Top-down synthesis and enhancing device adaptability of graphene quantum dots
Bibliographic record
Abstract
Quantum dots (QD) are rapidly making their way into several application sectors including optoelectronics, and there is a strong need to focus on non-toxic QDs. In this work, we have synthesized graphene QDs in the size range of 1.4-4.2 nm from inexpensive graphite by oxidative cleavage using a sulphuric and nitric acid mixture. A subsequent hydrogen peroxide oxidation step, investigated using two thermal budgets, has resulted in QDs with excellent photoluminescence (PL) intensity. Prolonged, higher temperature oxidation results in smaller size GQDs. X-ray photoelectron spectroscopy analysis confirmed the role of ·OH radicals in the oxidation process and Raman analysis revealed that the higher thermal budget oxidation results in lower defect density. To overcome the challenges in device adaptability due to the inherent acidity in the QDs, a post-synthesis neutralization process was devised. The neutralized GQDs were formed into a film to be used as the active layer in a photodetector device. Fluorescence decay analysis showed there is no significant change in lifetime because of the film formation process. The fabricated GQD photodetector device exhibited high photocurrent under ultraviolet illumination with an ON/OFF ratio of 400% at an applied bias of ±1 V. The device performance underlines the high potential for the non-toxic, top-down synthesized GQDs for application in optoelectronic devices.
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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) | 0.000 | 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".