Determination of the Isotopic Composition of Indium by MC-ICP-MS Using an Improved Measurement Model for the Gravimetric Isotope Mixture Method
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In this study, we report the first independent and primary isotope ratio measurement of indium by MC-ICP-MS, utilizing gravimetric isotope mixtures of two near-pure enriched indium isotopes. Consistent with previous findings, we observe that the traditional one-mixture-at-a-time calibration approach can introduce a strong dependence of the resulting isotope ratio correction factors on the composition of the mixtures. Our analysis suggests that this is due to biases inherent in measuring the isotopic composition of pure isotopic materials. To address this issue, we propose an improved calibration strategy that omits the use of isotope ratios measured in the two near-pure indium isotopes, at the expense of measuring additional isotope mixtures. The revised calibration approach yields an indium isotope ratio n ( 113 In)/ n ( 115 In) = 0.044 655 ± 0.000 009 (95% confidence level) for the high-purity indium isotopic reference material (NRC HIIN-1). This result is in agreement with our 2010 result (0.044 72 ± 0.000 19, 95% confidence level), obtained using the regression method with NIST SRM978a silver as a calibrator, while showing an improvement in measurement uncertainty by an order of magnitude.
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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".