Determination and prediction of surface coating composition, surface properties, and colloidal stability of nanoparticles after in situ exposure to natural waters

纳米颗粒(NPs)是一种新兴的污染物，其环境命运与分子污染物有着根本的区别。天然有机物(NOM)在纳米颗粒表面的吸附是控制颗粒聚集或吸附等相关过程的关键因素。目前可用的数据仅限于简化的实验室条件和系统。因此，为了模拟自然条件下纳米粒子的命运，有必要探索在环境相关条件和复杂的NOM条件下发生的吸附机制。我们最近开发并验证了一种使用透析袋将NPs原位暴露于NOM和地表水中其他溶解成分的新方法。这使我们现在能够获得与现实条件下发生的情况尽可能接近的具有表面涂层的粒子。此外，表面表征技术的进步使得能够以前所未有的详细程度确定复杂的天然NP涂层的组成和性能。在这个项目中，我们旨在探索和预测环境条件下的吸附机理，它对胶体稳定性的影响，以及它与初始NP涂层的关系。我们将研究五种不同类型的二氧化钛纳米粒子在野外条件下形成的纳米涂层的组成和结构，包括从商业产品中提取的粒子。这些颗粒将使用透析袋方法从广泛的水质参数中暴露在60种不同的自然水中。一旦回收，我们将使用XPS、FT-IR、ToF-SIMS和AFM对颗粒进行分析，以确定表面成分、吸附模式和涂层厚度。为了使用原子力显微镜研究涂层厚度，我们小组开发的一种专用样品制备方法将进一步测试和验证。通过激光解吸电离超高分辨FT-ICR MS方法直接测量粒子表面的分子，以及顺序提取和电喷雾电离FT-ICR MS，研究纳米粒子涂层的分子组成和稳定性。最后，我们将对暴露的纳米粒子在相应的天然水中进行聚集动力学实验，以解释异相和均聚集。所获得的数据将提供给多元机器学习模型，以探索初始涂层、天然涂层、水组成和聚集之间的关系，并根据附带的水参数预测纳米涂层的特征和聚集率。模型结果将为预测自然水域中纳米颗粒的环境命运提供宝贵的信息。

Nanoparticles (NPs) are emerging pollutants whose environmental fate differs fundamentally from molecular pollutants. The sorption of natural organic matter (NOM) onto the NP surface is a key factor for relevant processes which control the NP fate such as particle aggregation or sorption on natural surfaces. Currently available data are limited to simplified laboratory conditions and systems. To model the nanoparticle fate under natural conditions, it is therefore essential to explore the sorption mechanisms occurring under environmentally relevant conditions and complex NOM. We recently developed and validated a new method using a dialysis bag for in situ exposure of NPs to NOM and other dissolved components in surface waters. This enables us now to obtain particles with a surface coating as close as possible from what would occur under realistic conditions. In addition, advancements in surface characterization techniques allow to determine the composition and properties of complex natural NP coatings with an unprecedented level of detail.In this project, we aim at exploring and predicting the sorption mechanism under environmental conditions, its influence on the colloidal stability, and its relation to the initial NP coating. We will investigate the composition and structure of NOM-coating formed under field conditions for five different TiO2-nanoparticle types, including particles extracted from commercial products. These particles will be exposed to 60 different natural waters from a wide range of water quality parameters using the dialysis bag method. Once retrieved, we will analyze the particles using XPS, FT-IR, ToF-SIMS, and AFM for determining the surface composition, sorption mode, and coating thickness. For investigating the thickness of the coating using AFM, a dedicated sample preparation method, developed in our group will be further tested and validated. The molecular composition and stability of the NP coating will be investigated via direct measurement of molecules on the particle surface using a newly developed laser desorption ionization ultrahigh resolution FT-ICR MS method as well as sequential extraction followed by electrospray ionization FT-ICR MS. Finally, we will perform aggregation kinetics experiments of the exposed NPs in the corresponding natural water with and without natural colloids in order to account for both hetero- and homoaggregation.The obtained data will feed a multivariate machine learning model to explore to explore the relationship between initial coating, natural coating, water composition, and aggregation and to predict the characteristics of the NOM-coating and the aggregation rate from the incidental water parameters. The model results will provide invaluable inputs for the prediction of the environmental fate of nanoparticles in natural waters.