Synthesis, Applications, and Prospects of Graphene Quantum Dots: A Comprehensive Review
Bibliographic record
Abstract
Graphene quantum dot (GQD) is one of the youngest superstars of the carbon family. Since its emergence in 2008, GQD has attracted a great deal of attention due to its unique optoelectrical properties. Non-zero bandgap, the ability to accommodate functional groups and dopants, excellent dispersibility, highly tunable properties, and biocompatibility are among the most important characteristics of GQDs. To date, GQDs have displayed significant momentum in numerous fields such as energy devices, catalysis, sensing, photodynamic and photothermal therapy, drug delivery, and bioimaging. As this field is rapidly evolving, there is a strong need to identify the emerging challenges of GQDs in recent advances, mainly because some novel applications and numerous innovations on the ease of synthesis of GQDs are not systematically reviewed in earlier studies. This feature article provides a comparative and balanced discussion of recent advances in synthesis, properties, and applications of GQDs. Besides, current challenges and future prospects of these emerging carbon-based nanomaterials are also highlighted. The outlook provided in this review points out that the future of GQD research is boundless, particularly if upcoming studies focus on the ease of purification and eco-friendly synthesis along with improving the photoluminescence quantum yield and production yield of GQDs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".