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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".