Scalable Bulk Synthesis of Phase‐Pure <i>γ‐</i>Sn<sub>3</sub>N<sub>4</sub> as a Model for an Argon‐Flow‐Mediated Metathesis Reaction
Bibliographic record
Abstract
Abstract Nitrides represent a promising class of materials for a variety of applications. However, bulk synthesis remains a challenging task due to the stability of the N 2 molecule. In this study, we introduce a simple and scalable approach for synthesizing nitride bulk materials. Moderate reaction temperatures are achieved by using reactive starting materials, slow and continuous mixing of the starting materials, and by dissipating heat generated during the reaction. The impact on the synthesis of using different starting materials as nitrogen source and the influence of a flux were examined. γ‐ Sn 3 N 4 was selected as the model compound. The synthesis of pure γ ‐Sn 3 N 4 bulk material on a large scale has still been a challenge, although a few synthesis methods were already described in the literature. Here we synthesized γ ‐Sn 3 N 4 by metathesis reaction of argon‐diluted SnCl 4 with Li 3 N, Mg 3 N 2 or Ca 3 N 2 as nitrogen sources. Products were characterized by powder X‐ray diffraction, scanning and transmission electron microscopy, energy‐dispersive X‐ray spectroscopy, dynamic flash combustion analysis, hot gas extraction analysis, X‐ray photoelectron spectroscopy, Mössbauer spectroscopy and X‐ray absorption and emission spectroscopy. Additionally, single‐crystal diffraction data of γ‐ Sn₃N₄, previously unavailable, were successfully collected.
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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".