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Predicting and achieving self-recoverable mechanoluminescence based on contact electrification
Wenxiang Wang1, Shanwen Wang1, Jianwen Zhang1
1National & Local Joint Engineering Laboratory for Optical Conversion Materials and Technology, Lanzhou University, Lanzhou, P.R. China.
Nature Communications
|April 20, 2026
Summary
Self-recoverable mechanoluminescence (ML) is achieved through contact electrification, not piezoelectricity. New parameters, ΔΦs and εs, predict and enable ML in diverse phosphors for advanced devices.
Area of Science:
- Materials Science
- Solid State Physics
- Luminescence
Background:
- Self-recoverable mechanoluminescence (ML) enables light emission under stress, crucial for intelligent devices.
- Existing self-recoverable ML is primarily linked to piezoelectric materials, limiting its scope.
- A novel approach is needed to broaden the applicability of self-recoverable ML.
Purpose of the Study:
- To explore self-recoverable ML based on contact electrification.
- To introduce and validate key parameters (ΔΦs, εs) for quantifying contact electrification in phosphors.
- To develop a general strategy for achieving self-recoverable ML in a wider range of materials.
Main Methods:
- First-principles calculations to determine surface relative work function (ΔΦs) and surface dielectric constant (εs) for 114 phosphors.
- Experimental validation using 19 commercial phosphors to correlate ML behavior with calculated parameters.
- Interface engineering applied to inactive phosphors to enhance contact electrification and induce self-recoverable ML.
Main Results:
- A strong correlation was found between self-recoverable ML and the contact electrification parameters (ΔΦs, εs).
- Phosphors with high ΔΦs and low εs exhibited active self-recoverable ML.
- Interface engineering successfully induced self-recoverable ML in previously inactive phosphors.
Conclusions:
- Contact electrification offers a viable mechanism for self-recoverable ML, distinct from piezoelectricity.
- The parameters ΔΦs and εs serve as effective predictors for self-recoverable ML capabilities.
- This work provides a general strategy for designing and developing self-recoverable ML materials for diverse applications.
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