the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Oct 2024
The quantification of downhole fractionation for laser ablation mass spectrometry
Abstract. Downhole fractionation (DHF), a known phenomenon in static spot laser ablation, remains one of the most significant sources of uncertainty for laser-based geochronology. A given DHF pattern is unique to a set of conditions, including material, inter-element analyte pair, laser conditions, and spot volume/diameter. Current modelling methods (simple or complex linear models, spline-based modelling) for DHF do not readily lend themselves to uncertainty propagation, nor do they allow for quantitative inter-session comparison, let alone inter-laboratory or inter-material comparison.
In this study, we investigate the application of orthogonal polynomial decomposition for quantitative modelling of LA-ICP-MS DHF patterns with application to an exemplar U–Pb dataset across a range of materials and analytical sessions. We outline the algorithm used to compute the models and provide a brief interpretation of the resulting data. We demonstrate that it is possible to quantitatively compare the DHF patterns of multiple materials across multiple sessions accurately, and use uniform manifold approximation and projection (UMAP) to help visualise this large dataset.
We demonstrate that the algorithm presented advances our capability to accurately model LA-ICP-MS DHF and may enable reliable decoupling of the DHF correction for non-matrix matched materials, improved uncertainty propagation, and inter-laboratory comparison. The generalised nature of the algorithm means it is applicable not only to geochronology but also more broadly within the geosciences where predictable linear relationships exist.
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Status: open (until 25 Nov 2024)
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RC1: 'Comment on egusphere-2024-2908', Anonymous Referee #1, 15 Nov 2024
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While I am not in a position to critically evaluate the mathematical models used to quantify Differential Heating Factor (DHF) in this extensive dataset, the results presented here are compelling. Having previously analyzed a similar dataset and examined DHF patterns in U-Pb dating across various minerals, I am often struck by the surprising variability between them. The physical mechanisms underlying DHF remain somewhat ambiguous, which makes this study’s methodology and findings—particularly in the context of “Big Data”—valuable to the community engaged in LA-ICP-MS U-Pb dating methods.
Drawing from my experience, I believe certain aspects warrant additional discussion. Ideally, expanding this impressive dataset with data from alternative instrumentation (both laser ablation and ICP-MS systems) would mitigate some current limitations of the study, though it would likely raise additional questions as well.
Several potential limitations and influential factors in DHF quantification, as presented in this work, could benefit from a more detailed discussion. These include aspects such as signal duration, differences in instrumentation and operator handling, sample heterogeneity, inclusions, focal position accuracy, and variation in detector cross-calibration and dead time.
Lines 82-85: “The independence and physical meaning of the lambda coefficients allows them to be used to quantitatively compare independent fits (e.g., single analyses, materials, analytical sessions, differing laboratories) so long as other parameters (e.g., fluence, spot diameter/volume, laser wavelength) are considered.”
How are these other parameters accounted for within the analysis framework? What impact would neglecting them have on the method’s accuracy and reliability?Is the centering of time-dependent ratios influenced by signal duration, or is it only lambda 0 that is sensitive to such changes, potentially due to ICP tuning dependencies? Furthermore, how does total analysis time (with data presented at 30 and 40 seconds) impact the DHF pattern once data are centered for further calculation of lambda components (lambda 1, 2, 3, and 4) and subsequent UMAP visualization? If lambda components exhibit different characteristics during different parts of the signal—such as a linear trend dominating the initial 10 seconds and a higher-order trend thereafter—then the lambda coefficients would differ for signal durations of 20, 30, or 40 seconds. Consequently, the same analysis might plot differently in UMAP depending on signal duration. This warrants further discussion.
The statement, “and for Wilberforce, the steeper linear DHF component and larger uncertainty are due to inclusion of several points from some analyses that are highly leveraging the fit, even with automated outlier removal being applied,” suggests that inclusions and heterogeneity (particularly variable initial Pb content, as in Apatite) within reference materials can influence the DHF pattern beyond what outlier removal can address. How is this handled in your analysis? A more in-depth discussion would be beneficial.
This study is based on data from a single laboratory using a single laser ablation system and two similar ICP-MS instruments likely operated or trained by a single Lab Manager. The robustness of DHF quantification could be enhanced by incorporating data from different laser ablation systems, which vary in wavelength, pulse width, energy density, and ablation cell design, as well as different ICP-MS instruments. Please discuss how this limitation might affect the generalizability of the quantification.
Additionally, ICP-MS instruments employ various detection modes that require cross-calibration. How is it ensured that ablation signals—where intensity generally decreases during single-hole ablation—are unaffected by potential cross-calibration errors that could impact ratio measurements?
Minor comments:
Line 33: “As DHF is a volume- dependant spot-ablation phenomena…” I would rather state “ As DHF is a crater geometry dependant ….”
Line 216 there is an E missing in “(𝜆3) range from -1.08E-5 to +2.15-5,”
Citation: https://doi.org/10.5194/egusphere-2024-2908-RC1
Data sets
Raw and derived data: The quantification of downhole fractionation for laser ablation mass spectrometry Jarred Lloyd and Sarah Gilbert https://doi.org/10.25909/26778298
Supplementary analyte signal figures - The quantification of downhole fractionation for laser ablation mass spectrometry Jarred Lloyd https://doi.org/10.25909/26778592
Supplementary Figures - The quantification of downhole fractionation for laser ablation mass spectrometry Jarred Lloyd https://doi.org/10.25909/27041821
Model code and software
Julia scripts - The quantification of downhole fractionation for laser ablation mass spectrometry Jarred Lloyd https://doi.org/10.25909/26779255
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