π‐Phase Tin and Germanium Monochalcogenide Semiconductors: An Emerging Materials System
Bibliographic record
Abstract
Cubic π-phase monochalcogenides (MX, M = Sn, Ge; X = S, Se) are an emerging new class of materials that has recently been discovered. Here, their thermodynamic stability, progress in synthetic routes, properties, and prospective applications are reviewed. The thermodynamic stability is demonstrated through density functional theory total energy and phonon spectra calculations, which show that the π-phase polytype is stable across the monochalcogenide family. To date, only π-phase tin monochalcogenides have been observed experimentally while π-phase Ge-monochalcogenides are predicted to be stable but are yet to be experimentally realized. Various synthetic preparation protocols of π-SnS and π-SnSe are described, focusing on surfactant-assisted nanoparticle synthesis and chemical deposition of thin films from aqueous-bath compositions. These techniques provide materials with different surface energies, which are likely to play a major role in stabilizing the π-phase in nanoscale materials. The properties of this newly discovered family of semiconducting materials are discussed in comparison with their conventional orthorhombic polymorphs. These could benefit a number of photovoltaic and optoelectronic applications since, apart from being cubic, they also possess characteristic advantages, such as moderately low toxicity and natural abundance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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; 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".