Synthesis and properties of multi-functionalized graphene quantum dots with tunable photoluminescence and hydrophobicity from asphaltene and its oxidized and reduced derivatives
Bibliographic record
Abstract
, methanol, toluene, and chloroform) using a facile ultrasonication technique. Asphaltene surface chemistry was tuned by sequential oxidation and reduction to investigate the surface effects on GQD properties. Spectroscopic characterizations confirmed the presence of N, S, and O heteroatoms and different electron-donating and electron-withdrawing groups. Microscopic characterizations revealed that these crystalline carbon nanomaterials have mono-layered or multi-layered structures with lateral sizes in the range of ∼5-15 nm. The asphaltene-derived GQDs exhibit tunable PL with emission colors ranging from blue to orange, depending on the carbon precursor and the organic solvent. Solvent exchange studies also revealed that asphaltene and its derivatives contain hydrophilic and hydrophobic fractions, resulting in varied hydrophobicity of the synthesized GQDs. Adding to the appeal of the present work, PL quenching of GQD-silica hybrid materials upon exposure to nitro-aromatics confirms that these GQDs can be incorporated to different host materials for advanced sensing or optoelectronic applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".