Quantification of titanium dioxide (TiO<sub>2</sub>) anatase and rutile polymorphs in binary mixtures by Raman spectroscopy: an interlaboratory comparison
Bibliographic record
Abstract
Abstract This article presents an interlaboratory comparison (ILC) on Raman spectroscopy as a technique for relative quantification of the two most common polymorphs of titanium dioxide (TiO2)—anatase and rutile—in binary mixtures. Some standard methods are currently employed internationally for the determination of TiO2 content in samples (ISO 591-1, ASTM D3720-90), but require extensive sample preparation, do not distinguish between the two polymorphs or are accurate only for small fractions of either polymorph. Raman spectroscopy is a well-suited characterization technique for measuring and differentiating TiO2 in a fast, non-invasive way, while requiring no particular reagent or sample preparation. Eleven international participants conducted the study under the framework of Versailles Project on Advanced Materials and Standards. The collected data was analyzed by means of partial least squares regression after spectral preprocessing. The resulting models all show discrepancies of lower than 2% from the nominal values in the quantitative analysis over the concentration range of 5%–95% mixture fractions, with many datasets showing substantial improvement margins on this figure. The results of this ILC provide validation of Raman spectroscopy as a reliable method for quantification of TiO2 phases.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".