Achieving trap-depth-tunable organic persistent luminescence through host energy-level engineering
Year: 2025
Type: article
Abstract: Recent studies have indicated that traps play a crucial role in determining characteristics of organic persistent luminescence, yet constructing traps with controllable depth remains highly challenging. Herein, we propose a host energy-level engineering strategy, which enables precise control of trap depth. Using the Randall-Wilkins method, the trap depth can be tuned from 0.38 to 0.72 eV without altering emission wavelength and the result aligns well with density functional theory calculations. Specifically, a host-guest material namely CPND@DPEPO, which has a deep trap of ~ 0.72 eV, exhibits deep-blue persistent luminescence lasting for 27 hours and efficient energy storage over 14 days at room temperature. By utilizing these trap-containing materials as the emission layer in organic light-emitting diodes, we develop a pixel-programmable information storage device. This work establishes a fundamental principle for designing organic materials with controllable trap depth, potentially expanding their applications in night tracing, military communication and biological imaging. Zhan et al. report a host energy level engineering strategy to modulate trap depth in organic host-guest systems, achieving persistent luminescence up to 27 hours and energy storage for 14 days. 4×4 electroluminescent arrays based on the host-guest system demonstrate pixel-programmable information storage capabilities.
Source: Nature Communications
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Natural Science Foundation of Guangdong Province, Support Program for Longyuan Youth and Fundamental Research Funds for the Universities of Gansu Province, Fundamental Research Funds for the Central Universities
OpenAlex ID: W7116863151
