emission spectral intensity. This suggests that Cr 3+ ions exhibit higher efficiency in energy capture and release within the material, thereby enhancing its luminescence performance. As depicted in Figure S7, with the increase of Cr 3+ ion concentration, the ML intensity exhibits a gradually enhanced trend, and reach its maximum intensity at a Cr 3+ concentration of 0.04, which is consistent with the optimal concentration observed in the photoluminescence spectra. Therefore, the optimized ZGIO:0.04Cr 3+ has a higher probability of energy level transition, enabling it to exhibit intense luminescence under external force. Figure S8A illustrates the excitation and emission spectra of Cr 3+ -doped ZGIO phosphors at varying concentrations of Al 3+ ion doping. With the increase of Al 3+ ion concentration, the excitation peak exhibit a redshift from 316 nm to 553 nm, while the emission peak is blueshifted. This phenomenon indicates that the introduction of Al 3+ ions significantly alters the crystal field environment of the material, thereby affecting the energy level structure of Cr 3+ ions. Specifically, the substitution of Al 3+ ions for Ga 3+ ions will increase the strength of the crystal field, causing a shift in the energy levels of Cr 3+ ions, and subsequently resulting in a deviation in the position of the transition peaks. Furthermore, as the concentration of Al 3+ ions increases, the type of energy level transition for Cr 3+ ions changes from 4 T 2 → 4 A 2 to 2 E→ 4 A 2 , further confirming the regulatory effect of Al 3+ ions on the energy level structure of Cr 3+ ions. These findings provide an important theoretical basis for controlling the optical properties of Cr 3+ doped phosphors to achieve low-energy excitation and high-efficiency emission. When the Al 3+ ion concentration reaches 0.10, the emission spectrum intensity reaches a maximum value of 12.18-fold (Figure S8B). Meanwhile, the peak wavelength of the emission spectrum shifted from 797 nm to a shorter wavelength region at 703 nm (Figure S9), and the emission intensity showed a consistently increasing trend (Fig. 3 A). Figure S10 presents contour plots of the wavelength-resolved photoluminescence spectra for ZGIO:Cr 3+ and ZAIO:Cr 3+ . The spectrum of ZGIO:Cr 3+ exhibits broad-bandwidth and relatively dispersed excitation-emission signals, attributed to the weak crystal field effect and high lattice distortion of the ZGIO matrix, leading to the dominance of the broad 4 T 2 → 4 A 2 transition of Cr 3+ (Figure S10A). In contrast, after Al 3+ doping (Figure S10B), the excitation-emission signals evolve into localized, periodically distributed peaks. This is because the smaller ionic radius of Al 3+ reduces lattice distortion and enhances crystal field strength, which promotes a shift in the ground excited state of Cr 3+ ions from 4 T 2 to 2 E, dominating the 2 E→ 4 A 2 peak transition. When all Ga 3+ ions were replaced by Al 3+ , the emission spectrum intensity reached its maximum, increasing to 40.65-fold of the original value (Fig. 3 B).To gain a deeper understanding of the phenomenon where the spectral emission spectrum shifts from broad peak emission to sharp peak emission, the changes in bond angles in the refined statistical results were analyzed, and the distortion degree of the unit cell was calculated (Fig. 3 C). When the content of Al 3+ ions increased, the variance of bond angles in the unit cell decreased, and the distribution of electrons in the energy band became more concentrated and orderly, thereby leading to sharp peak emission. The introduction of Al 3+ ions may enhance the stability of the lattice and reduce the influence of lattice vibrations on electron transitions, resulting in sharp peak emission. Furthermore, in ZGIO: x Cr 3+ materials, as Ga ions are progressively replaced by Al 3+ ions, the energy level transitions of Cr 3+ ions undergo significant changes. Specifically, the transition of Cr 3+ changes from 4 T 2 → 4 A 2 to 2 E→ 4 A 2 . This transition is primarily ascribed to the introduction of Al 3+ , which enhances the crystal field strength, leading to an increased splitting of the Cr 3+ d-orbital energy levels and rendering the 2 E state as the lowest excited state. Such changes in energy level transitions not only significantly affect the luminescence characteristics of the material but also have important implications for its optical properties and potential applications (Fig. 3 D). Figure 3 E presents the diffuse reflectance spectra of the phosphors in the wavelength range of 240–780 nm as the Al 3+ ion concentration increases from 0 to 1.00. In Figure S11, the excitation peaks of ZGIO:0.04Cr 3+ and ZAIO:0.04Cr 3+ match well with the absorption peaks in the diffuse reflection spectra. With the increase of Al 3+ concentration, the absorption peak corresponding to the 4 A 2 → 4 T 2 transition is significantly enhanced, likely due to the introduction of Al 3+ ions altering the crystal field environment and thus affecting the energy level structure and optical properties of Cr 3+ ions (Fig. 3 F). To elucidate the underlying mechanism of the transformation from a broad emission peak to a sharp emission peak induced by Al 3+ doping in the material, systematic theoretical calculations were analyzed. The results showed that the band gap of the material was expanded from 1.08 eV of ZGIO to 1.74 eV of ZAlO (Figure S12A and S12B). This change was attributed to the smaller ionic radius of Al 3+ , which could enhance the crystal field strength, improve the lattice symmetry, and suppress lattice distortion, thereby raising the electron energy level and significantly reducing the contribution of Al-related orbitals to the conduction band (Figure S12C and S12D). The broadening of the band gap directly led to the narrowing of the emission spectrum, providing a theoretical basis for the regulation of the material's luminescence performance. Figure 3 G displays the trend of changes in fluorescence lifetime as the Al 3+ ion concentration increases from 0 to 1.00. Figure 3 H demonstrates that the lifetime value significantly increases from 0.153 ms to 1.76 ms as the concentration of Al 3+ ions increases. This can be attributed to the addition of Al 3+ ions, which changed the weak crystal field environment to a strong one. This effect led to a change in the electronic energy level structure of Cr 3+ ions, which in turn influenced the dynamic process of electron capture and release. Eventually, this results in an increase in fluorescence lifetime. 29 Additionally, the Al 3+ concentration increases, both the peak position and intensity of the TL spectra undergo significant changes, indicating that the trap depth and trap density gradually increase (Figs. 3 I and S13). This change is due to the introduction of additional defect states or alterations in the energy level distribution of existing defect states by the doping of Al 3+ ions, thereby affecting the behavior of carrier capture and release, leading to changes in trap characteristics. Figure S14 displays the emission spectra under different excitation powers, and it can be observed that there is only a change in the photoluminescence intensity. The d 3 electrons of Cr 3+ mainly determine the relative positions of 2 E and 4 T 2 in the octahedral field through the crystal field parameter Dq/B. As long as the crystal field strength remains unchanged, the energy level intervals of these two levels are fixed. Therefore, the peak positions of the R line ( 2 E→ 4 A 2 ) or the broadband ( 4 T 2 → 4 A 2 ) hardly drift with the pump power. Key performance indicators for evaluating phosphor materials are thermal stability and quantum efficiency. 32 Figure S15 displays the temperature-dependent emission spectra of ZAIO:Cr 3+ and ZGIO:Cr 3+ . As the temperature is raised from 298 K to 473 K, thermal quenching associated with non-radiative transitions causes the integrated emission intensities of ZAIO:Cr 3+ and ZGIO:Cr 3+ to remain at 92.67% and 69.53% of their room-temperature values at 423 K, respectively. 33 As shown in Figure S16, the internal quantum efficiencies (IQE) of ZAIO:Cr 3+ and ZGIO:Cr 3+ are 98.15% and 26.94%, respectively. After replacing Ga 3+ with Al 3+ , the quantum efficiency and thermal stability of the sample were significantly improved. This was because the radius of Al 3+ was smaller and the ligand bond energy was higher. This not only reduced the distortion degree of the octahedron where Cr 3+ was located, enhanced the symmetry of the coordination field, but also strengthened the lattice rigidity, significantly suppressing non-radiative transitions. 34 As a result, both the quantum efficiency and thermal stability were simultaneously improved. Moreover, compared with currently available broadband NIR phosphors, ZAIO:Cr 3+ exhibits superior thermal stability and quantum efficiency (Table S4). As shown in Fig. 4 A, we fabricated a custom-made device to investigate the ML properties, which includes a NIR camera, an optical spectrometer, and a mechanical control unit. Figure 4 B illustrates the ML spectra of ZnGa 1 − m Al m InO 4 :0.04Cr 3+ phosphors with Al 3+ ion incorporation under a fixed external force (F = 20 N). The doping of Al 3+ ions can modulate the piezoelectric properties of the material, thereby enhancing the local piezoelectric field and promoting the increase in trap depth and trap density. This is in complete agreement with the situation where the ML strength has increased. Figure 4 C exhibits the variation in the stress-induced luminescence intensity produced by the ZAIO:Cr 3+ phosphor under different external forces. As the external force increases, the ML intensity shows a significant enhancement trend. The application of external force leads to the generation of more defect states in the phosphor, which can serve as recombination centers for electrons and holes, thereby enhancing the ML response. In addition, the lattice distortion induced by stress alters the crystal-field environment around Cr 3+ ions, thereby affecting their energy-level structure and luminescence properties. 