Trap Assisted Dynamic Mechanoluminescence Toward Self‐Referencing and Visualized Strain Sensing

Strain sensors utilizing mechanoluminescent (ML) materials have garnered significant attention and application due to their advantages, such as self-powering, non-contact operation, and real-time response. However, ML-based strain sensing techniques typically rely on the establishing of a mathematical relationship between ML intensity and mechanical parameters. The absolute ML intensity is vulnerable to environmental factors, which can result in measurement errors. Herein, an color-resolved visualized dynamic ML and self-referencing strain sensing is investigated in Ca<sub>9</sub>Al(PO<sub>4</sub>)<sub>7</sub>: Tb<sup>3+</sup>, Mn<sup>2+</sup>. By analyzing the ML performance under various mechanical stimulations and adjustable strain parameters, a relationship between strain and the ML intensity ratio of Tb<sup>3+</sup>/Mn<sup>2+</sup> is aimed to bed established. This will enable the development of a self-referencing and visualized strain sensing technology. Through a comparison of luminescence characteristics under continuous mechanical stimulation (stretching) and continuous X-ray irradiation, it is discovered that the ratiometric dynamic ML is primarily driven by the dynamic filling and continuous release of carriers form traps, which compensates for the ML of Mn<sup>2+</sup>. Leveraging the self-referencing and color-resolved (from green to red) visualized ML characteristics, an application scenario for monitoring human joint movement is developed. This approach offers new insights into the use of dynamic ML materials in strain sensing and human-machine interaction.

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Publication Details

Journal
Advanced Science
Published
2024-11-27
DOI
https://doi.org/10.1002/advs.202410673
Citations
36
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
6.64

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article

Trap Assisted Dynamic Mechanoluminescence Toward Self‐Referencing and Visualized Strain Sensing

Tianli Wang, Xuhui Xu, Jiahao Huang, Jianqiang Xiao et al.
36 citations
Advanced Science
Advanced Sensor and Energy Harvesting Materials
6.64
article

Trap Assisted Dynamic Mechanoluminescence Toward Self‐Referencing and Visualized Strain Sensing

Tianli Wang, Xuhui Xu, Jiahao Huang, Jianqiang Xiao, Lei Zhao, Pengfei Zhang, Ziyi Guo, Jiaojiao Zheng, Xiongwu Xie
article en
36 citations

Abstract

Strain sensors utilizing mechanoluminescent (ML) materials have garnered significant attention and application due to their advantages, such as self-powering, non-contact operation, and real-time response. However, ML-based strain sensing techniques typically rely on the establishing of a mathematical relationship between ML intensity and mechanical parameters. The absolute ML intensity is vulnerable to environmental factors, which can result in measurement errors. Herein, an color-resolved visualized dynamic ML and self-referencing strain sensing is investigated in Ca<sub>9</sub>Al(PO<sub>4</sub>)<sub>7</sub>: Tb<sup>3+</sup>, Mn<sup>2+</sup>. By analyzing the ML performance under various mechanical stimulations and adjustable strain parameters, a relationship between strain and the ML intensity ratio of Tb<sup>3+</sup>/Mn<sup>2+</sup> is aimed to bed established. This will enable the development of a self-referencing and visualized strain sensing technology. Through a comparison of luminescence characteristics under continuous mechanical stimulation (stretching) and continuous X-ray irradiation, it is discovered that the ratiometric dynamic ML is primarily driven by the dynamic filling and continuous release of carriers form traps, which compensates for the ML of Mn<sup>2+</sup>. Leveraging the self-referencing and color-resolved (from green to red) visualized ML characteristics, an application scenario for monitoring human joint movement is developed. This approach offers new insights into the use of dynamic ML materials in strain sensing and human-machine interaction.

Advanced ScienceVol. 12(3)
Kunming University of Science and Technology (CN), Baoji University of Arts and Sciences (CN)
Natural Science Foundation of Shaanxi Province
Life in Land
Openalex Percentile: Top 2%
Advanced Sensor and Energy Harvesting Materials
6.64
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