Mechano-electro-optical conversion dynamics in mechanoluminescence and its application in remote human–robot interaction

Abstract Mechanoluminescence (ML) is bringing a paradigm-shifting for next-generation light-based human–robot interaction. However, the overlooked character of ML temporal dynamic response remains a critical barrier to overcoming the limitation of mechano-optical conversion efficiency. Here, by resolving the dynamic interplay among stimuli rate, interfacial charge accumulation and ML performance of three typical materials, like ZnS:Cu 2+ , SrAl 2 O 4 :Eu 2+ ,Dy 3+ , Y 3 Al 5 O 12 :Ce 3+ , the cognition of ML has been deeply understand. Obviously, the ML performance is predominantly governed by the cross-coupling of stimuli rate and stimuli time rather than absolute stress magnitude. For the first time, the optimal stretching stimulation rate for commercial ZnS:Cu 2+ , SrAl 2 O 4 :Eu 2+ ,Dy 3+ and Y 3 Al 5 O 12 :Ce 3+ are respectively determined as ~ 10.3 Mpa/s, ~ 11.0 Mpa/s, ~ 31.9 Mpa/s, which is of great significance for obtaining high-performance ML behavior, and an ubiquitous ML hysteresis phenomenon is demonstrated originating from a time-consuming mechano-electro-optical conversion process even existing in trap-controlled SrAl 2 O 4 :Eu 2+ ,Dy 3+ . Moreover, a qualitative relationship for ML brightness (MLB), stimuli rate ( sr ), stimuli time ( st ), inherent interfacial triboelectricity coefficient ( iitre ) and relative interfacial triboelectricity coefficient ( ritre ) is established as MLB = f ( sr )* g ( st )* p ( iitre )* q ( ritre ) for guiding the design of ML elastomers. For instance, based on this equation, a topology-optimized Y 3 Al 5 O 12 :Ce 3+ @polydimethylsiloxane (PDMS) elastomer is engineered, achieving unprecedented 693 times brighter emission, 78% lower stress threshold and 20% lighter weight, which is successfully applied in remote control (~ 450 m) of quadruped robot. Three main contributions of this work include: (i) demonstrating the influence law of temporal dynamic stimulation on ML performance. (ii) resolving long-standing mechano-optical asynchrony debates. (iii) establishing a universal guideline for designing high-performance ML platforms.