35 After UV lamp charging and thermal treatment, the ML intensity remains almost unchanged, indicating that the energy stored in the trap states can be emptied (Fig. 4 D). Through the free-fall ball experiment, a metal ball with a mass of 100 grams was dropped freely from different heights (10–60 centimeters) and collided with the powder sample. The influence of impact force on the mechanical luminescence spectrum of the sample was studied (Figure S17), and ML luminescence photos of the sample after being hit by the free-fall were taken (Figure S18). The positive correlation between ML intensity and the falling height confirmed the elastic mechanical stretching of ZAIO:Cr 3+ (Figure S17C), making it an ideal candidate material for a stress sensor. Furthermore, the time stability test revealed that the decrease in the ZAIO:Cr 3+ phosphor after 14 days was almost negligible (Figure S19A-B). Self-recoverable ML properties, as a key performance indicator of advanced ML materials, are defined by the ability to maintain stable luminescence intensity and dynamic response during cyclic mechanical stimulation, while eliminating the reliance on external pre-irradiation (such as ultraviolet or visible light activation) to supplement luminescent carriers. Therefore, for ZAIO:Cr 3+ system, the trap-mediated ML model widely reported in the literature can be ruled out. The ML behavior is attributed to the local piezoelectric effect, which is widely recognized as the origin of self-recoverable ML. To systematically investigate the regulatory effect of trap states on the ML intensity of materials, ZGIO:Cr 3+ and ZAIO:Cr 3+ phosphors were annealed at 300 ℃ for 20 minutes. The ML intensity and TL curves of both samples were measured before and after heat treatment for comparison. As shown in Figs. 4 E and S20, the TL intensity decreased sharply after heating, confirming the emptying of traps. Notably, the ML intensity remained nearly unchanged, demonstrating that traps exert negligible influence on the ML intensity. Comprehensive analysis indicates that the ML behavior should be attributed to the piezoelectric effect, a conclusion that has gained widespread recognition in the academic community. 17 In ZGIO:Cr 3+ and ZAIO:Cr 3+ , the non-uniform distribution of electrons gives rise to localized piezoelectric fields, thereby enhancing charge separation and recombination. Strain-induced piezoelectric fields can modulate and accelerate the directed migration of electrons, which include electrons generated by external forces as well as those produced through the triboelectric effect (charging of grain contact surfaces during friction). These electrons can transfer energy to the matrix, which is then transferred to the dopants via non-radiative transitions, alternatively, electrons can directly transfer energy to the dopants through collisions. Consequently, the activated dopants emit fluorescence and return to the ground state. Under continuous external force, piezoelectric materials can generate a high concentration of electrons, which is why persistent luminescence can still be observed even if carriers are fully detrapped. As depicted in Fig. 4 F, the local piezoelectric response of ZAIO:Cr 3+ was measured using piezoelectric force microscopy under a tip bias of G10 V. The observed amplitude butterfly-shaped hysteresis loops strongly confirmed its excellent piezoelectric properties, which were highly consistent with the reported theoretical results. Figure 4 G illustrates a schematic of a local piezoelectric-induced ML mechanism. 36 , 37 When mechanical stimuli (such as pressure or strain) are applied to the material, a piezoelectric effect will be generated inside the material, leading to the redistribution of charges between the conduction band and the valence band. 38 This charge transfer can cause electrons to transition from the valence band to the conduction band, forming electron-hole pairs. Under the action of the local piezoelectric field, the energy level structure of Cr 3+ ions shifts, resulting in a change in their energy level positions. 39 When electrons transition back from the conduction band to the valence band, they may be captured by Cr 3+ ions and enter their specific energy levels. Subsequently, these electrons undergo transitions between the energy levels of Cr 3+ ions and emit photons, thus producing ML. In addition, during the charging process, electrons and holes recombine between the energy levels of Cr 3+ ions, releasing energy. This energy release is manifested in the form of light, that is, ML. 40 The detailed ML peak wavelengths and FWHM values of these Cr 3+ -doped NIR phosphors are presented in Fig. 4 H and Table S4. Among them, the optimal emission wavelength of ZGIO:Cr 3+ phosphor is 797 nm, and its FWHM value is 159.2 nm. The primary peak position of the ML of the ZAIO:Cr 3+ phosphor is located at 703 nm. The powder samples were ground using a glass tube, and the motion trajectories resulting from the applied stress were captured by a near-infrared camera (Figure S21 and Video 1). The gray values of the NIR-ML images were extracted, and the spatial distribution of ML intensity at different trajectory points was clearly displayed. These results indicate that NIR-ML has significant advantages in obtaining stress information, and the detection flexibility and sensitivity of the samples (ZGIO:Cr 3+ and ZAIO:Cr 3+ ) before and after regulation have both been improved. When these unexcited powders were directly ground in a quartz mortar or scratched in different organic matrices (such as PET, PDMS and PU), they all exhibited bright NIR emission. This indicates that the ML emission is independent of the type of organic matrix, and the frictional luminescence has little effect on the ML properties of this series of samples (Figure S22). As depicted in Figs. 4 I and S23, due to the soft texture of PDMS being more suitable for practical applications, the ZAIO:Cr 3+ @PDMS film exhibited a bright NIR-ML luminescence phenomenon during pressing, stretching, tearing, folding, and rubbing operations, due to the frictional contact between the organic matrix and the ML powder. Figure 5 presents the overall architecture, signal response characteristics, and recognition performance of the developed sign language recognition system. 41 Specifically, Fig. 5 A shows the hardware integration schematic: a glove embedded with 5-channel flexible sensors (ZAIO:Cr 3+ @PDMS and PES) acts as the signal acquisition front-end. Current signals from the sensors are converted to analog voltage signals via signal conditioning modules (transimpedance amplifier, multiplexer), transmitted to the STM32 microcontroller through photoelectric conversion and an analog-to-digital conversion interface, and finally uploaded to a terminal device via a Wi-Fi module for algorithm-based signal recognition. First, cyclic tests demonstrated negligible variations in voltage signals collected during 25 repeated bends of the thumb at the same angle. Second, anti-interference tests confirmed minimal differences in voltage signals from 10 repeated thumb bends at the same angle under ambient light and humid conditions (Figure S24). Figure 5 B (index finger) and Figure S25 display voltage response curves of the glove sensors at different finger bending angles (30°, 60°, 90°), revealing distinguishable peak intensities and intensity ratios of characteristic signals for each motion across the five fingers. Figure 5 C illustrates the temporal signal distribution of 10 sign language motions (corresponding to semantics such as "Good", "Light", and "Love"), with distinct waveform differences providing a robust feature basis for machine learning. Figure 5 D and Video 2 demonstrate the actual wearing demonstration of the system, verifying its adaptability in dynamic scenarios. Figure 5 E and S26 illustrate the neural network workflow for sign language motion classification: 5-channel temporal signals are input, feature extraction is performed via convolution-pooling operations, features are flattened into a one-dimensional vector through a Flatten layer, and the vector is finally fed into a fully connected classifier to output recognition results for 10 target sign language motions. 42 Training curves in Fig. 5 F indicate that the model achieved 98.64% accuracy on the training set and 99.46% accuracy on the validation set after 50 epochs, demonstrating excellent fitting and generalization capabilities. The confusion matrix in Fig. 5 G further validates the system’s reliability in recognizing target sign language motions, with high recognition accuracy across all motion categories. By integrating optical tactile sensing with neural networks, this system enables efficient perception and accurate recognition of sign language motions, offering a viable solution for practical applications in accessible communication. With the surge in traffic flow and the improvement in vehicle intelligence, traditional road surfaces are no longer able to meet the development needs of "functional infrastructure". Intelligent road surfaces can generate multiple derivative values. Figure S27A depicts the synthesis and workflow of intelligent road surfaces. First, powder and PDMS are combined to prepare intelligent road surfaces with integrated ZAIO:Cr 3+ @PDMS luminescent functional layers. When the tire interacts with the road surface dynamically, the mechanical stimulation at the interface triggers the NIR-ML signal of the functional layer and transmits it to the upper computer for feature analysis. This process realizes the cross-domain conversion of "mechanical stimulation - optical signal - information output", providing a hardware foundation for in-situ monitoring of the tire condition of automobiles. Figure S27B illustrates the digital encoding logic of the signal, converting the ML signal into digital signals through PES, and marking the states of the "Front/Back/Left/Right" four-dimensional space nodes (red nodes represent abnormal features) to achieve digital characterization of the five types of tire conditions (no fault, high/low tire pressure, left/right side bias angle). This encoding rule can directly connect to the on-board terminal, providing intuitive status feedback to the driver. Figure S26C demonstrates the discrimination method based on image features. By using the texture, size and shape features of the luminescent image, different mechanical action traces of tire states can be directly distinguished. This image recognition strategy does not require complex signal processing and can achieve rapid classification of conditions. Combining digital signals and images for machine learning can build an efficient, safe and collaborative modern transportation system. Therefore, self-recoverable sign language recognition and intelligent road system have demonstrated great potential for application in the field of high-precision mechanical testing. 