Topotactic Solid-State Metal Hydride Reductions of Sr<sub>2</sub>MnO<sub>4</sub>
Bibliographic record
Abstract
We report novel details regarding the reactivity and mechanism of the solid-state topotactic reduction of Sr2MnO4 using a series of solid-state metal hydrides. Comprehensive details describing the active reducing species are reported and comments on the reductive mechanism are provided, where it is shown that more than one electron is being donated by H(-). Commonly used solid-state hydrides LiH, NaH, and CaH2, were characterized in terms of reducing power. In addition the unexplored solid-state hydrides MgH2, SrH2, and BaH2 are evaluated as potential solid-state reductants and characterized in terms of their reductive reactivities. These 6 group I and II metal hydrides show the following trend in terms of reactivity: MgH2 < SrH2 < LiH ≈ CaH2 ≈ BaH2 < NaH. The order of the reductants are discussed in terms of metal electronegativity and bond strengths. NaH and the novel use of SrH2 allowed for targeted synthesis of reduced Sr2MnO(4-x) (0 ≤ x ≤ 0.37) phases. The enhanced control during synthesis demonstrated by this soft chemistry approach has allowed for a more comprehensive and systematic evaluation of Sr2MnO(4-x) phases than previously reported phases prepared by high temperature methods. Sr2MnO3.63(1) has for the first time been shown to be monoclinic by powder X-ray diffraction and the oxidative monoclinic to tetragonal transition occurs at 450 °C.
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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".