A sensitive, rapid and inexpensive method to assess aluminium(III) ions in technetium eluates
Bibliographic record
Abstract
The aim of the study was to validate a semiquantitative analytical method to identify the aluminium(III) [Al(III)] concentration in 99Mo/99mTc generator eluates to check the European Pharmacopoeia (Ph. Eur.) requirement (<5 μg/ml). Three different solutions measuring 20 ml - 0.2% 1,10-phenanthroline, 0.05% chrome azurol S and 20% hexamethylenetetramine - were prepared. A cellulose filter paper was subsequently immersed in them, dried overnight at room temperature and cut into rectangles. A volume of 5 μl of first eluates of various 99Mo/99mTc generators was placed onto a reagent paper and the spot colour was compared with a standard aluminium solutions scale (0-100 μg/ml). A cyan/magenta/yellow/key (CMYK) model analysis was adapted to quantify the intensity of colour on the paper, and the presence of aluminium in the eluates was detected by a spectrophotometer. Small changes in standard solution pH (4.1-5.2) and chrome azurol S concentration did not affect the analysis. The cyan channel image analysis was proportional to the Al3+ solution concentration (y=25 019x+1489, R2=0.9554 within 2.5-8 μg/ml). The detection limit for aluminium by the visual test method is about 1 μg/ml, and fading is absent. The cyan channel image analysis method is independent of the observer and is applicable for the evaluation of the chemical purity of 99Mo/99mTc generator eluates. Our colorimetric 'spot test' is advantageous for the visual evaluation of Al pertechnetate concentrations as required by Ph. Eur. showing a sensitivity and a limit of detection superior to that of commercially available spot systems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".