4. Conclusions To meet the core requirements of human-machine interaction for flexible sensors in terms of stability, anti-interference capability, and self-powering, this study has successfully developed a high-performance self-recoverable NIR ML material. By controlling the doping concentration of Al 3+ , the crystal field environment of Cr 3+ ions can be adjusted, thereby increasing the NIR luminescence intensity by 40.65 times. More importantly, the prepared samples possess self-recovery mechanical luminescence properties. After 3000 cycles of mechanical stimulation, the luminescence intensity still retains 98% of the initial value, and no pre-irradiation treatment is required. Mechanism studies have confirmed that the ML characteristics in this material mainly result from the piezoelectric effect, rather than the traditional trap-controlled model. This piezoelectric-controlled ML material generates a local electric field under mechanical stimulation, promoting charge separation and recombination, thereby achieving efficient NIR luminescence. Given the above excellent performance, the ML material ZnAlInO 4 :Cr 3+ @PDMS is utilized through integrating an optical sensing system and a neural network to demonstrate its application potential in high-precision stress monitoring and intelligent sensing. This research expands the design strategy for high-performance NIR ML materials and provides a reliable solution for developing optical sensing systems and intelligent recognition. Further research will explore the performance of NIR ML materials in more practical scenarios and reveal their potential in driving the development of related fields. Declarations Conflict of interest There are no conflicts to declare. Acknowledgments The work is supported by the Higher Education Scientific Research Project of Hebei Province (No.JCZX2025025), the National Natural Science Foundation of China (No.51902080). References S. Lee, R. Peng, C. Wu, M. Li, Programmable black phosphorus image sensor for broadband optoelectronic edge computing, Nature communications , 2022, 13(1), 1485. H. Huang, S. Shi, J. Zha, Y. Xia, H. Wang, P. Yang, L. Zheng, S. Xu, W. Wang, Y. Ren, Y. Wang, Y. Chen, H. Chan, J. Ho, Y. Chai, Z. Wang, C. Tan, In-sensor compressing via programmable optoelectronic sensors based on van der Waals heterostructures for intelligent machine vision, Nature communications , 2025, 16(1), 3836. Z. Ye, S. Fang, T. Zhang, H. Cheng, J. Ou, J. Yu, Y. Zhuang, R. Xie. 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Lee, J.Y. Park, A breathable, reliable, and flexible siloxene incorporated porous sebs-based triboelectric nanogenerator for human–machine interactions, Advanced Energy Materials , 2023, 14(6), 2302471. X. Wang, C. Lu, Z. Jiang, G. Shao, J. Cao, X.Y. Liu, Meso hybridized silk fibroin watchband for wearable biopotential sensing and ai gesture signaling, Advanced Science , 2024, 12(5), 2410702. Additional Declarations There is no conflict of interest Supplementary Files Supportinginformation.docx Supporting information Video1.mp4 Video 1 Video2.mp4 Video 2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8664424","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":580963483,"identity":"07a332fb-2a9b-438b-a6f1-456a56cdd996","order_by":0,"name":"Panlai Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACPmYwZcHAxt7Y+OADMVrYIFokGNh4DjcbziBKCwNUC4NEeps0B1Fa2BnYHvzcISHPJ/mwQZqBwU5Ot4Gww9gNe89IGLZJJzYYFzAkG5sdIKyFTYK3TYIRpCV5BsOBxG3EaJH82yZh3yZ5sOEwD7FapIG2JAItamwmVgu7sWybRHIbT2Iz4wwDIvzCz3+A7eHbNhvb+e3Hn//4UGEnR1ALUNM3JI4BQeUQtxGnbBSMglEwCkYuAACiiTUVhjKcxgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0972-9343","institution":"Hebei University","correspondingAuthor":true,"prefix":"","firstName":"Panlai","middleName":"","lastName":"Li","suffix":""},{"id":580963484,"identity":"a7f39410-f5b1-49a2-95e6-e2e2eaaa27f0","order_by":1,"name":"Xue Meng","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Meng","suffix":""},{"id":580963485,"identity":"bc819c69-a732-4f2d-8c9c-3853ebd277eb","order_by":2,"name":"Mingxin Zhou","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Mingxin","middleName":"","lastName":"Zhou","suffix":""},{"id":580963486,"identity":"3bf03149-aa11-4572-8a76-8680b0415b93","order_by":3,"name":"Jinlong Wang","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Jinlong","middleName":"","lastName":"Wang","suffix":""},{"id":580963487,"identity":"e7fa02bf-417b-4433-be24-111c6c9cdaa0","order_by":4,"name":"Guodong Zhang","email":"","orcid":"https://orcid.org/0009-0005-9417-0039","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Guodong","middleName":"","lastName":"Zhang","suffix":""},{"id":580963488,"identity":"1dd16263-2fbc-4892-98af-4919d0a7a8f8","order_by":5,"name":"Hao Suo","email":"","orcid":"https://orcid.org/0000-0002-6106-9402","institution":"City University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Suo","suffix":""},{"id":580963489,"identity":"a0b895ec-5e51-4bc1-8c40-b859bf7a64f8","order_by":6,"name":"Zhijun Wang","email":"","orcid":"","institution":"Hebei University","correspondingAuthor":false,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-01-22 02:26:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8664424/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8664424/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101451320,"identity":"6a38f51c-8521-406a-9c6f-5196910a28bb","added_by":"auto","created_at":"2026-01-29 20:30:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4217237,"visible":true,"origin":"","legend":"\u003cp\u003eFlexible sensors suitable for sign language recognition systems. (A) Diagram of the sign language recognition scenario. (B) Resistive sensor: High temperature drift, susceptible to electromagnetic interference. (C) Capacitive sensor: Significant influence from humidity, susceptible to electromagnetic interference. (D) Piezoelectric sensor: Reduced high-temperature performance, susceptible to electromagnetic interference. (E) ML sensor: Not affected by temperature, humidity, temperature drift, or electromagnetic interference. (F) Trap-controlled ML material: Requires pre-irradiation. Piezoelectric-induced ML material: No need for pre-irradiation and can withstand high temperatures.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/a9c3c267d015ef2afd19fab6.png"},{"id":101751735,"identity":"ceb4909d-3aa9-445b-a097-ba0b2ce6496b","added_by":"auto","created_at":"2026-02-03 10:23:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4485662,"visible":true,"origin":"","legend":"\u003cp\u003e(A) X-ray diffraction patterns of ZGIO:\u003cem\u003ex\u003c/em\u003eCr\u003csup\u003e3+\u003c/sup\u003e and ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e. (B) Rietveld refinement of XRD patterns for ZGIO:0.04Cr\u003csup\u003e3+\u003c/sup\u003e and ZAIO:0.04Cr\u003csup\u003e3+ \u003c/sup\u003ecrystals. (C) Dependence of cell parameters (a, b, and c) and unit cell volume (V) on the Al\u003csup\u003e3+\u003c/sup\u003e content of ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+ \u003c/sup\u003e(\u003cem\u003em\u003c/em\u003e = 0, 0.2, 0.4, 0.6, 0.8, 1). (D) Scanning electron microscopy and elemental mapping images ZnGa\u003csub\u003e0.9\u003c/sub\u003eAl\u003csub\u003e0.1\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e. (E-H) High-resolution XPS patterns of Cr\u003csup\u003e3+\u003c/sup\u003e, Ga\u003csup\u003e3+\u003c/sup\u003e and Al\u003csup\u003e3+\u003c/sup\u003e. (I) Raman spectra of ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+ \u003c/sup\u003e(\u003cem\u003em\u003c/em\u003e = 0, 0.1, 0.6, 1) crystals. (J) High-resolution transmission electron microscopy image.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/7bc03e229b034ac0ab9112db.jpeg"},{"id":101751410,"identity":"bcbe2ae2-35c5-48fb-bca9-10bfdbca0c01","added_by":"auto","created_at":"2026-02-03 10:20:04","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4261289,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PLE and PL spectra of ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e (\u003cem\u003em\u003c/em\u003e = 0 - 1.00). (B) Relationship between Al\u003csup\u003e3+\u003c/sup\u003e ion concentration and PL spectrum intensity. (C) The calculation results of bond angle variance under different concentrations of Al\u003csup\u003e3+\u003c/sup\u003e ions. (D) Cr\u003csup\u003e3+\u003c/sup\u003e emission principle interpreted by combining the T–S energy level diagram and configuration coordinate model in the strong and weak octahedral field. (E) Diffuse reflectance spectra of ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e. (F) Diffuse reflectance spectra focused on 500 - 650 nm. (G) Decay curves of ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e samples. (H) Calculation results of lifetime at different Al\u003csup\u003e3+\u003c/sup\u003e ion concentrations. (I) TL spectra of ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/ba214cc6b4476a4c50119bbd.jpeg"},{"id":103056266,"identity":"ef17c643-3284-47c1-bec2-8b63dd2f5fb8","added_by":"auto","created_at":"2026-02-20 09:00:36","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5066718,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The experimental setup used to measure the ML. (B) The ML spectrum of ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO₄:0.04Cr\u003csup\u003e3+\u003c/sup\u003e. (C) The integrated ML intensity of ZnGa\u003csub\u003e1-\u003c/sub\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO₄:0.04Cr\u003csup\u003e3+\u003c/sup\u003e within the force range of 5 - 30 N. (D) The effects of ultraviolet light irradiation and heating (at 423 K) on the overall mechanical loss intensity of ZAIO:0.04Cr\u003csup\u003e3+\u003c/sup\u003e (top) and the cyclic stability under continuous mechanical excitation of 20 N (bottom). (E) TL and ML spectra of ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e before and after heating at 573 K for 20 min. (F) ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e piezoelectric response force microscope amplitude and phase. (G) Schematic diagram of the mechanism for converting mechanical energy to photon energy in Cr\u003csup\u003e3+\u003c/sup\u003e-doped ZGIO and ZAIO. (H) Comparison of the excitation wavelength and FWHM between Cr\u003csup\u003e3+\u003c/sup\u003e-doped ML phosphors. (I) Optical photos of multiple mechanical stimuli.