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<p>The photosynthetic production of hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) from water and oxygen presents a sustainable alternative to the energy-intensive anthraquinone process. Covalent organic frameworks (COFs) have emerged as promising photocatalysts for H<sub>2</sub>O<sub>2</sub> generation. However, most existing COF photocatalysts yield H<sub>2</sub>O<sub>2</sub> at concentrations too low for practical applications, largely due to ongoing challenges in simultaneously optimizing photocatalytic activity and structural stability. Here, we introduce a large language model-driven design strategy for the targeted synthesis of high-performance COF photocatalysts. By analyzing a curated corpus of 355 peer-reviewed articles on COF-based photocatalysis with a language model-driven knowledge extraction pipeline, we extract and structure over 11,000 chemical relationships related to building block identity, linkage robustness, and H<sub>2</sub>O<sub>2</sub> yield. Guided by this artificial intelligence-derived knowledge base, we identify 4,4′,4″-(1,3,5-triazine-2,4,6-triyl)trianiline and benzo[1,2-b:3,4-b′:5,6-b″]trithiophene-2,5,8-tricarbaldehyde as optimal building blocks and thiazole as the preferred linkage motif for constructing a robust, photocatalytic COF. The resulting Thz-COF achieve a high H<sub>2</sub>O<sub>2</sub> concentration of 82.3 mM (~0.28 wt%) in aqueous solution (without using sacrificial agents), with a solar-to-chemical energy conversion efficiency of 1.39%.</p>

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Synthesis of covalent organic frameworks for photocatalytic hydrogen peroxide production guided by large language models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Chang Shu,
  • Ledu Wang,
  • Xiaoju Yang,
  • Wenao Xie,
  • Peixuan Xie,
  • Xiao Wang,
  • Xuan Yang,
  • Jingyi Rao,
  • Kewei Wang,
  • Linjiang Chen,
  • Bien Tan,
  • Xiaoyan Wang

摘要

The photosynthetic production of hydrogen peroxide (H2O2) from water and oxygen presents a sustainable alternative to the energy-intensive anthraquinone process. Covalent organic frameworks (COFs) have emerged as promising photocatalysts for H2O2 generation. However, most existing COF photocatalysts yield H2O2 at concentrations too low for practical applications, largely due to ongoing challenges in simultaneously optimizing photocatalytic activity and structural stability. Here, we introduce a large language model-driven design strategy for the targeted synthesis of high-performance COF photocatalysts. By analyzing a curated corpus of 355 peer-reviewed articles on COF-based photocatalysis with a language model-driven knowledge extraction pipeline, we extract and structure over 11,000 chemical relationships related to building block identity, linkage robustness, and H2O2 yield. Guided by this artificial intelligence-derived knowledge base, we identify 4,4′,4″-(1,3,5-triazine-2,4,6-triyl)trianiline and benzo[1,2-b:3,4-b′:5,6-b″]trithiophene-2,5,8-tricarbaldehyde as optimal building blocks and thiazole as the preferred linkage motif for constructing a robust, photocatalytic COF. The resulting Thz-COF achieve a high H2O2 concentration of 82.3 mM (~0.28 wt%) in aqueous solution (without using sacrificial agents), with a solar-to-chemical energy conversion efficiency of 1.39%.