Machine-learning assisted filterless color imaging with donor–acceptor ratio engineered self-driven organic photodetectors

作者：Yujie Xie, Xixiang Zhu, Jinpeng Li, Haomiao Yu, Kai Wang, Zhenmei He, Yukang Lu, Yongchao Xie, Henan Li, Zheng Chen, Hanlin Hu, Hui Zhong, Xiaoxian Zhang, Yumeng Shi, Aiwei Tang · 发表于：Nano Research · 年份：2025 · DOI：10.26599/nr.2026.94908382 · 被引用次数：1 · 研究领域：Luminescence and Fluorescent Materials、Advanced Sensor and Energy Harvesting Materials、Organic Electronics and Photovoltaics

Miniaturized optical wavelength-sensing devices based on solution-processed organic materials hold great promise for integration into portable and wearable technologies. Yet, the realization of self-powered compact wavelength sensors remains elusive. Here, we report a self-powered wavelength sensor built from broadband photodetectors featuring a meticulously engineered PM6: L8-BO active layer. By systematically varying the donor-acceptor stoichiometries and implementing these blends in nano-scale active layers (50 nm and 100 nm) that modulate the internal optical field distribution, we tailor the spectral responsivity of individual sensor units, yielding distinct wavelength-dependent optoelectronic signatures. An array of these wavelength-discriminating units enables quantitative discrimination and identification of incident light wavelengths. The device accurately resolves wavelengths from 380 nm to 850 nm with a resolution of better than ～1 nm, determined through the photocurrent ratio mapping of the four photodetector elements. As a proof of concept, we demonstrate the device’s capability in wavelength recognition and full-color imaging, underscoring its potential for compact, self-powered, and versatile optical sensing platforms.