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/0e48adc74de9e40da3f65d6a.jpeg"},{"id":101751744,"identity":"7ce255b5-2cff-4da4-9c8f-469646401a26","added_by":"auto","created_at":"2026-02-03 10:23:04","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5310238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e. \u003c/strong\u003e(A) Schematic diagram of the sign language recognition system (transimpedance amplifier (TIA), multiplexer (MUX), analog-to-digital conversion (ADC), microcontroller unit (MCU)). (B) Voltage signals from the index finger at different bending angles. (C) Sign language translation achieved through different gestures and their corresponding voltage signals. (D) Sign language recognition scenario. (E) Overview of data processing and deep learning implementation for sign language classification. (F) Relationship between CNN recognition accuracy and number of training iterations for training and validation sets. (G) Confusion matrix for 10 sign language classifications.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/dc5cdd43450fd0fc870728a3.jpeg"},{"id":103507111,"identity":"3d6afe8a-238e-4727-96be-38e09f298986","added_by":"auto","created_at":"2026-02-26 13:40:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23615820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/524fc666-e5c9-4167-91f2-e47747844885.pdf"},{"id":101451325,"identity":"99b1871e-0479-4dcb-aad0-fd09dfbbe579","added_by":"auto","created_at":"2026-01-29 20:30:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7100127,"visible":true,"origin":"","legend":"Supporting information","description":"","filename":"Supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/400482f44fff79415f5a4f0a.docx"},{"id":101451324,"identity":"ac707fa7-76a6-45e8-a927-ef8c67511335","added_by":"auto","created_at":"2026-01-29 20:30:11","extension":"mp4","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14059232,"visible":true,"origin":"","legend":"Video 1","description":"","filename":"Video1.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/e92f7b85f3eefc4beb971b4c.mp4"},{"id":101451327,"identity":"c935c48c-bce7-49bd-902e-79204f8aa587","added_by":"auto","created_at":"2026-01-29 20:30:11","extension":"mp4","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":33578184,"visible":true,"origin":"","legend":"Video 2","description":"","filename":"Video2.mp4","url":"https://assets-eu.researchsquare.com/files/rs-8664424/v1/de4c646c90d440fc51d6e4eb.mp4"}],"financialInterests":"There is no conflict of interest","formattedTitle":"High-Precision Sign Language Recognition Enabled by Self-Recoverable Near-Infrared Mechanoluminescent Materials","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the rapid development of artificial intelligence and the internet of things technologies, human-machine interaction (HMI) has emerged as a core technology driving the intelligent transformation of multiple fields, including wearable devices, industrial robots, virtual reality, and barrier-free communication.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e So far, the mainstream hand motion capture technologies for HMI can be broadly categorized into two categories. The first category relies on visual sensing\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e or inertial measurement units (IMU).\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e These technologies feature high data transmission rates, but the visual methods are susceptible to occlusion and ambient light interference. In addition, IMU-based systems require the integration of multiple sensing modules and impose stringent demands on data processing capabilities, resulting in poor portability and high power consumption.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Among the flexible sensors adopted in the second category,\u003csup\u003e9, 10\u003c/sup\u003e traditional electrical sensors (resistive,\u003csup\u003e11\u003c/sup\u003e capacitive,\u003csup\u003e12\u003c/sup\u003e and piezoelectric types\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e) are prone to electromagnetic interference in complex industrial or daily environments, leading to signal distortion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By contrast, mechanoluminescent (ML) sensors transmit signals through optical channels, which can effectively avoid electromagnetic interference and ensure stable operation in electromagnetic-intensive scenarios.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Moreover, ML material-based sensors can achieve self-powering via the direct conversion of mechanical energy into light energy, eliminating the need for external power supplies or pre-irradiation charging.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Additionally, ML materials can be combined with a wide range of flexible substrates and integrated with photoelectric sensors (PES) for distributed pressure detection. These unique advantages render ML sensors highly compatible with the current requirements for HMI systems that demand low-power consumption, portability, and high adaptability.\u003c/p\u003e \u003cp\u003eSince the ML phenomenon was first observed in the 17th century, extensive research has yielded numerous high-performance materials, such as SrAl\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e:Eu\u003csup\u003e2+\u003c/sup\u003e, Dy\u003csup\u003e3+\u003c/sup\u003e, ZnS:Mn\u003csup\u003e2+\u003c/sup\u003e, and CaZnOS:Mn\u003csup\u003e2+\u003c/sup\u003e.\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e However, most early-stage mechanoluminescent (ML) materials were defect-controlled types, which required energy to be stored in trap states via pre-irradiation charging.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e This characteristic has severely restricted their applications in real-time and continuous human-machine interaction (HMI) scenarios. Recently, self-recoverable ML materials based on the piezoelectric effect have become a research hotspot.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Such materials can maintain stable luminescence intensity under cyclic mechanical stimulation without the need for pre-irradiation. For example, Xiong et al. reported a Cr\u003csup\u003e3+\u003c/sup\u003e-doped ML material, which exhibits a peak emission at 716 nm with a full width at half maximum (FWHM) of 40 nm, and possesses self-recoverable properties in the deep red to near-infrared (NIR) range.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Subsequently, many researchers successfully developed a series of Cr\u003csup\u003e3+\u003c/sup\u003e ion-activated NIR ML materials through screening of persistent luminescent phosphors.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Lu\u003csub\u003e3\u003c/sub\u003eAl\u003csub\u003e5\u003c/sub\u003eO\u003csub\u003e12\u003c/sub\u003e:Cr\u003csup\u003e3+\u003c/sup\u003e is also a NIR ML material with piezoelectric properties, and exhibits a NIR emission peaking at 700 nm. Introducing Ga\u003csup\u003e3+\u003c/sup\u003e ions via a chemical co-substitution strategy modulates the crystal field strength and defect distribution, enabling piezoelectric-induced ML luminescence.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The advent of these self-recoverable ML materials has laid a critical material foundation for developing a new generation of high-performance flexible sensing systems. However, how to fully integrate their inherent advantages into and revolutionize the existing sensing technology architectures remains a key challenge to be further explored.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo address the limitations of existing HMI sensing technologies and further enhance the performance of flexible ML sensors, this study develops a series of self-recoverable NIR ML materials, namely ZnGa\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:Cr\u003csup\u003e3+\u003c/sup\u003e, which exhibit excellent piezoelectric properties, low cost and biocompatibility. The luminescence performance is optimized by regulating the host composition and defect engineering. The piezoelectric effect induces local electric fields within the material, which can alter the band structure and trap depths, facilitating the detrapping of electrons and their subsequent recombination with holes to produce light emission. The interaction between the piezoelectric field and the crystal field strength can regulate the energy level of Cr\u003csup\u003e3+\u003c/sup\u003e ions, thereby influencing the NIR luminescence. This paper will systematically examine the luminescence characteristics of Cr\u003csup\u003e3+\u003c/sup\u003e ions across various matrices, correlates the luminescence and ML performance with the piezoelectric effect, crystal field strength and defect distribution, and experimentally verifies the proposed piezoelectric-induced ML mechanism. Ultimately, this study further explored the practical application potential of this material in the field of stress monitoring. By integrating it with PES and convolutional neural networks (CNN), it provides solid theoretical support and reliable experimental basis for the development of advanced sensing systems with high sensitivity and precise intelligent recognition capabilities.\u003c/p\u003e"},{"header":"2. Results and discussion","content":"\u003cp\u003eThe crystal structure of ZnGaInO\u003csub\u003e4\u003c/sub\u003e (ZGIO) belongs to the orthorhombic system, with a space group of R-3m. In this structure, metal ions form complex coordination environments with O\u003csup\u003e2\u0026minus;\u003c/sup\u003e ions, featuring both pentacoordinated and hexacoordinated sites. By systematically replacing Ga\u003csup\u003e3+\u003c/sup\u003e ions with Al\u003csup\u003e3+\u003c/sup\u003e ions in ZGIO, ZnAlInO\u003csub\u003e4\u003c/sub\u003e (ZAIO) was successfully synthesized. This compound maintains an orthorhombic crystal structure, akin to that of ZGIO. However, the introduction of Al\u003csup\u003e3+\u003c/sup\u003e induces subtle changes in lattice parameters and local structure due to the smaller ionic radius of Al\u003csup\u003e3+\u003c/sup\u003e (0.48 \u0026Aring;) compared to Ga\u003csup\u003e3+\u003c/sup\u003e (0.55 \u0026Aring;). This substitution leads to lattice contraction, as the smaller Al\u003csup\u003e3+\u003c/sup\u003e ions occupy the metal ion sites (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, the higher electronegativity of Al\u003csup\u003e3+\u003c/sup\u003e compared to Ga\u003csup\u003e3+\u003c/sup\u003e results in stronger charge attraction to surrounding oxygen ions, altering the crystal field symmetry and strength. These modifications significantly influence the electronic transition energy levels, as illustrated in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e were synthesized through a high-temperature solid-state reaction at 1450 ℃. The raw materials were mixed in a chemical stoichiometric ratio and then placed in an alumina crucible, which was maintained at this temperature for 4 hours. Their ML properties enable their application in various fields, such as sign language recognition and intelligent highways.