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

原位暴露于天然水后纳米颗粒的表面涂层成分、表面性质和胶体稳定性的测定和预测

• 批准号: 458047880
• 负责人: Dr. Oliver Lechtenfeld
• 金额: --
• 依托单位: Fachbereich Chemie und Biologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/458047880?language=en
• 关键词: Determination; prediction; surface; coating; composition

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.

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根本不同。天然有机物（NOM）在NP表面上的吸附是控制NP命运的相关过程（如颗粒聚集或在天然表面上的吸附）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。为了模拟自然条件下的纳米粒子的命运，因此，有必要探索在环境相关的条件下发生的吸附机制和复杂的NOM。我们最近开发和验证了一种新的方法，使用透析袋在原位暴露的纳米粒子的NOM和其他溶解组分在表面沃茨。这使我们现在能够获得具有尽可能接近现实条件下发生的表面涂层的颗粒。此外，在表面表征技术的进步允许确定复杂的天然NP涂层的组成和性能与前所未有的详细程度。在这个项目中，我们的目标是探索和预测在环境条件下的吸附机制，其对胶体稳定性的影响，以及它与初始NP涂层的关系。我们将调查的NOM涂层的组成和结构，在现场条件下形成的五种不同的二氧化钛纳米粒子类型，包括从商业产品中提取的颗粒。使用透析袋法，将这些颗粒暴露于60种不同的天然沃茨，这些天然河水具有广泛的水质参数。一旦取回，我们将使用XPS，FT-IR，ToF-SIMS和AFM分析颗粒，以确定表面组成，吸附模式和涂层厚度。为了使用AFM研究涂层的厚度，我们小组开发的专用样品制备方法将进一步测试和验证。NP涂层的分子组成和稳定性将通过使用新开发的激光解吸电离质谱分辨率FT-ICR MS方法直接测量颗粒表面上的分子以及随后电喷雾电离FT-ICR MS的顺序提取来研究。最后，我们将在含有和不含天然胶体的相应天然水中进行暴露的NP的聚集动力学实验，所获得的数据将供给多变量机器学习模型以探索探索初始涂层、天然涂层、水组成和聚集之间的关系，并从附带的水参数预测NOM涂层的特征和聚集速率。该模型的结果将为预测纳米颗粒在自然沃茨中的环境命运提供宝贵的投入。