Multielemental Laser-Enhanced Ionization Spectrometry for the Determination of Lead at the Trace Level in Pelletized Coal Using Laser Ablation and Internal Standard Signal Normalization
Bibliographic record
Abstract
Laser ablation laser-enhanced ionization (LA-LEI) was used for the simultaneous measurement of lead and indium in pelletized graphite and coal samples. UV Laser ablation of the solid samples was performed in an ablation cell and the ablated material was carried by a flow of gas to a miniature LEI flame where lead was detected. The influence of parameters such as binder content of the solid pellets and dispersion of the analytes spiked in the sample material, as well as the number of ablation pulses per crater on signal repeatability and on the size and shape of ejected particles was examined. Measurement repeatability values of 2 to 5% of relative standard deviation were obtained using indium as an internal standard to correct for variations in the ablation rate. A limit of detection of 120 nanograms per gram was calculated for the determination of Pb in high-purity graphite. The Pb concentration in the NIST 1632c Bituminous Coal certified reference material was determined to within 1% of its certified value, using graphite as the matrix-matching material and In as the internal standard in pelletized solid samples.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".