Femtosecond laser ablation of highly oriented pyrolytic graphite: a green route for large-scale production of porous graphene and graphene quantum dots
Bibliographic record
Abstract
Porous graphene (PG) and graphene quantum dots (GQDs) are attracting attention due to their potential applications in photovoltaics, catalysis, and bio-related fields. We present a novel way for mass production of these promising materials. The femtosecond laser ablation of highly oriented pyrolytic graphite (HOPG) is employed for their synthesis. Porous graphene (PG) layers were found to float at the water-air interface, while graphene quantum dots (GQDs) were dispersed in the solution. The sheets consist of one to six stacked layers of spongy graphene, which form an irregular 3D porous structure that displays pores with an average size of 15-20 nm. Several characterization techniques have confirmed the porous nature of the collected layers. The analyses of the aqueous solution confirmed the presence of GQDs with dimensions of about 2-5 nm. It is found that the formation of both PG and GQDs depends on the fs-laser ablation energy. At laser fluences less than 12 J cm(-2), no evidence of either PG or GQDs is detected. However, polyynes with six and eight carbon atoms per chain are found in the solution. For laser energies in the 20-30 J cm(-2) range, these polyynes disappeared, while PG and GQDs were found at the water-air interface and in the solution, respectively. The origin of these materials can be explained based on the mechanisms for water breakdown and coal gasification. The absence of PG and GQDs, after the laser ablation of HOPG in liquid nitrogen, confirms the proposed mechanisms.
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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 | 0.001 |
| 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".