In Pursuit of a Greener Fluorinated Chemical Modifier for the Direct Determination of Rare Earth Elements in Refractory Geological Materials using ETV-ICPOES
Bibliographic record
Abstract
Rare earth elements (REEs) have critical industrial and research applications.Accurately determining REEs can be challenging, as these elements are typically found in refractory ore samples.These ores are difficult to dissolve, making analysis via traditional pneumatic nebulization (PN) methods troublesome.For this reason, a solid sample introduction system and a direct analysis method are preferred.In prior studies, electrothermal vaporization coupled to inductively coupled plasma optical emission spectrometry (ETV-ICPOES) has shown promise for the analysis of geological samples.One of the challenges with REEs is that they are prone to carbide formation, which can complicate accurate determination.However, halogenated reagents have been shown to prevent carbide formation and enhance the volatility of analytes.Past work used carbon tetrafluoride (CF4) gas to introduce halogens to the sample.In this work, polytetrafluoroethylene (PTFE) powder was mixed with the sample prior to analysis instead.This study demonstrates that premixing PTFE powder with the sample reduces the relative standard deviations (RSD) compared to individually mixing aliquots in a small graphite boat prior to each replicate measurement.A 5:3 ratio of sample-to-PTFE powder maximized sensitivity.To compensate for sample loading effects on the plasma, an Ar emission line (404.442nm) was used for internal standardization.Given that fluorinated gases can have ozone-depleting effects, using PTFE powder instead of CF4 is much more environmentally friendly.It is also less expensive.Although sensitivity was highest when using CF4 as a chemical modifier for Ce, Er, Gd, Ho, La, Lu, Nd, Sc, Tb, and Y (10 out of 16 REEs), by 3 (Ho) to 19 (Nd) fold versus when using PTFE, the limit of detection (LOD) was lowest when using PTFE powder for Dy, Er, Eu, Ho, Pr, Tm, Y, and Yb (8 out of 16 REEs), by 2 (Er) to 12 (Y) fold, because of a significantly lower background signal than with CF4.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".