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., MnxSn1–xS2 (x = 0.1–0.2), as derivatives of K2xMnxSn1–xS2. 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, K2xMnxSn1–xS2 is treated with I2 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 (P3̅m) featuring hexagonal slabs with AA stacking and appropriate interlayer space along the c axis. Upon shaking, both MnxSn1–xS2 (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. MnxSn1–xS2 nanosheets can be viewed as a magnetically doped derivative of SnS2, 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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".