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

职业：开发量化环境样品中天然和人为含金属纳米颗粒的测量系统

• 批准号: 2237291
• 负责人: Alexander Gundlach-Graham
• 金额: $ 62.94万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237291&HistoricalAwards=false
• 关键词: CAREER; Development; Measurement; System; Quantify

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.

在化学划分的化学测量和成像计划的支持下，爱荷华州立大学的亚历山大·甘克拉赫·格拉汉姆及其研究小组正在努力改善对环境样品中人为纳米颗粒和微粒的检测和定量。纳米颗粒污染是当代公共卫生的关注。在可预见的未来，预计工程纳米颗粒的生产和应用以及通过人类活动产生的偶然纳米颗粒的暴露有望增加。这，开发分析工具以准确测量，表征和监测这些物种对于了解纳米颗粒污染的程度和预测影响的程度和预测影响至关重要。 Gundlach-Graham组将推动使用痕量元素质谱法作为检测，量化和分类的人为纳米材料的工具。他们将开发新颖的工具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育材料，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到，因为它们很小（数百万到数百万个原子），以总质量浓度稀释，并且通常存在于复杂的，富含粒子的材料中，这些物品包含天然存在的成分。这项研究的目的是开发一个可靠的高通量测量系统，用于对潜水员样本类型的含金属纳米颗粒和微粒的定量和分类。为了实现这一目标，Gundlach-Graham组将开发新颖的校准方法和数据处理策略，以通过单粒子电阻耦合等离子体的飞行时间质谱法（SPICP-TOFMS）分析纳米颗粒。具体而言，该团队将推动使用多元素指纹的使用来将天然NP与人为NP分开，研究无监督的机器学习方法以识别新型粒子类别，并创建开源软件工具，以通过SPICP-TOFMS准确且可靠地量化粒子类型的准确量化粒子类型。预计这些发展将提高科学家追踪纳米关节的能力，并了解环境中污染颗粒的命运和运输。在本提案的教育部分中，团队将开发基于计算机的学习模块，以教授计算机编程，蒙特卡洛模拟的基本概念以及分析化学学生的数据分析策略；这些资源将免费获得并发布开放访问。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是珍贵的支持。

项目成果

Introducing “time-of-flight single particle investigator” (TOF-SPI): a tool for quantitative spICP-TOFMS data analysis

隆重推出“飞行时间单粒子探测器”(TOF-SPI)：一种用于定量 spICP-TOFMS 数据分析的工具

• DOI: 10.1039/d3ja00421j
• 发表时间: 2024
• 期刊: Journal of Analytical Atomic Spectrometry
• 影响因子: 3.4
• 作者: Gundlach-Graham, Alex;Harycki, Stasia;Szakas, Sarah E.;Taylor, Tristen L.;Karkee, Hark;Buckman, Raven L.;Mukta, Shahnaz;Hu, Rui;Lee, Woolin
• 通讯作者: Lee, Woolin