Experimental Technology and Management

设计了力致发光材料应用研究的跨学科开放性综合教学实验。以人体生物力学检测为应用场景，首先对商业Zn S:Cu材料进行材料表征测试；再采用填充型复合材料薄膜制备法优化传感元件的组成比例（ZnS:Cu∶PDMS∶固化剂=0.6∶1.0∶0.1），提升力光转化效率并确保传感稳定性；最后开发Python图像处理算法重构力致发光信号的时空分辨热力图，量化牙齿咬合力分布及膝关节动态应力响应。实验创新性地采用“科研项目驱动—工程问题导向—创新创业实践”的三维培养模式，通过文献调研、器件制备、系统集成等全流程训练，强化学生跨学科问题解决能力，从而满足新工科背景下复合型人才培养需求。

[Objective] With the rapid advancement of intelligent sensing technology, mechanoluminescence(ML) materials—which emit light under mechanical stress—have garnered significant interest in stress sensing and biomechanical applications. Among them, ZnS:Cu stands out given its high luminescence efficiency, superior force–light conversion properties, and excellent reusability, making it highly suitable for biomedical sensing. However, the integration of ML materials into engineering education remains underexplored. To bridge this gap, this study designs an interdisciplinary and open-ended teaching experiment introducing ML materials into experimental education. By incorporating elements of material science, biomechanics, optical sensing, and computational analysis, this experiment aims to enhance students' interdisciplinary problem-solving abilities and hands-on engineering skills, aligning with the evolving demands of modern engineering education. [Methods] The experiment follows a structured framework simulating real-world research and development processes and integrating multiple aspects of material science, biomechanics, and computational analysis. It first characterizes commercial ZnS:Cu materials, where students conduct optical and structural analyses to fundamentally understand material properties. This is followed by sensor fabrication, where a composite thin-film preparation method is employed to optimize sensor composition(with a ZnS:Cu to PDMS base to PDMS curing agent ratio of 0.6:1:0.1), ensuring an optimal balance between force–light conversion efficiency and mechanical flexibility. After fabrication, students develop a Python-based image processing algorithm to reconstruct spatiotemporal ML signal maps. This enables the quantitative visualization of force distribution in dental occlusion and dynamic knee joint stress responses. Throughout the experiment, a research-driven, problem-oriented, and innovation-focused teaching model is implemented, engaging students in literature review, experimental design, data analysis, and system integration. This approach simulates real-world engineering workflows, fostering interdisciplinary collaboration and critical thinking. [Results] Through this experiment, students acquire hands-on experience in ML sensor fabrication, optical signal acquisition, and computational analysis. Experimental results confirm that the optimized ZnS:Cu/PDMS composite exhibits significant ML emission, with the highest force–light conversion efficiency achieved at a ZnS:Cu doping ratio of 0.6:1.0:0.1. Using high-resolution imaging and Python-based data processing, students successfully visualize stress distribution in biomechanical applications. Compared with conventional methods, such as occlusion paper and wax, the ML sensor demonstrates superior sensitivity, repeatability, and real-time force mapping capabilities, highlighting its potential for biomedical sensing. Furthermore, student feedback indicates notable improvements in interdisciplinary problem-solving skills, experimental proficiency, and teamwork, underscoring the pedagogical value of this teaching experiment. [Conclusions] The comprehensive teaching experiment introduced in this study establishes an effective educational framework integrating cutting-edge material science with digital signal processing in engineering curricula. By combining theoretical instruction with practical applications, it enhances students' experimental research capabilities, interdisciplinary collaboration skills, and innovation-driven thinking. This approach bridges the gap between fundamental scientific principles and real-world engineering applications, equipping students with the necessary competencies to address contemporary engineering challenges. Future iterations of this experiment will incorporate artificial intelligence and data analytics to further expand ML-based sensing applications and continuously refine teaching methodologies to cultivate the next generation of engineers.