Structural control of Na<sub>2</sub>TiO<sub>3</sub> in pre‐treating natural rutile ore by alkali roasting for TiO<sub>2</sub> production
Bibliographic record
Abstract
To improve the use of natural rutile ore with fine particle size and high contents of MgO and CaO in China, a novel pre‐treatment method of natural rutile ore by NaOH roasting to prepare α ‐Na 2 TiO 3 for TiO 2 production was proposed. γ ‐Na 2 TiO 3 is a stable by‐product that can lead to serious caking in the reactor and low titanium yield. Thus, structural control in alkali roasting is necessary. The influence of particle size of natural rutile ore, NaOH‐to‐ore mass ratio, roasting temperature and roasting time on the crystal forms of Na 2 TiO 3 was systematically investigated. The optimized reaction parameters were as follows: average particle size of natural rutile ore, −9.2 µm; NaOH‐to‐rutile mass ratio, 1.2; roasting temperature, 550 °C; and roasting time, 70 min. In these conditions, the titanium conversion and the fraction of α ‐Na 2 TiO 3 exceeded 99.5 % and 98 %, respectively. Moreover, the addition of H 2 O or as‐prepared partial α ‐Na 2 TiO 3 as crystal seeds into the reaction system could prepare the roasting product with α ‐Na 2 TiO 3 as its main phase using −45 µm (−325 mesh) natural rutile ore.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".