可解读的机器学习辅助优化金属有机框架对吸附:结合对吸附机制分析的表征方法
Chaojie Wang1, Yuxin Zhao2, Hanbo Chen3
1School of Environmental and Chemical Engineering, Foshan University, Foshan, 528225, China.
概括
机器学习优化了使用金属有机框架 (MOFs) 的反吸附. MOF-808在从水中去除反方面表现出高效率和快速动力学,为重金属污染提供了可持续的解决方案.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 水生环境中的反 (Sb) 污染对生态系统和人类健康构成重大风险.
- 传统的Sb修复方法面临诸如长时间处理,复杂参数优化和二次污染物产生等挑战.
- 机器学习 (ML) 提供创新的解决方案,以克服环境修复方面的局限性.
研究的目的:
- 使用可解释的机器学习模型优化反吸附过程.
- 为了确定最佳的吸附条件和有效的吸附剂去除.
- 通过ML和实验验证,提供对吸附的机制性见解.
主要方法:
- 采用四个可解释的ML模型 (包括SVM) 来优化Sb吸附.
- 使用Shapley增量解释 (SHAP) 和部分依赖图 (PDP) 进行模型解释.
- 合成了MOF-808,并进行了实验性吸附研究,通过FTIR和XPS分析验证.
主要成果:
- 该SVM模型确定了十个最佳吸附条件,突出显示MOF-808是优越的吸附剂.
- MOF-808达到120.60毫克g-1的最大Sb (III) 吸附能力,在1小时内达到96%的吸附能力.
- 吸附机制证实涉及协调相互作用和键.
结论:
- 通过SHAP和PDP增强的ML模型提供了机械洞察力,并优化了Sb吸附.
- MOF-808在去除Sb方面表现出高效率,快速动力学和广泛的pH适用性,表明可扩展的环境修复的潜力.
- 该研究建立了一个优化废水处理和设计下一代吸附剂的框架,以减轻重金属的重量.
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