Speciation of Chromium by High-Performance Thin-Layer Chromatography with Direct Determination by Laser Ablation Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
It is of considerable importance to be able to distinguish metallic species because their toxicity depends on their chemical form. Therefore, the analysis of environmental samples can be enhanced by the combination of high-performance thin-layer chromatography (HPTLC) with laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS). In this study, Cr (3+) and Cr (6+) were separated on silica gel HPTLC plates using aqueous mobile phases. Separation was achieved in seconds with retardation factors ( R f ) of 0 and 1 for Cr (3+) and Cr (6+), respectively. LA was used to volatilize the chromium species directly from the chromatographic material prior to ICPMS detection. A linear calibration was obtained, and detection limits (3sigma) of 6 ng for Cr (6+) and 0.4 ng for Cr (3+) were achieved with precision ranging from 3 to 40% at the 95% confidence level. The silicon present in the stationary phase was used as an internal standard. This procedure allows for a rapid separation and quantification, requires only 0.5 muL of sample, and lower detection limits can be achieved through preconcentration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".