Air-Stable Short-Wave Infrared Tin Telluride and Tin Telluride/Zinc Telluride Core–Shell Colloidal Quantum Dots
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide To date, colloidal quantum dots (CQDs) with absorption in the short-wave infrared region (SWIR, 1–2.6 μm) typically consist of hazardous cadmium, lead, or mercury chalcogenides, which limit commercial acceptance. Environmentally friendly alternatives are therefore required to ensure minimal damage to ecosystems during fabrication, use, and disposal. A promising hazardous-element-free SWIR absorbing nanomaterial candidate is tin chalcogenide. We have developed tin telluride (SnTe) CQDs, with a size ranging from ∼17 to 26 nm and corresponding absorption peak from ∼2.3 to 2.5 μm, indicative of size-dependent quantum confinement. Air-stable tin salts (tin chloride or tin acetate) were employed instead of the typical air-sensitive bis(bis(trimethylsilyl)amino tin(II). The synthesis was systematically investigated by optimizing the ligand type (1-dodecanethiol was used to replace oleic acid to prevent oxidation), injection method, growth temperature, reaction time, and feed molar ratio between tin and tellurium precursors. To improve stability in air, a ZnTe shell was successfully synthesized via cation exchange reaction at 70 °C with zinc acetate. The SnTe/ZnTe core–shell nanocrystals were fully characterized, revealing the formation of a protective ZnTe shell with a thickness of two to three monolayers, resulting in long-term stability in air (up to 1 month). These air-stable CQDs may offer a low-toxicity alternative nanomaterial for low-cost solution-processable fabrication of SWIR optoelectronics.
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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".