Copper-Doped Carbon Nanodots with Superior Photocatalysis, Directly Obtained from Chromium-Copper-Arsenic-Treated Wood Waste
Bibliographic record
Abstract
An ecofriendly approach was developed for preparing copper-doped carbon dots (CDs) with superior photocatalysis using chromium-copper-arsenic (CCA)-treated wood waste as a precursor. Original wood (W-CDs), CCA-treated wood (C-CDs), and bioremediation CCA wood (Y-CDs) were used as the precursors. The chemical composition and structural, morphological, and optical properties, as well as the photocatalytic ability of the synthesized CDs varied with wood type. The C-CDs and W-CDs had similar characteristics: quasispherical in shape and with a diameter of 2 to 4.5 nm. However, the Y-CDs particles were irregular and stacked together, with a size of 1.5-3 nm. The presence of nitrogen prevented the formation of an aromatic structure for those CDs fabricated from bioremediation CCA wood. The three synthesized CDs showed a broad absorption peak at 260 nm and a weak absorption peak at 320 nm. Proof of the model study for the fabrication of luminescent CDs from CCA wood waste for bioimaging was provided. The degradation rate of CD photocatalytic MB was 97.8% for 30 min. Copper doping gives the CDs electron acceptor properties, improving their photocatalytic efficiency. This study provides novel ways to prepare nanomaterials from decommissioned wood as a nontoxic and low-cost alternative to fluorescent dots.
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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".