Determination of trace elements in titanium oxides by wavelength dispersive x‐ray fluorescence spectrometry (WD‐XRF)
Bibliographic record
Abstract
Abstract TiO 2 is used in a great variety of industries (foods, medicines, cosmetics, etc.). In food industry, although the use of TiO 2 as additive was banned by EU in 2022, it is still authorized in medicinal products, and is allowed as food ingredient in US and Canada. Focusing on cosmetics, regulations state some forbidden elements (As, Cd, Ni, Hg, Sb, and Pb), and others allowed with a specific limit (Co, Cr, and Se). Most researches about TiO 2 characterization are focused on the purity determination and no studies analyzing trace metals in this material have been found. Due to the potential impact of those trace elements on health and safety, a robust method for determining them in TiO 2 is required. A methodology for the determination of As, Cd, Co, Cr, Hg, Ni, Pb, Sb, and Se at trace level in TiO 2 by WD‐XRF has been developed. Sample was prepared as pressed pellets to achieve low limits required by regulations, and the best conditions were established using n‐butyl methacrylate as binder and plastic spatula to avoid Cr contamination coming from the stainless‐steel one. An in‐depth inquiry conducted to get calibration and validation standards revealed a lack of reference materials; therefore, additions of pure oxides of each element were made to high‐purity TiO 2 . Validation was performed by two means: analyzing synthetic standards prepared as stated and analyzing two commercial TiO 2 by an independent method (ICP‐OES). The developed methodology was suitable to be used as control method to assess whether the materials meet the regulations, since time required to undertake the analysis is much less than other methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".