\u003c/p\u003e \u003cp\u003eThe crystal structure and phase purity of the synthesized materials were investigated. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and S2 present the XRD patterns of ZGIO:\u003cem\u003ex\u003c/em\u003eCr\u003csup\u003e3+\u003c/sup\u003e and ZnGa\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e, respectively. Both patterns exhibit well-defined diffraction peaks that closely match the standard reference data, confirming the high phase purity and excellent crystallinity of the synthesized materials. When smaller ions are incorporated into the crystal lattice, the lattice will contract, resulting in the diffraction peaks shifting to larger angles. Notably, as the Al\u003csup\u003e3+\u003c/sup\u003e content increases, the main peak position of the diffraction pattern gradually shifts towards smaller angles, indicating that the material has transformed from ZGIO to ZAIO (Figure S2B). This shift demonstrates the successful incorporation of Al\u003csup\u003e3+\u003c/sup\u003e into the lattice and the formation of a solid solution. To further verify its purity, the Rietveld refinement was carried out on the ZGIO:\u003cem\u003ex\u003c/em\u003eCr\u003csup\u003e3+\u003c/sup\u003e and ZnGa\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e phosphors and the refinement results were all within the confidence range (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Figure S3-S4). Table S2 provides detailed crystal cell parameters. As the concentration of Cr\u003csup\u003e3+\u003c/sup\u003e increases, the crystal cell parameters show a decreasing trend, which is consistent with the lattice contraction caused by the difference in ionic radii. As the content of Al\u003csup\u003e3+\u003c/sup\u003e ions increases, the crystal cell parameters (a, b, c) and the crystal cell volume (V) of the refined sample gradually decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Table S3). This is because the smaller Al\u003csup\u003e3+\u003c/sup\u003e ions (CN\u0026thinsp;=\u0026thinsp;5, 0.48 \u0026Aring;) gradually replace the Ga\u003csup\u003e3+\u003c/sup\u003e ions (CN\u0026thinsp;=\u0026thinsp;5, 0.55 \u0026Aring;). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, the scanning electron microscope (SEM) image reveals a uniform distribution of particles, with an average size of approximately 5 micrometers, and all constituent elements are uniformly distributed throughout the sample. This uniform particle morphology is beneficial for enhancing the optical properties of the material, as it can reduce light scattering and promote efficient luminescence. The surface elemental composition and chemical states of the materials were analyzed using X-ray photoelectron spectroscopy (XPS). Figures S5A and S5B display the XPS survey spectra of ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e, respectively, providing comprehensive information on the elemental composition of the material surfaces. To further clarify the chemical environments of Cr\u003csup\u003e3+\u003c/sup\u003e ions in these two matrices, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF display the XPS spectra of Cr 2p\u003csub\u003e3/2\u003c/sub\u003e and Cr 2p\u003csub\u003e1/2\u003c/sub\u003e, respectively. The Cr 2p\u003csub\u003e3/2\u003c/sub\u003e peaks of ZnGaInO\u003csub\u003e4\u003c/sub\u003e:Cr\u003csup\u003e3+\u003c/sup\u003e and ZnAlInO\u003csub\u003e4\u003c/sub\u003e:Cr\u003csup\u003e3+\u003c/sup\u003e are located at 576 eV and 577 eV, while the Cr 2p\u003csub\u003e1/2\u003c/sub\u003e peaks are situated at 586 eV and 587 eV, respectively. In this case, the smaller ionic radius of Al\u003csup\u003e3+\u003c/sup\u003e ions may lead to an increase in the crystal field strength around Cr\u003csup\u003e3+\u003c/sup\u003e ions, thereby enhancing the binding energy of Cr 2p electrons.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e For ZnGa\u003csub\u003e0.9\u003c/sub\u003eAl\u003csub\u003e0.1\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:Cr\u003csup\u003e3+\u003c/sup\u003e, the Ga 3d\u003csub\u003e5/2\u003c/sub\u003e and Ga 3d\u003csub\u003e3/2\u003c/sub\u003e peaks are at 20.4 eV and 18.4 eV, respectively, and the Al 2p peak is at 74.9 eV (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). These results indicate that adjusting the composition of the material can modify the electronic structure and energy bands, thereby influencing their optical and electrical properties. This further verifies the substitution of Ga\u003csup\u003e3+\u003c/sup\u003e by Al\u003csup\u003e3+\u003c/sup\u003e in the lattice. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI, low-wavenumber peaks corresponding to the stretching and bending vibrations of Zn-O bonds reflect the local structure of ZnO and the vibrational environment of Zn in the crystal lattice. For example, the peak around 200 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is related to the bending vibration mode of Zn-O bonds. Ga and Al ions have similar ionic radii and chemical properties, and they often occupy similar sites in the lattice. The vibrational peaks of the bonds they form with O (Ga-O, Al-O) are difficult to completely distinguish and appear in the medium-low wavenumber range (such as 300\u0026ndash;600 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The peak values of the vibration mode of the In-O bond usually occur in the mid-wave number region (such as 600\u0026ndash;800 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and may have characteristic peaks, which reflect the vibration coordination structure of indium in the crystal lattice.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Subsequently, the interlayer spacing in ZnGa\u003csub\u003e0.9\u003c/sub\u003eAl\u003csub\u003e0.1\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e were further observed through high-resolution transmission electron microscopy (HRTEM) images. As displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ, lattice fringes can be clearly observed in ZnGa\u003csub\u003e0.9\u003c/sub\u003eAl\u003csub\u003e0.1\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e. Among them, the interlayer spacing of approximately 0.419 nm can be attributed to the (006) plane of ZnGa\u003csub\u003e0.9\u003c/sub\u003eAl\u003csub\u003e0.1\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure S6A presents the excitation spectra of ZGIO:\u003cem\u003ex\u003c/em\u003eCr\u003csup\u003e3+\u003c/sup\u003e monitored at the emission peak of 797 nm, which consists of three broad excitation bands with peak positions located at 352 nm, 416 nm, and 581 nm, respectively. These peaks correspond to the transition absorptions of the d-electron energy levels of Cr\u003csup\u003e3+\u003c/sup\u003e ions, namely \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eT\u003csub\u003e1\u003c/sub\u003e(\u003csup\u003e2\u003c/sup\u003eE), \u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eT\u003csub\u003e1\u003c/sub\u003e(\u003csup\u003e4\u003c/sup\u003eF), and \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e(\u003csup\u003e4\u003c/sup\u003eF).\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Accordingly, Figure S6A displays the PL spectra of ZGIO:\u003cem\u003ex\u003c/em\u003eCr\u003csup\u003e3+\u003c/sup\u003e exhibit a broadband emission in the range of 600\u0026ndash;1100 nm, which is attributed to the \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e transition of Cr\u003csup\u003e3+\u003c/sup\u003e ions. As the doping concentration of Cr\u003csup\u003e3+\u003c/sup\u003e ions increases, the emission intensity first increases and then decreases (Figure S6B). Figure S6C illustrates the diffuse reflectance spectra at different Cr\u003csup\u003e3+\u003c/sup\u003e doping concentrations, where three distinct absorption peaks align with the excitation bands in the excitation spectrum, further confirming the energy level transition characteristics of Cr\u003csup\u003e3+\u003c/sup\u003e. Figure S6D illustrates the variation in decay curves at different Cr\u003csup\u003e3+\u003c/sup\u003e doping concentrations. As the Cr\u003csup\u003e3+\u003c/sup\u003e concentration increases, the lifetime progressively shortens. This can be attributed to the larger interatomic spacing of Cr\u003csup\u003e3+\u003c/sup\u003e ions, which weakens the intensity of non-radiative transitions. However, as the Cr\u003csup\u003e3+\u003c/sup\u003e concentration rises, the number of interionic non-radiative transition pathways gradually increases, accelerating the relaxation rate of excited-state electrons and ultimately leading to a sustained decrease in lifetime.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Thermoluminescence (TL) analysis depicted in Figures S6E and S6F indicates that an increase in the Cr\u003csup\u003e3+\u003c/sup\u003e ion doping concentration is significantly positively correlated with the increase in trap depth and the enhancement of emission spectral intensity. This suggests that Cr\u003csup\u003e3+\u003c/sup\u003e ions exhibit higher efficiency in energy capture and release within the material, thereby enhancing its luminescence performance. As depicted in Figure S7, with the increase of Cr\u003csup\u003e3+\u003c/sup\u003e ion concentration, the ML intensity exhibits a gradually enhanced trend, and reach its maximum intensity at a Cr\u003csup\u003e3+\u003c/sup\u003e concentration of 0.04, which is consistent with the optimal concentration observed in the photoluminescence spectra. Therefore, the optimized ZGIO:0.04Cr\u003csup\u003e3+\u003c/sup\u003e has a higher probability of energy level transition, enabling it to exhibit intense luminescence under external force.