Improving detection and figures of merit in single-particle inductively coupled plasma-mass spectrometry via transient event heights

Background

Single-particle inductively coupled plasma-mass spectrometry (SP-ICP-MS) is a powerful method for characterizing micro- and nanoparticulate materials. The technique primarily relies on the linear relationship between the integrated intensities of individual events (peak areas) and the analyte mass, though transit times (peak widths) have also been used for quantitative purposes. This work (1) evaluates the potential of using peak heights as analytical signals in SP-ICP-MS, (2) introduces a new method for determining peak heights, and (3) explores scenarios in which peak height offers added value over the commonly used SP-ICP-MS signals.

Results

A new method was proposed to estimate peak height values in SP-ICP-MS accurately. The cumulative intensity across consecutive dwell times was modeled using a third-degree polynomial, from which the adjusted peak height was derived. This approach reduces the uncertainty associated with using raw maximum intensity values, yielding NP distributions comparable to those obtained via integrated intensities. The effect of dwell time on peak height was also evaluated. An optimal range (50 μs–200 μs) was identified, where a linear relationship was observed between the peak height and the square of the NP diameter. Within this range, peak height showed the lowest bias when characterizing smaller NPs, indicating the potential to improve the limit of quantification (LoQ). Additionally, peak heights proved helpful in determining the limit of detection (LoD) and setting appropriate threshold values for data processing, thereby helping to flag incorrect resultsand addressing a challenge in SP-ICP-MS analysis.

Significance

This is the first study to evaluate peak height as an analytical signal in SP-ICP-MS. The results highlight its advantages in specific applications, such as sizing NPs near the LoD, and in supporting the more reliable use of other signals, such as peak areas, by helping to identify incorrect threshold selection that could lead to biased distributions. Finally, monitoring peak heights allows for a more realistic and assumption-free determination of the LoD.