Surface Oxidation of 2D γ‐Indium Sulfide Nanoplatelets and Its Impact on Photoluminescence
Bibliographic record
Abstract
Two‐dimensional (2D) semiconductor materials are being explored for applications including photocatalysis and optoelectronic devices. This class of materials features high surface‐to‐volume ratio that imparts unique physical properties. Yet, this feature makes 2D semiconductor materials prone to chemical changes that translate to variations in their photophysical behavior. Large‐scale application of these materials requires an understanding of degradation processes toward developing strategies to increase their stability. Here, the effect of air exposure on underexplored 2D semiconductor material γ‐In2S3 is studied. This In2S3 polymorph is stabilized as nanoplatelets, which are then characterizes before and after exposure to air. A marked surface oxidation , accompanied by drastic variations in photoluminescence is observed. Temperature‐dependent spectroscopic measurements show that the emission spectrum of fresh γ‐In2S3 nanoplatelets is dominated by two defect‐related bands, while oxidation enhances the contribution of a third band. An energy level scheme is proposed based on the analysis of the spectroscopic data. This study finally portrays how a postsynthesis treatment with an oxygen‐containing molecule (oleic acid) changes the photoluminescence of γ‐In2S3 nanoplatelets similarly to a prolonged air exposure. The work shines light on the optical properties of γ‐In2S3, paving the way for their control and the use of this material in luminescence sensing.
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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".