The complex sorption mechanisms of carbon adsorbents for the diverse group of persistent, mobile, and potentially toxic substances (PMs or PMTs) present significant challenges in understanding and predicting adsorption behavior. While the development of quantitative predictive tools for adsorbent design often relies on extensive training data, there is a notable lack of experimental sorption data for PMs accompanied by detailed sorbent characterization. Rather than focusing on predictive tool development, this study aims to elucidate the underlying mechanisms of sorption by applying data analysis methods to a high-quality dataset. This dataset includes more than 60 isotherms for 22 PM candidates and well-characterized high-surface-area activated carbon (AC) materials. We demonstrate how tools such as distance correlation and clustering can be used effectively to identify the key parameters driving the sorption process. Using these approaches, we found that aromaticity, followed by hydrophobicity, are key sorbate descriptors for sorption, overshadowing steric and charge effects for a given sorbent. Aromatic PMs, although classified as mobile contaminants based on their sorption to soil, are well adsorbed by AC as engineered adsorbent via π-π interactions. Non-aromatic and especially anionic compounds show much greater variability in sorption. The influence of ionic strength and natural organic matter on adsorption was considered. Our approach will help in the analysis of solute-sorption systems and in the development of new adsorbents beyond the specific examples presented here. In order to make the approach accessible, the code is freely available and described on GitHub (https://github.com/Laura-Lotteraner/PM-Sorption), following the FAIR data principles.

中文翻译：

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更好地了解活性炭中持久性和移动性污染物的吸附情况：将数据分析技术应用于有限大小的实验数据集


碳吸附剂对各种持久性、可移动性和潜在毒性物质 （PM 或 PMT） 的复杂吸附机制对理解和预测吸附行为提出了重大挑战。虽然用于吸附剂设计的定量预测工具的开发通常依赖于广泛的训练数据，但明显缺乏 PM 的实验吸附数据以及详细的吸附剂表征。本研究不是专注于预测工具的开发，而是旨在通过将数据分析方法应用于高质量的数据集来阐明吸附的潜在机制。该数据集包括 22 种候选颗粒物的 60 多个等温线和表征良好的高表面积活性炭 （AC） 材料。我们演示了如何有效地使用距离关联和聚类等工具来识别驱动吸附过程的关键参数。使用这些方法，我们发现芳香性（其次是疏水性）是吸附的关键山梨酸盐描述符，掩盖了给定吸附剂的空间位位效应和电荷效应。芳香族 PM 虽然根据其对土壤的吸附而被归类为移动污染物，但作为工程吸附剂，通过 π-π 相互作用被 AC 很好地吸附。非芳香族化合物，尤其是阴离子化合物，在吸附方面表现出更大的可变性。考虑了离子强度和天然有机物对吸附的影响。我们的方法将有助于分析溶质吸附体系，并有助于开发除此处介绍的具体示例之外的新型吸附剂。为了使该方法易于访问，代码在 GitHub （https://github.com/Laura-Lotteraner/PM-Sorption） 上免费提供和描述，遵循 FAIR 数据原则。