\u003c/p\u003e \u003cp\u003eFigure S8A illustrates the excitation and emission spectra of Cr\u003csup\u003e3+\u003c/sup\u003e-doped ZGIO phosphors at varying concentrations of Al\u003csup\u003e3+\u003c/sup\u003e ion doping. With the increase of Al\u003csup\u003e3+\u003c/sup\u003e ion concentration, the excitation peak exhibit a redshift from 316 nm to 553 nm, while the emission peak is blueshifted. This phenomenon indicates that the introduction of Al\u003csup\u003e3+\u003c/sup\u003e ions significantly alters the crystal field environment of the material, thereby affecting the energy level structure of Cr\u003csup\u003e3+\u003c/sup\u003e ions. Specifically, the substitution of Al\u003csup\u003e3+\u003c/sup\u003e ions for Ga\u003csup\u003e3+\u003c/sup\u003e ions will increase the strength of the crystal field, causing a shift in the energy levels of Cr\u003csup\u003e3+\u003c/sup\u003e ions, and subsequently resulting in a deviation in the position of the transition peaks. Furthermore, as the concentration of Al\u003csup\u003e3+\u003c/sup\u003e ions increases, the type of energy level transition for Cr\u003csup\u003e3+\u003c/sup\u003e ions changes from \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e to \u003csup\u003e2\u003c/sup\u003eE\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e, further confirming the regulatory effect of Al\u003csup\u003e3+\u003c/sup\u003e ions on the energy level structure of Cr\u003csup\u003e3+\u003c/sup\u003e ions. These findings provide an important theoretical basis for controlling the optical properties of Cr\u003csup\u003e3+\u003c/sup\u003e doped phosphors to achieve low-energy excitation and high-efficiency emission. When the Al\u003csup\u003e3+\u003c/sup\u003e ion concentration reaches 0.10, the emission spectrum intensity reaches a maximum value of 12.18-fold (Figure S8B). Meanwhile, the peak wavelength of the emission spectrum shifted from 797 nm to a shorter wavelength region at 703 nm (Figure S9), and the emission intensity showed a consistently increasing trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Figure S10 presents contour plots of the wavelength-resolved photoluminescence spectra for ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e. The spectrum of ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e exhibits broad-bandwidth and relatively dispersed excitation-emission signals, attributed to the weak crystal field effect and high lattice distortion of the ZGIO matrix, leading to the dominance of the broad \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e transition of Cr\u003csup\u003e3+\u003c/sup\u003e (Figure S10A). In contrast, after Al\u003csup\u003e3+\u003c/sup\u003e doping (Figure S10B), the excitation-emission signals evolve into localized, periodically distributed peaks. This is because the smaller ionic radius of Al\u003csup\u003e3+\u003c/sup\u003e reduces lattice distortion and enhances crystal field strength, which promotes a shift in the ground excited state of Cr\u003csup\u003e3+\u003c/sup\u003e ions from \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e to \u003csup\u003e2\u003c/sup\u003eE, dominating the \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eE\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e peak transition. When all Ga\u003csup\u003e3+\u003c/sup\u003e ions were replaced by Al\u003csup\u003e3+\u003c/sup\u003e, the emission spectrum intensity reached its maximum, increasing to 40.65-fold of the original value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).To gain a deeper understanding of the phenomenon where the spectral emission spectrum shifts from broad peak emission to sharp peak emission, the changes in bond angles in the refined statistical results were analyzed, and the distortion degree of the unit cell was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). When the content of Al\u003csup\u003e3+\u003c/sup\u003e ions increased, the variance of bond angles in the unit cell decreased, and the distribution of electrons in the energy band became more concentrated and orderly, thereby leading to sharp peak emission. The introduction of Al\u003csup\u003e3+\u003c/sup\u003e ions may enhance the stability of the lattice and reduce the influence of lattice vibrations on electron transitions, resulting in sharp peak emission. Furthermore, in ZGIO:\u003cem\u003ex\u003c/em\u003eCr\u003csup\u003e3+\u003c/sup\u003e materials, as Ga ions are progressively replaced by Al\u003csup\u003e3+\u003c/sup\u003e ions, the energy level transitions of Cr\u003csup\u003e3+\u003c/sup\u003e ions undergo significant changes. Specifically, the transition of Cr\u003csup\u003e3+\u003c/sup\u003e changes from \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e to \u003csup\u003e2\u003c/sup\u003eE\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e. This transition is primarily ascribed to the introduction of Al\u003csup\u003e3+\u003c/sup\u003e, which enhances the crystal field strength, leading to an increased splitting of the Cr\u003csup\u003e3+\u003c/sup\u003e d-orbital energy levels and rendering the \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eE state as the lowest excited state. Such changes in energy level transitions not only significantly affect the luminescence characteristics of the material but also have important implications for its optical properties and potential applications (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE presents the diffuse reflectance spectra of the phosphors in the wavelength range of 240\u0026ndash;780 nm as the Al\u003csup\u003e3+\u003c/sup\u003e ion concentration increases from 0 to 1.00. In Figure S11, the excitation peaks of ZGIO:0.04Cr\u003csup\u003e3+\u003c/sup\u003e and ZAIO:0.04Cr\u003csup\u003e3+\u003c/sup\u003e match well with the absorption peaks in the diffuse reflection spectra. With the increase of Al\u003csup\u003e3+\u003c/sup\u003e concentration, the absorption peak corresponding to the \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e transition is significantly enhanced, likely due to the introduction of Al\u003csup\u003e3+\u003c/sup\u003e ions altering the crystal field environment and thus affecting the energy level structure and optical properties of Cr\u003csup\u003e3+\u003c/sup\u003e ions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). To elucidate the underlying mechanism of the transformation from a broad emission peak to a sharp emission peak induced by Al\u003csup\u003e3+\u003c/sup\u003e doping in the material, systematic theoretical calculations were analyzed. The results showed that the band gap of the material was expanded from 1.08 eV of ZGIO to 1.74 eV of ZAlO (Figure S12A and S12B). This change was attributed to the smaller ionic radius of Al\u003csup\u003e3+\u003c/sup\u003e, which could enhance the crystal field strength, improve the lattice symmetry, and suppress lattice distortion, thereby raising the electron energy level and significantly reducing the contribution of Al-related orbitals to the conduction band (Figure S12C and S12D). The broadening of the band gap directly led to the narrowing of the emission spectrum, providing a theoretical basis for the regulation of the material's luminescence performance. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG displays the trend of changes in fluorescence lifetime as the Al\u003csup\u003e3+\u003c/sup\u003e ion concentration increases from 0 to 1.00. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH demonstrates that the lifetime value significantly increases from 0.153 ms to 1.76 ms as the concentration of Al\u003csup\u003e3+\u003c/sup\u003e ions increases. This can be attributed to the addition of Al\u003csup\u003e3+\u003c/sup\u003e ions, which changed the weak crystal field environment to a strong one. This effect led to a change in the electronic energy level structure of Cr\u003csup\u003e3+\u003c/sup\u003e ions, which in turn influenced the dynamic process of electron capture and release. Eventually, this results in an increase in fluorescence lifetime.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Additionally, the Al\u003csup\u003e3+\u003c/sup\u003e concentration increases, both the peak position and intensity of the TL spectra undergo significant changes, indicating that the trap depth and trap density gradually increase (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI and S13). This change is due to the introduction of additional defect states or alterations in the energy level distribution of existing defect states by the doping of Al\u003csup\u003e3+\u003c/sup\u003e ions, thereby affecting the behavior of carrier capture and release, leading to changes in trap characteristics. Figure S14 displays the emission spectra under different excitation powers, and it can be observed that there is only a change in the photoluminescence intensity. The d\u003csup\u003e3\u003c/sup\u003e electrons of Cr\u003csup\u003e3+\u003c/sup\u003e mainly determine the relative positions of \u003csup\u003e2\u003c/sup\u003eE and \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e in the octahedral field through the crystal field parameter Dq/B. As long as the crystal field strength remains unchanged, the energy level intervals of these two levels are fixed. Therefore, the peak positions of the R line (\u003csup\u003e2\u003c/sup\u003eE\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e) or the broadband (\u003csup\u003e4\u003c/sup\u003eT\u003csub\u003e2\u003c/sub\u003e\u0026rarr;\u003csup\u003e4\u003c/sup\u003eA\u003csub\u003e2\u003c/sub\u003e) hardly drift with the pump power.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKey performance indicators for evaluating phosphor materials are thermal stability and quantum efficiency.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Figure S15 displays the temperature-dependent emission spectra of ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e. As the temperature is raised from 298 K to 473 K, thermal quenching associated with non-radiative transitions causes the integrated emission intensities of ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e to remain at 92.67% and 69.53% of their room-temperature values at 423 K, respectively.