Single-step synthesis of graphene quantum dots by femtosecond laser ablation of graphene oxide dispersions
Bibliographic record
Abstract
In the last few years, graphene quantum dots (GQDs) have attracted the attention of many research groups for their outstanding properties, which include low toxicity, chemical stability and photoluminescence. One of the challenges of GQD synthesis is finding a single-step, cheap and sustainable approach for synthesizing these promising nanomaterials. In this study, we demonstrate that femtosecond laser ablation of graphene oxide (GO) dispersions could be employed as a facile and environmentally friendly synthesis method for GQDs. With the proper control of laser ablation parameters, such as ablation time and laser power, it is possible to produce GQDs with average sizes of 2-5 nm, emitting a blue luminescence at 410 nm. We tested the feasibility of the synthesized GQDs as materials for electronic devices by aerosol-jet printing of an ink that is a mixture of water dispersion of laser synthesized GQDs and silver nanoparticle dispersion, which resulted in lower resistivity of the final printed patterns. Preliminary results showed that femtosecond laser synthesized GQDs can be mixed with silver nanoparticle dispersion to fabricate a hybrid material, which can be employed in printing electronic devices by either printing patterns that are more conductive and/or reducing costs of the ink by decreasing the concentration of silver nanoparticles (AgNPs) in the ink.
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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".