Femtosecond laser irradiation as a novel method for nanosheet growth and defect generation in g-C<sub>3</sub>N<sub>4</sub>
Bibliographic record
Abstract
Abstract Among the many recently developed photo-catalytic materials, graphitic carbon nitride (g-C 3 N 4 ) shows great promise as a catalytic material for water splitting, hydrogen generation, and related catalytic applications. Herein, synthesized bulk g-C 3 N 4 is simply irradiated under a 35 fs pulse at mixed photon energies (800 nm and its second harmonic). g-C 3 N 4 was synthesized from melamine following a facile thermal polymerization procedure. The prepared material was introduced, in an aqueous environment, to the femtosecond laser for various lengths of time. The treated material demonstrates a significant increase in surface area, relative to the untreated samples, indicating that irradiation is a successful method for exfoliation. The subsequent characterization reveals that the mixed irradiation process drives significant defect generation and sheet growth, which is not seen under 800 nm irradiation. Extended mixed irradiation results in 4 nm thick nanosheets with lateral dimensions 4× that of the bulk material. The treated material shows improved dye absorption/removal. This novel method of defect generation and nanosheet growth shows great potential as a g-C 3 N 4 pre-treatment method for co-catalytic applications. Herein it is shown that femtosecond laser irradiation drives exfoliation beyond 100 nm particle sizes, and sheet-like morphologies under extended irradiation, which must be taken into account when using this method to improve material performance.
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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".