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e As shown in Figure S16, the internal quantum efficiencies (IQE) of ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e are 98.15% and 26.94%, respectively. After replacing Ga\u003csup\u003e3+\u003c/sup\u003e with Al\u003csup\u003e3+\u003c/sup\u003e, the quantum efficiency and thermal stability of the sample were significantly improved. This was because the radius of Al\u003csup\u003e3+\u003c/sup\u003e was smaller and the ligand bond energy was higher. This not only reduced the distortion degree of the octahedron where Cr\u003csup\u003e3+\u003c/sup\u003e was located, enhanced the symmetry of the coordination field, but also strengthened the lattice rigidity, significantly suppressing non-radiative transitions.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e As a result, both the quantum efficiency and thermal stability were simultaneously improved. Moreover, compared with currently available broadband NIR phosphors, ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e exhibits superior thermal stability and quantum efficiency (Table S4).\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, we fabricated a custom-made device to investigate the ML properties, which includes a NIR camera, an optical spectrometer, and a mechanical control unit. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB illustrates the ML spectra of ZnGa\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:0.04Cr\u003csup\u003e3+\u003c/sup\u003e phosphors with Al\u003csup\u003e3+\u003c/sup\u003e ion incorporation under a fixed external force (F\u0026thinsp;=\u0026thinsp;20 N). The doping of Al\u003csup\u003e3+\u003c/sup\u003e ions can modulate the piezoelectric properties of the material, thereby enhancing the local piezoelectric field and promoting the increase in trap depth and trap density. This is in complete agreement with the situation where the ML strength has increased. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC exhibits the variation in the stress-induced luminescence intensity produced by the ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e phosphor under different external forces. As the external force increases, the ML intensity shows a significant enhancement trend. The application of external force leads to the generation of more defect states in the phosphor, which can serve as recombination centers for electrons and holes, thereby enhancing the ML response. In addition, the lattice distortion induced by stress alters the crystal-field environment around Cr\u003csup\u003e3+\u003c/sup\u003e ions, thereby affecting their energy-level structure and luminescence properties.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e After UV lamp charging and thermal treatment, the ML intensity remains almost unchanged, indicating that the energy stored in the trap states can be emptied (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Through the free-fall ball experiment, a metal ball with a mass of 100 grams was dropped freely from different heights (10\u0026ndash;60 centimeters) and collided with the powder sample. The influence of impact force on the mechanical luminescence spectrum of the sample was studied (Figure S17), and ML luminescence photos of the sample after being hit by the free-fall were taken (Figure S18). The positive correlation between ML intensity and the falling height confirmed the elastic mechanical stretching of ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e (Figure S17C), making it an ideal candidate material for a stress sensor. Furthermore, the time stability test revealed that the decrease in the ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e phosphor after 14 days was almost negligible (Figure S19A-B). Self-recoverable ML properties, as a key performance indicator of advanced ML materials, are defined by the ability to maintain stable luminescence intensity and dynamic response during cyclic mechanical stimulation, while eliminating the reliance on external pre-irradiation (such as ultraviolet or visible light activation) to supplement luminescent carriers. Therefore, for ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e system, the trap-mediated ML model widely reported in the literature can be ruled out. The ML behavior is attributed to the local piezoelectric effect, which is widely recognized as the origin of self-recoverable ML. To systematically investigate the regulatory effect of trap states on the ML intensity of materials, ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e phosphors were annealed at 300 ℃ for 20 minutes. The ML intensity and TL curves of both samples were measured before and after heat treatment for comparison. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and S20, the TL intensity decreased sharply after heating, confirming the emptying of traps. Notably, the ML intensity remained nearly unchanged, demonstrating that traps exert negligible influence on the ML intensity. Comprehensive analysis indicates that the ML behavior should be attributed to the piezoelectric effect, a conclusion that has gained widespread recognition in the academic community.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e In ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e, the non-uniform distribution of electrons gives rise to localized piezoelectric fields, thereby enhancing charge separation and recombination. Strain-induced piezoelectric fields can modulate and accelerate the directed migration of electrons, which include electrons generated by external forces as well as those produced through the triboelectric effect (charging of grain contact surfaces during friction). These electrons can transfer energy to the matrix, which is then transferred to the dopants via non-radiative transitions, alternatively, electrons can directly transfer energy to the dopants through collisions. Consequently, the activated dopants emit fluorescence and return to the ground state. Under continuous external force, piezoelectric materials can generate a high concentration of electrons, which is why persistent luminescence can still be observed even if carriers are fully detrapped.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, the local piezoelectric response of ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e was measured using piezoelectric force microscopy under a tip bias of G10 V. The observed amplitude butterfly-shaped hysteresis loops strongly confirmed its excellent piezoelectric properties, which were highly consistent with the reported theoretical results. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG illustrates a schematic of a local piezoelectric-induced ML mechanism.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e When mechanical stimuli (such as pressure or strain) are applied to the material, a piezoelectric effect will be generated inside the material, leading to the redistribution of charges between the conduction band and the valence band.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e This charge transfer can cause electrons to transition from the valence band to the conduction band, forming electron-hole pairs. Under the action of the local piezoelectric field, the energy level structure of Cr\u003csup\u003e3+\u003c/sup\u003e ions shifts, resulting in a change in their energy level positions.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e When electrons transition back from the conduction band to the valence band, they may be captured by Cr\u003csup\u003e3+\u003c/sup\u003e ions and enter their specific energy levels. Subsequently, these electrons undergo transitions between the energy levels of Cr\u003csup\u003e3+\u003c/sup\u003e ions and emit photons, thus producing ML. In addition, during the charging process, electrons and holes recombine between the energy levels of Cr\u003csup\u003e3+\u003c/sup\u003e ions, releasing energy. This energy release is manifested in the form of light, that is, ML.\u003csup\u003e40\u003c/sup\u003e The detailed ML peak wavelengths and FWHM values of these Cr\u003csup\u003e3+\u003c/sup\u003e-doped NIR phosphors are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH and Table S4. Among them, the optimal emission wavelength of ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e phosphor is 797 nm, and its FWHM value is 159.2 nm. The primary peak position of the ML of the ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e phosphor is located at 703 nm. The powder samples were ground using a glass tube, and the motion trajectories resulting from the applied stress were captured by a near-infrared camera (Figure S21 and Video 1). The gray values of the NIR-ML images were extracted, and the spatial distribution of ML intensity at different trajectory points was clearly displayed. These results indicate that NIR-ML has significant advantages in obtaining stress information, and the detection flexibility and sensitivity of the samples (ZGIO:Cr\u003csup\u003e3+\u003c/sup\u003e and ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e) before and after regulation have both been improved. When these unexcited powders were directly ground in a quartz mortar or scratched in different organic matrices (such as PET, PDMS and PU), they all exhibited bright NIR emission. This indicates that the ML emission is independent of the type of organic matrix, and the frictional luminescence has little effect on the ML properties of this series of samples (Figure S22). As depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI and S23, due to the soft texture of PDMS being more suitable for practical applications, the ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e@PDMS film exhibited a bright NIR-ML luminescence phenomenon during pressing, stretching, tearing, folding, and rubbing operations, due to the frictional contact between the organic matrix and the ML powder.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the overall architecture, signal response characteristics, and recognition performance of the developed sign language recognition system.