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Trap-state network dynamics enable material-native temporal information processing
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DOI：10.1016/j.nanoen.2026.112387.png)
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Abstract
En 中文
Trap states in persistent luminescent materials are generally regarded as carrier storage centers governing afterglow behavior. Here, we reveal a previously unexplored functionality of persistent luminescent materials, in which defect-mediated energy-storage dynamics support temporal information processing. Sequential optical stimulation generates history-dependent luminescence responses that retain information from previous excitation events. We demonstrate that these history-dependent luminescence trajectories establish an intrinsic physical state space capable of nonlinear transformation and temporal memory. Distinct binary excitation histories are mapped into distinguishable afterglow decay trajectories, enabling 4-bit temporal signal representation within a single luminescent material. As a proof of concept, the resulting state space achieves a trajectory prediction accuracy exceeding 90%. The observed state evolution is closely associated with trap-state redistribution and excited-state dynamics, suggesting that defect-related energy-storage processes can actively participate in temporal information encoding. By exploiting luminescence dynamics as a computational resource, this work establishes a direct link between energy storage and information processing. These findings expand the functionality of persistent luminescent materials beyond photon emission, establish a direct link between energy storage and information processing, and provide a pathway toward material-native temporal information processing.
Keywords:
Persistent luminescent materials
History-dependent luminescence dynamics
Trap-state network
Temporal information processing
Computational defect engineering
Journal
IF:
17.1
Papers:
1.2W
Citations:
13.0W
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