Software Methods and Tools for WDS Light Element Analysis
Bibliographic record
Abstract
Wavelength Dispersive Spectrometry (WDS) quantitative analysis of light (low Z) elements “ticks all the boxes”: large absorption corrections, peak shape/shift effects from chemical and valence effects, absorption effects from “unanalyzed” elements, time dependent intensity (TDI) effects due to beam sensitivity/ion migration, conductive coating effects and standard selection choices can all affect analytical accuracy of light elements at major concentrations. WDS quantitative analysis of light elements at trace levels brings into play additional effects such as non-linear background shapes and spectral interferences. We will examine these analytical issues for both major and trace levels of light elements and review what software methods and tools can aid in obtaining accurate light element analyses with WDS EPMA. Matrix correction accuracy, especially regarding the absorption correction is particularly important for low energy emissions lines, and generally requires the application of empirically determined mass absorption coefficients for best accuracy. Although peak shape/shift effects from chemical bonding effects can be dealt with in light element WDS by acquisition of integrated peak intensities, this is slow and reduces precision. Peak shape and shift changes between the primary standard and unknown samples must therefore usually be corrected for by applying either specified (measured) or compound (summation of binary compounds) area peak factors (APFs) using the method of Bastin (1992) [1]. An example of a specified area peak factor determination for F Kα from CaF2 (std) to BaF2 (unk) is shown in Figure 1. Correction of intensity changes over time or time dependent intensity (TDI) effects can often be important for some light elements, such as oxygen in oxides and/or water/hydroxyl in glasses and also trace carbon measurements due to hydrocarbon contamination. For instance, changes in oxygen intensities over time in a hydrous glass (Withers-N5) bombarded by a 15 keV, 10 nA electron beam, 20 µm diameter are shown in Figure 2. The accuracy of light element analyses can be tested in various ways. One such test involves measuring the concentration of water in a glass by first determining the total oxygen in the glass, then calculating the oxygen from cation stoichiometry (assuming a specific ferric/ferrous stoichiometry for Fe), and finally converting the excess (or deficit) oxygen to water or hydroxyl [2]. Table 1 shows the results for a number of synthetic glasses with water contents ranging from zero to ∼5 wt%. The analyses were corrected for intensity change over time (Na, K, Si and O) using MgO as the primary oxygen standard and correcting for peak shape differences from MgO using compound area-peak-shape factors (APFs) calculated from measured binary factors [1]. Note that the sodium volatile corrections were fairly large even with a 10 nA and 20 um beam. Also note that the oxygen intensity did decrease even more significantly than the Si intensity as the H2O content increased. The last two glasses (N4b and N5) had “volatile” corrections for Na of about 100% which is a large correction for accuracy. The “H2O w/o blank” column shows the results with all of the corrections applied except for the “iterated blank” correction. The accuracy issues for H2O are clearly visible compared with the Fourier Transform Infrared (FTIR) measurements. The “H2O w/ blank” shows the results with the Donovan et. al. (2011) [3] “iterated blank” correction applied to oxygen for all samples based on the NBS K-411 glass oxygen concentration. The oxygen concentration of this SRM glass standard had been previously adjusted for excess oxygen from photometry for a total oxygen concentration of 43.558 wt%. These examples demonstrate the complexity and significance of accurate corrections in quantifying light elements by EPMA, as evidenced by the analysis of water and light element contents in synthetic glasses and other compounds, and the necessity of careful correction techniques to ensure analytical accuracy. Area peak factor determination for F Kα from CaF2 to BaF2. The F Kα peak shapes (peak to area intensity ratios) are significantly different between these two compounds. The higher the spectral resolution of the diffractor, the more the APF factor diverges from unity (e.g., TAP vs. LDE). TDI measurements of the O Kα X-ray line measured on a Withers-N5 hydrous glass by a 15 kV, 10 nA electron beam and 20 µm diameter electron beam. Extrapolation of the intensity to time t = 0 is used to determine the original intensity before ion migration or volatilization effects begin. Quantification results of water and light elements in Withers hydrous glass specimens. Analytical conditions: 40 degrees takeoff angle, 15 keV, 10 nA, 20 µm electron beam. “H2O (FTIR)” are the nominal and FTIR H2O wt% values from Tony Withers respectively. “VOL%” values are the relative percent TDI (time dependent intensity) correction for Na, K, Si and O. “H2O w/o blank” are the H2O values derived from measured excess oxygen concentrations based on hydrogen stoichiometry without a blank correction. “H2O w/ blank” are the H2O values derived from measured excess oxygen concentrations based on hydrogen stoichiometry included in matrix correction and with use of the K-411glass standard as a “blank” correction. Quantification results of water and light elements in Withers hydrous glass specimens. Analytical conditions: 40 degrees takeoff angle, 15 keV, 10 nA, 20 µm electron beam. “H2O (FTIR)” are the nominal and FTIR H2O wt% values from Tony Withers respectively. “VOL%” values are the relative percent TDI (time dependent intensity) correction for Na, K, Si and O. “H2O w/o blank” are the H2O values derived from measured excess oxygen concentrations based on hydrogen stoichiometry without a blank correction. “H2O w/ blank” are the H2O values derived from measured excess oxygen concentrations based on hydrogen stoichiometry included in matrix correction and with use of the K-411glass standard as a “blank” correction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.028 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".