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使用机器学习预测药品和个人护理产品在长期老化微塑料上的吸附能力。

Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning.

机构信息

Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China; Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Changsha 410083, PR China; Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore.

Center for Environment and Water Resources, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China; Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, Changsha 410083, PR China.

出版信息

J Hazard Mater. 2023 Sep 15;458:131963. doi: 10.1016/j.jhazmat.2023.131963. Epub 2023 Jun 30.

DOI:10.1016/j.jhazmat.2023.131963
PMID:37406525
Abstract

We investigated the adsorption mechanism of 66 coexisting pharmaceuticals and personal care products (PPCPs) on microplastics treated with potassium persulfate, potassium hydroxide, and Fenton reagent for 54, 110, and 500 days. The total adsorption capacity (q) of 66 PPCPs on 15 original microplastics was 171.8 - 1043.7 μg/g, far below that of 177 long-term aged microplastics (7114.0 - 13,114.4 μg/g). Around 69.8% of q was primarily influenced by the total energy, energy of the highest occupied molecular orbital, and energy gap of PPCPs, calculated using the B3LYP/6-31 G* level. Furthermore, 111 aged microplastics exhibited similar total q values. Additionally, we developed predictive models based on attenuated total reflectance Fourier transform infrared spectroscopy to predict the individual and total q on 192 microplastics. These models, including the maximal information coefficient and gradient boosting decision tree regression, exhibited high accuracy with R values of 0.9772 and 0.9661, respectively, and p-values below 0.001. Spectroscopic analysis and machine learning models highlighted surface functional group alterations and the importance of the 1528-1700 cm spectral region and carbon skeleton in the adsorption process. In summary, our findings contribute to understanding the adsorption of PPCPs on microplastics, particularly in the context of long-term aging effects.

摘要

我们研究了经过过硫酸钾、氢氧化钾和芬顿试剂处理 54、110 和 500 天的微塑料对共存的 66 种医药和个人护理产品(PPCPs)的吸附机制。15 种原始微塑料对 66 种 PPCPs 的总吸附容量(q)为 171.8-1043.7μg/g,远低于 177 种长期老化的微塑料(7114.0-13114.4μg/g)。q 的约 69.8%主要受 PPCPs 的总能量、最高占据分子轨道能量和能隙的影响,这些能量是用 B3LYP/6-31G* 水平计算的。此外,111 种老化的微塑料表现出相似的总 q 值。此外,我们还基于衰减全反射傅里叶变换红外光谱开发了预测模型,以预测 192 种微塑料的单个和总 q 值。这些模型,包括最大信息系数和梯度提升决策树回归,分别具有 0.9772 和 0.9661 的高 R 值和低于 0.001 的 p 值,显示出较高的准确性。光谱分析和机器学习模型突出了表面官能团的变化,以及 1528-1700cm 光谱区域和碳骨架在吸附过程中的重要性。总之,我们的研究结果有助于理解 PPCPs 在微塑料上的吸附,特别是在长期老化效应的背景下。

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