Preparation and Some Properties of Nanostructural Rare Earth Nitrides by Using the Reaction of Hydrides with Ammonia
Bibliographic record
Abstract
By the use of the thermal decomposition of rare earth (RE) amides to nitrides, the preparation of nanostructural RE nitrides (CeN, PrN, NdN, SmN, GdN, TbN, DyN, HoN and ErN) was extensively studied in search of optimal conditions.In this method, the preparation of amides as an effective precursor for the nitrides was similarly of importance; the amide was here prepared by high-pressure reactions of RE hydrides with ammonia in an autoclave or reactive ball milling of the RE hydrides and ammonia.CeN, PrN, NdN and SmN were successfully prepared by the thermal decomposition of the RE amides or amide-like compounds formed by the reaction of the dihydrides with ammonia, whereas GdN, TbN, DyN, HoN and ErN were not obtained in this way.The dihydrides of gadolinium, terbium, dysprosium, holmium and erbium were generally too stable to react with ammonia to form the amides compared to those of cerium, praseodymium, neodymium and samarium.In the preparation of CeN, PrN and SmN, the amide precursors obtained by the autoclave reaction of the dihydrides with ammonia yielded nanostructural nitrides with higher surface areas (3.9-5.9 m 2 /g), compared to those obtained by the reactive ball milling method.
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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".