Structural origins of carbon quantum dot luminescence by synchrotron x-ray spectroscopy
Bibliographic record
Abstract
Abstract A carbon quantum dot (CQD) sample series was synthesized from citric acid and varying concentrations of thiourea. The highest (sample 1) and lowest (sample 2) concentrations of thiourea exhibited unique visual effects and electronic structures. X-ray excited optical luminescence (XEOL) along with UV-visible spectroscopy provided unique insight into the absorption and emission mechanisms of samples 1 and 2, where only sample 2 emitted XEOL. Sample 1 exhibited the commonly observed aggregation caused quenching (ACQ) effects in the solid state. While sample 2 displayed unique aggregation induced emissions (AIE) effects upon exciting the sample above the C K edge. The AIE and ACQ sample differences were suspected to be from S moiety differences arising from the varying thiourea concentrations during synthesis. Furthermore, x-ray absorption spectroscopy (XAS) in modes of total electron yields (TEY) and partial fluorescence yields (PFY) allowed the identification of specific core and surface states of the CQDs. It was discovered that thiophene moieties were uniquely formed in the AIE sample’s surface and not anywhere in the ACQ CQD sample. The thiophene surface functionality is believed to be a significant contributor to the AIE effects seen in the XEOL studies. Understanding and preventing the common CQD ACQ mechanism allows the application of CQDs in solid lighting 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".