Magnetic Nanosheets via Chemical Exfoliation of K<sub>2<i>x</i></sub>Mn<sub><i>x</i></sub>Sn<sub>1–<i>x</i></sub>S<sub>2</sub>
Bibliographic record
Abstract
We report two chemical routes to prepare new magnetically doped two-dimensional (2D) tin sulfides, i.e., Mn x Sn 1– x S 2 ( x = 0.1–0.2), as derivatives of K 2 x Mn x Sn 1– x S 2 . Initial codoping with K ensures high Mn doping in the nanosheets and is additionally aiding exfoliation. In chemical route 1, the parent compound is first reacted with a diluted HCl solution, which dissolves all K and half the Mn atoms, followed by treatment with TEAOH/methanol, which results in exfoliation. In chemical route 2, K 2 x Mn x Sn 1– x S 2 is treated with I 2 in acetonitrile, leading to the removal of almost all K atoms, followed by dispersing the product in dimethylformamide, which results in partial exfoliation. The composition and structures are characterized using scanning electron microscopy, high-resolution scanning transmission electron microscopy, and selected area electron diffraction. After K removal and before exfoliation, the structure is trigonal ( P 3̅ m ) featuring hexagonal slabs with AA stacking and appropriate interlayer space along the c axis. Upon shaking, both Mn x Sn 1– x S 2 ( x = 0.1–0.2) phases can be exfoliated into thinner flakes, but nanosheets with a thickness of 2.5 nm can only be achieved via chemical route 1 . Magnetic susceptibility measurements of the parent, intermediate, and restacked exfoliated products reveal that the manganese magnetic moment saturates at a moderate field, only in the exfoliated material. Mn x Sn 1– x S 2 nanosheets can be viewed as a magnetically doped derivative of SnS 2, a material that is used as a high on/off ratio transistor, photocatalyst, and electrode material. Therefore, the new layered metal sulfides obtained here may have diverse applications in 2D magnetism, catalysis, or batteries.
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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".