The Research of Preparation Process of Tungsten Oxide with Enriched 186W Isotope
Bibliographic record
Abstract
Enriched tungsten-186 oxide is one of the stable isotope products of tungsten. It is used to prepare the radioactive medical isotope product 188WO3, which can be used as the target material of 188W/188Re generator to produce medical 188Re isotope product, so as to meet the increasing requirements of biomedical markers, bone cancer treatment and other medical related fields. In order to satisfy the current market demand for 186WO3, the preparation process of 186WO3 with tungsten-186 hexafluoride (186WF6) as the raw material was studied and determined. The key parameters of preparation processes were systematically optimized, and the influence of essential parameters on the 186WO3 product purity and process yield was obtained, which determined the optimal process parameters. Finally, the high conversion rate process route was achieved, and the enriched 186WO3 products were obtained. The chemical purity of 186WO3 can reach 99.91%-99.93% with the process yield of 89.8%-90.5% under the optimal process conditions. The research consequences of this article can provide a theoretical and experimental basis for the batch preparation of 186WO3, and give the technical reference for the process of preparing metal oxides from other similar gas-phase fluorides.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".