Probe sonication-assisted rapid synthesis of highly fluorescent sulfur quantum dots
Bibliographic record
Abstract
A new type of heavy-metal free single-element nanomaterial, called sulfur quantum dots (SQDs), has gained significant attention due to its advantages over traditional semiconductor QDs for several biomedical and optoelectronic applications. A straightforward and rapid synthesis approach for preparing highly fluorescent SQDs is needed to utilize this nanomaterial for technological applications. Until now, only a few synthesis approaches have been reported; however, these approaches are associated with long reaction times and low quantum yields (QY). Herein, we propose a novel optimized strategy to synthesize SQDs using a mix of probe sonication and heating, which reduces the reaction time usually needed from 125 h to a mere 15 min. The investigation employs cavitation and vibration effects of high energy acoustic waves to break down the bulk sulfur into nano-sized particles in the presence of highly alkaline medium and oleic acid. In contrast to previous reports, the obtained SQDs exhibited excellent aqueous solubility, desirable photostability, and a relatively high photoluminescence QY up to 10.4% without the need of any post-treatment. Additionally, the as-synthesized SQDs show excitation-dependent emission and excellent stability in different pH (2-12) and temperature (20 °C-80 °C) environments. Hence, this strategy opens a new pathway for rapid synthesis of SQDs and may facilitate the use of these materials for biomedical and optoelectronic applications.
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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".