Harnessing Metal-Organic Frameworks for NIR-II Light-Driven Multiphoton Photocatalytic Water Splitting in Hydrogen Therapy.
Xin LuXinlei YuBo LiXianshun SunLongjiu ChengYuanZhong KaiHongping ZhouYupeng TianDandan LiPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2024)
The construction of near-infrared (NIR) light-activated hydrogen-producing materials that enable the controlled generation and high-concentration release of hydrogen molecules in deep tumor tissues and enhance the effects of hydrogen therapy holds significant scientific importance. To address the key technical challenge of low-efficiency oxidation-reduction reactions for narrow-bandgap photocatalytic materials, this work proposes an innovative approach for the controllable fabrication of multiphoton photocatalytic materials to overcome the limitations imposed by traditional near-infrared photocatalysts with "narrow-bandgap" constraints. Herein, an NIR-responsive multiphoton photocatalyst, ZrTc-Co, is developed by utilizing a post-synthetic coordination modification strategy to introduce hydrogenation active site Co II into a multiphoton responsive MOF (ZrTc). The results reveal that with the introduction of the Co II site, electron-hole recombination can be efficiently suppressed, thus promoting the efficiency of hydrogen evolution reaction. In addition, the integration of Co II can effectively enhance charge transfer and improve static hyperpolarizability, which endows ZrTc-Co with excellent multiphoton absorption. Moreover, hyaluronic acid modification endows ZrTc-Co with cancer cell-specific targeting characteristics, laying the foundation for tumor-specific elimination. Collectively, the proposed findings present a strategy for constructing NIR-II light-mediated hydrogen therapeutic agents for deep tumor elimination.
Keyphrases
- visible light
- photodynamic therapy
- hyaluronic acid
- metal organic framework
- fluorescence imaging
- cancer therapy
- fluorescent probe
- gene expression
- highly efficient
- stem cells
- machine learning
- reduced graphene oxide
- dna damage
- oxidative stress
- genome wide
- deep learning
- artificial intelligence
- big data
- hydrogen peroxide
- heat stress
- nitric oxide