Buckman Raven L, Gundlach-Graham Alexander
Department of Chemistry, Iowa State University, Ames, IA, USA.
Anal Methods. 2024 Aug 29;16(34):5802-5811. doi: 10.1039/d4ay00859f.
Single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) is used to measure the mass amounts of elements in individual nano and submicron particles. In spICP-TOFMS, element signals can only be recorded as "particles" if they are above the critical value, which is the threshold used to distinguish between particle-derived and background signals. If elements in particles are present in amounts close to or below the critical value, then these elements cannot be quantitatively measured, and the shape of the measured mass distributions will not be accurate. In addition, recorded spICP-TOFMS signal distributions are impacted by measurement uncertainty due to counting statistics inherent to the mass analyzer. Counting noise is most pronounced for elements detected with low signal levels and can lead to systematic biases in the observed element masses and mass ratios from a particle event. In turn, spICP-TOFMS data can lead to incorrect conclusions about element composition and/or size of recorded particles. To better understand how biases and noise can alter the interpretation of data, we employ Monte Carlo simulations to model spICP-TOFMS signals as a function of measurement parameters, such as particle size distribution (PSD), multi-element composition, absolute sensitivities (TofCts g), and measurement noise from ion-counting (Poisson) statistics. Monte Carlo simulations allow for the systematic comparison of known (simulated) element mass distributions to experimental (measured) data. To demonstrate the accuracy of our model in predicting spICP-TOFMS signal structure, we highlight the match between data from in-lab measurements and simulations for the detection of CeO, ferrocerium mischmetal, and bastnaesite particles. Through Monte Carlo simulations, we explore how analyte PSDs and other measurement parameters can lead to the determination of biased particle sizes, particle numbers, element ratios, and multi-element compositions.
单颗粒电感耦合等离子体质谱仪（spICP - TOFMS）用于测量单个纳米和亚微米颗粒中元素的质量含量。在spICP - TOFMS中，只有当元素信号高于临界值时才能被记录为“颗粒”，该临界值是用于区分颗粒衍生信号和背景信号的阈值。如果颗粒中的元素含量接近或低于临界值，那么这些元素就无法进行定量测量，并且所测量的质量分布形状也不会准确。此外，由于质量分析仪固有的计数统计，记录的spICP - TOFMS信号分布会受到测量不确定性的影响。对于低信号水平检测到的元素，计数噪声最为明显，并且可能导致在观察到的颗粒事件的元素质量和质量比中出现系统偏差。反过来，spICP - TOFMS数据可能会导致关于记录颗粒的元素组成和/或尺寸的错误结论。为了更好地理解偏差和噪声如何改变数据的解释，我们采用蒙特卡罗模拟来将spICP - TOFMS信号建模为测量参数的函数，如颗粒尺寸分布（PSD）、多元素组成、绝对灵敏度（TofCts g）以及离子计数（泊松）统计产生的测量噪声。蒙特卡罗模拟允许对已知（模拟）元素质量分布与实验（测量）数据进行系统比较。为了证明我们的模型在预测spICP - TOFMS信号结构方面的准确性，我们突出了实验室测量数据与用于检测CeO、铈铁混合稀土金属和氟碳铈矿颗粒的模拟数据之间的匹配。通过蒙特卡罗模拟，我们探索了分析物PSD和其他测量参数如何导致对有偏差的颗粒尺寸、颗粒数量、元素比率和多元素组成的测定。
