CAREER: Development of a Measurement System to Quantify Natural and Anthropogenic Metal-Containing Nanoparticles in Environmental Samples

在化学系化学测量与成像项目的支持下，爱荷华州立大学的Alexander Gundlach-Graham和他的研究小组正在努力改进环境样本中人为纳米颗粒和微颗粒的检测和定量。纳米颗粒污染是一个当代公共卫生问题。在可预见的未来，工程纳米颗粒的生产和应用以及通过人类活动偶然产生的纳米颗粒的暴露预计会增加。因此，开发准确测量、表征和监测这些物种的分析工具对于了解纳米颗粒污染的程度和预测影响至关重要。Gundlach-Graham小组将推进微量元素质谱法的使用，作为一种检测、量化和分类极低浓度人为纳米材料的工具。他们将开发新的仪器方法和开源软件工具，以提高粒子测量的吞吐量和准确性。作为该项目的一部分，该团队还将生成和实施新的教材，以教授高级数据分析策略，作为分析化学课程的一部分。环境样品中的纳米颗粒很难检测，因为它们很小（数亿到数百万个原子），总质量浓度较低，并且通常存在于含有天然颗粒的复杂、富含颗粒的基质中。本研究的目的是开发一种强大的高通量测量系统，用于定量和分类来自不同样品类型的含金属纳米颗粒和微粒。为了实现这一目标，gundlachg - graham小组将开发新的校准方法和数据处理策略，用于单粒子电感耦合等离子体飞行时间质谱（spICP-TOFMS）对纳米颗粒的分析。具体来说，该团队将推进使用多元素指纹来分离自然NPs和人为NPs，研究无监督机器学习方法来识别新的颗粒类别，并创建开源软件工具，通过spICP-TOFMS精确和稳健地量化颗粒类型。这些发展有望提高科学家追踪纳米颗粒的能力，并了解环境中污染颗粒的命运和运输。在该提案的教育部分，该团队将开发基于计算机的学习模块，向分析化学学生教授计算机编程，蒙特卡罗模拟和数据分析策略的基本概念；这些资源将免费提供，并以开放方式发布。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Alexander Gundlach-Graham and his research group at Iowa State University are working to improve the detection and quantification of anthropogenic nanoparticles and microparticles in environmental samples. Nanoparticle pollution is a contemporary public health concern. For the foreseeable future, production and application of engineered nanoparticles and exposure to incidental nanoparticles produced through human activities are expected to increase. Thus, the development of analytical tools to accurately measure, characterize, and monitor these species is critical to understanding the extent—and predicting the impact—of nanoparticle pollution. The Gundlach-Graham group will advance the use of trace-element mass spectrometry as a tool to detect, quantify, and classify anthropogenic nanomaterials at extremely low concentrations. They will develop novel instrumental approaches and open-source software tools to improve the throughput and accuracy of particle measurements. As part of this project, the team will also generate and implement new educational materials to teach advanced data analysis strategies as part of analytical chemistry curricula. Nanoparticles in environmental samples are difficult to detect because they are small (hundreds to millions of atoms), dilute in terms of total mass concentration, and are often present in complex, particle-rich matrices that contain naturally occurring particulates. The aim of this research is to develop a robust high-throughput measurement system for the quantification and classification of metal-containing nanoparticles and microparticles from diverse sample types. To meet this goal, the Gundlach-Graham group will develop novel calibration approaches and data-processing strategies for the analysis of nanoparticles by single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS). Specifically, the team will advance the use of multi-element fingerprints to separate natural NPs from anthropogenic NPs, investigate unsupervised machine learning approaches to identify novel particle classes, and create open-source software tools for the accurate and robust quantification of particle types by spICP-TOFMS. These developments are expected to improve the ability of scientists to track nanoparticulates and to understand the fate and transport of contaminating particles in the environment. In the educational component of this proposal, the team will develop computer-based learning modules to teach basic concepts of computer programming, Monte Carlo simulation, and data analysis strategies to analytical chemistry students; these resources will be freely available and published open access.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.