Quick Determination of Fourteen Rare Earth Elements in Powdered Refractory Materials Using Electrothermal Vaporization with Detection by Inductively Coupled Plasma Optical Emission Spectrometry
Bibliographic record
Abstract
The determination of rare earth elements (REEs) in refractory materials by inductively coupled plasma (ICP) spectrometry with conventional sample introduction using pneumatic nebulization requires the dissolution of these samples, which is difficult. This work presents an optimized method using electrothermal vaporization (ETV) coupled to ICP optical emission spectrometry (OES) for the direct determination of REEs in refractory geological materials. Solid sampling eliminates the dissolution step, thereby increasing sample throughput. Furthermore, the small sample mass required with ETV allows faster analysis with greater sensitivity than with nebulization by eliminating the dilution inherent to dissolution and introducing more sample into the plasma compared to nebulization. Point-by-point internal standardization with an argon emission line compensates for the visible sample loading effect on the plasma. Multivariate optimizations of the carrier, bypass, and CF 4 reaction gas flow rates, along with optimization of the pyrolysis temperature and cooling time between the pyrolysis and vaporization steps, enhanced analyte signals by 2.9–11 times compared to a previous ETV-ICPOES method for the analysis of slag. Adding at least 30 μL of high-purity water to the graphite boat containing solid samples enhanced sensitivity by 61% on average and enabled external calibration using standard solutions, which provided accurate results for 14 REEs (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tm, Y, and Yb). In contrast, only REEs with certified concentrations in the CRMs encompassing those in the sample could be determined by using CRMs for external calibration.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".