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Specifically, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA shows the hardware integration schematic: a glove embedded with 5-channel flexible sensors (ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e@PDMS and PES) acts as the signal acquisition front-end. Current signals from the sensors are converted to analog voltage signals via signal conditioning modules (transimpedance amplifier, multiplexer), transmitted to the STM32 microcontroller through photoelectric conversion and an analog-to-digital conversion interface, and finally uploaded to a terminal device via a Wi-Fi module for algorithm-based signal recognition. First, cyclic tests demonstrated negligible variations in voltage signals collected during 25 repeated bends of the thumb at the same angle. Second, anti-interference tests confirmed minimal differences in voltage signals from 10 repeated thumb bends at the same angle under ambient light and humid conditions (Figure S24). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB (index finger) and Figure S25 display voltage response curves of the glove sensors at different finger bending angles (30\u0026deg;, 60\u0026deg;, 90\u0026deg;), revealing distinguishable peak intensities and intensity ratios of characteristic signals for each motion across the five fingers. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC illustrates the temporal signal distribution of 10 sign language motions (corresponding to semantics such as \"Good\", \"Light\", and \"Love\"), with distinct waveform differences providing a robust feature basis for machine learning. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD and Video 2 demonstrate the actual wearing demonstration of the system, verifying its adaptability in dynamic scenarios. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE and S26 illustrate the neural network workflow for sign language motion classification: 5-channel temporal signals are input, feature extraction is performed via convolution-pooling operations, features are flattened into a one-dimensional vector through a Flatten layer, and the vector is finally fed into a fully connected classifier to output recognition results for 10 target sign language motions.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Training curves in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF indicate that the model achieved 98.64% accuracy on the training set and 99.46% accuracy on the validation set after 50 epochs, demonstrating excellent fitting and generalization capabilities. The confusion matrix in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG further validates the system\u0026rsquo;s reliability in recognizing target sign language motions, with high recognition accuracy across all motion categories. By integrating optical tactile sensing with neural networks, this system enables efficient perception and accurate recognition of sign language motions, offering a viable solution for practical applications in accessible communication.\u003c/p\u003e \u003cp\u003eWith the surge in traffic flow and the improvement in vehicle intelligence, traditional road surfaces are no longer able to meet the development needs of \"functional infrastructure\". Intelligent road surfaces can generate multiple derivative values. Figure S27A depicts the synthesis and workflow of intelligent road surfaces. First, powder and PDMS are combined to prepare intelligent road surfaces with integrated ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e@PDMS luminescent functional layers. When the tire interacts with the road surface dynamically, the mechanical stimulation at the interface triggers the NIR-ML signal of the functional layer and transmits it to the upper computer for feature analysis. This process realizes the cross-domain conversion of \"mechanical stimulation - optical signal - information output\", providing a hardware foundation for in-situ monitoring of the tire condition of automobiles. Figure S27B illustrates the digital encoding logic of the signal, converting the ML signal into digital signals through PES, and marking the states of the \"Front/Back/Left/Right\" four-dimensional space nodes (red nodes represent abnormal features) to achieve digital characterization of the five types of tire conditions (no fault, high/low tire pressure, left/right side bias angle). This encoding rule can directly connect to the on-board terminal, providing intuitive status feedback to the driver. Figure S26C demonstrates the discrimination method based on image features. By using the texture, size and shape features of the luminescent image, different mechanical action traces of tire states can be directly distinguished. This image recognition strategy does not require complex signal processing and can achieve rapid classification of conditions. Combining digital signals and images for machine learning can build an efficient, safe and collaborative modern transportation system. Therefore, self-recoverable sign language recognition and intelligent road system have demonstrated great potential for application in the field of high-precision mechanical testing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eTo meet the core requirements of human-machine interaction for flexible sensors in terms of stability, anti-interference capability, and self-powering, this study has successfully developed a high-performance self-recoverable NIR ML material. By controlling the doping concentration of Al\u003csup\u003e3+\u003c/sup\u003e, the crystal field environment of Cr\u003csup\u003e3+\u003c/sup\u003e ions can be adjusted, thereby increasing the NIR luminescence intensity by 40.65 times. More importantly, the prepared samples possess self-recovery mechanical luminescence properties. After 3000 cycles of mechanical stimulation, the luminescence intensity still retains 98% of the initial value, and no pre-irradiation treatment is required. Mechanism studies have confirmed that the ML characteristics in this material mainly result from the piezoelectric effect, rather than the traditional trap-controlled model. This piezoelectric-controlled ML material generates a local electric field under mechanical stimulation, promoting charge separation and recombination, thereby achieving efficient NIR luminescence. Given the above excellent performance, the ML material ZnAlInO\u003csub\u003e4\u003c/sub\u003e:Cr\u003csup\u003e3+\u003c/sup\u003e@PDMS is utilized through integrating an optical sensing system and a neural network to demonstrate its application potential in high-precision stress monitoring and intelligent sensing. This research expands the design strategy for high-performance NIR ML materials and provides a reliable solution for developing optical sensing systems and intelligent recognition. Further research will explore the performance of NIR ML materials in more practical scenarios and reveal their potential in driving the development of related fields.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThere are no conflicts to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe work is supported by the Higher Education Scientific Research Project of Hebei Province (No.JCZX2025025), the National Natural Science Foundation of China (No.51902080).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eS. Lee, R. Peng, C. Wu, M. Li, Programmable black phosphorus image sensor for broadband optoelectronic edge computing, \u003cem\u003eNature communications\u003c/em\u003e, 2022, 13(1), 1485.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Huang, S. Shi, J. Zha, Y. Xia, H. Wang, P. Yang, L. 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Liu, Meso hybridized silk fibroin watchband for wearable biopotential sensing and ai gesture signaling, \u003cem\u003eAdvanced Science\u003c/em\u003e, 2024, 12(5), 2410702.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Phosphor, Mechanoluminescence, Intelligent neural networks","lastPublishedDoi":"10.21203/rs.3.rs-8664424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8664424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntelligent evolution of human-machine interaction technology demands core capabilities in flexible sensors, including high stability, anti-interference properties, and self-powering. Traditional electrical sensors usually struggle to adapt to complex and long-term application scenarios. Mechanoluminescence (ML) materials offer a novel solution to this challenge, while existing ML materials still face issues such as the requirement for pre-radiation charging and insufficient cycling stability. Herein, this work developed a series of self-recoverable near-infrared (NIR) ML materials - ZnGa\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eAl\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003eInO\u003csub\u003e4\u003c/sub\u003e:Cr\u003csup\u003e3+\u003c/sup\u003e, which possess excellent piezoelectric properties, low cost and biocompatibility. By adjusting the doping concentration of Al\u003csup\u003e3+\u003c/sup\u003e ions, the crystal field strength of the material was precisely controlled, resulting in a 40.65-fold increase in photoluminescence intensity. Even after undergoing thousands of cycles of mechanical stimulation, the self-recoverable NIR ML material can still maintain 98% of its initial luminescence intensity. When integrated with photoelectric sensors, ZAIO:Cr\u003csup\u003e3+\u003c/sup\u003e@PDMS demonstrated outstanding performance in sign language recognition (achieving 99.46% accuracy) and intelligent highway monitoring through convolutional neural networks. This work provides novel insights for designing NIR ML materials and lays the foundation for integrating ML materials with intelligent neural networks.\u003c/p\u003e","manuscriptTitle":"High-Precision Sign Language Recognition Enabled by Self-Recoverable Near-Infrared Mechanoluminescent Materials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 20:30:06","doi":"10.21203/rs.3.rs-8664424/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98f0349b-5163-4336-8f31-fae8c5e17c8d","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61800383,"name":"Physical sciences/Optics and photonics/Optical materials and structures"},{"id":61800384,"name":"Physical sciences/Optics and photonics/Optical techniques"}],"tags":[],"updatedAt":"2026-02-24T09:17:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 20:30:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8664424","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8664424","identity":"rs-8664424","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}