Analysis of pharmaceutical tablets by laser ablation inductively coupled plasma atomic emission spectrometry and mass spectrometry (LA-ICP-AES and LA-ICP-MS)Electronic supplementary information (ESI) available: images of the ablation craters and trenches in the tablets. See http://www.rsc.org/suppdata/ja/b3/b314732k/
Bibliographic record
Abstract
Laser ablation was studied with inductively coupled plasma atomic emission spectrometry (LA-ICP-AES) and with inductively coupled mass spectrometry (LA-ICP-MS) for the analysis of pharmaceutical tablets (10% and 20% Neusilin). For spot analysis with LA-ICP-AES, precision ranged from 12–31% relative standard deviation (RSD), but improved to 1–6% when ratios of signals were used. For continuous scanning, the precision ranged from 1–7% RSD. Weaker laser conditions required for ICP-MS gave precisions of 47–61% RSD (29% when signal ratios were used). Under unoptimized conditions, the detection limits for LA-ICP-AES of tablets were 70 µg g−1 for Al and 20 µg g−1 for Mg. The detection limits for LA-ICP-MS were 40 µg g−1 for Al and 6 µg g−1 for Mg. These results suggest that LA-ICP spectroscopy may find application in tablet analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".