Papaya peel waste carbon dots/reduced graphene oxide nanocomposite: From photocatalytic decomposition of methylene blue to antimicrobial activity
Bibliographic record
Abstract
Carbon dots (CDs) have gained unprecedented attention as a novel luminescent zero-dimensional carbon nanomaterial owing to their diverse industrial applications. Herein, we reported the sustainable synthesis of fluorescent CDs from papaya peel waste, acting as a natural carbon originator. As-prepared CDs and reduced graphene oxide (RGO) were fabricated in the composites through a facile one-step hydrothermal method. Synthesized RGO/CDs (RC) nanocomposites were characterized using spectroscopic, diffraction, and electron-microscopic techniques. Nanocomposites with variable RGO to CD mass ratios were tested for photodegradation of textile dye methylene blue (MB). The highest photocatalytic activity (degradation efficiency of 87% in 135 min) was obtained in the nanocomposite containing a 2꞉1 mass ratio (RC2). The RGO sheets in the nanocomposite acted as media for electron acceptors, promoting the fast transfer and separation of photoinduced electrons during CDs excitation, thus preventing the recombination of the electron and holes. Based on the agar well diffusion assay, the nanocomposites exhibited excellent antibacterial activity than other tested materials against Bacillus subtilis (Gram-positive) and Pseudomonas aeruginosa (Gram-negative) bacterium. The largest inhibition zone area (22 mm), i.e., the highest antimicrobial activity, was obtained in the nanocomposite tested against Gram-positive strains. Taken together, the synergistic effect of RGO and CDs enhanced the photocatalytic and antibacterial performance of synthesized nanocomposite material.
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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".