Determination of platinum group elements in environmental samples using in-line mini-column pre-concentration and separation coupled to inductively coupled plasma mass spectrometry
Bibliographic record
Abstract
A method for the determination of platinum group elements (PGEs) in natural tree samples was developed. An alumina column in-line with inductively coupled plasma mass spectrometry (ICP-MS) achieves the separation of interferents as well as pre-concentration of the analytes. The application of this proposed method on tree top samples displayed an effective separation of Ru, Rh, Re, Pd, Ir and Pt from the interferents, Ni, Cu and Zn for quantitative analysis of the analytes. The concentration data was compared to ICP-HRMS data and while it was difficult to determine whether the concentrations were in agreement or not, as both methods have a large degree of error. However, both methods displayed elevated concentrations of PGEs in areas over geological conductors in Rock Lake, Manitoba. This proposed method offers distinct advantages over previous on-line methods, as it is extended to include multiple PGEs as well as reduces sample consumption to a more suitable volume for natural samples. While the detection limit is higher than previous methods due to the lowered sample volume, it is still lower than the detection limits reported in commercial laboratories. ii Acknowledgements I would like to thank Dr. Beauchemin and Dr. Kyser, my supervisors first and foremost for their patience and guidance. To all the members of the Beauchemin lab, who helped figure out ICP-MS problems, and to Kim Wiegand who helped tremendously by assisting me with analysis while I was away on maternity leave. I’d like to thank the members of
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".