揭示ReO的多机制相互作用中的关键机制4- 删除:用于定向吸附剂设计的知识数据双驱框架.
Ling Yuan1, Han Zhang1, Chen Chen1
1State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China.
ACS applied materials & interfaces
|January 9, 2026
概括
一个新的知识-数据双驱动机器学习框架准确地确定了静电相互作用作为酸盐吸附的关键机制. 这一洞察力使得能够设计出具有破纪录的放射性技术去除能力的共价有机框架.
科学领域:
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
- 环境化学环境化学
背景情况:
- 从核废物中有效地去除甲 (TcO4-) 对于安全处置至关重要.
- 由于干扰因素,目前的TcO4-吸附剂具有有限的容量和不清楚的吸附机制.
- 联有机框架 (COFs) 作为吸附剂具有前景,但需要对优化有机学的理解.
研究的目的:
- 开发一个机器学习 (ML) 框架,整合领域知识和数据,以阐明吸附机制.
- 通过知识数据双驱 (DKD) 方法,确定COF上酸盐 (ReO4-) 吸附的主要机制.
- 基于机械学的见解,设计和合成一种具有增强ReO4-吸附能力的新型COF.
主要方法:
- 开发了一个知识数据双驱动 (DKD) 机器学习框架,其中包含了五种吸附机制的数学描述符.
- 将DKD模型的预测准确性和可解释性与纯数据驱动模型进行了比较.
- 使用SHAP分析量化了不同机制对ReO4吸收的贡献.
- 用密度函数理论 (DFT) 的计算和光谱分析来确认吸附机制.
主要成果:
- 与数据驱动模型 (R2 = 0.91) 相比,DKD-ML模型实现了更高的预测准确性 (R2 = 0.93).
- 电静电相互作用被确定为主要的吸附机制,为ReO4吸收贡献66.7%.
- 合成了一种具有超高电荷密度的新型胺基结合COF,Tb-APDC-M,实现了1689.78 mg g-1的创纪录的ReO4吸附能力.
- 增强的性能归因于在iminium连接处引入的高电荷密度.
结论:
- DKD-ML框架有效地阐明了多孔材料中复杂的吸附机制.
- 以机械洞察为指导的有针对性的吸附剂设计可以显著提高TcO4-.等关键污染物的吸附能力.
- 这项工作为开发先进的吸附剂提供了一条道路,用于高效的放射性废物整治.
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