<i>Sargassum horneri</i>‐based carbon‐doped <scp>TiO<sub>2</sub></scp> and its aquatic naphthalene photodegradation under sunlight irradiation
Bibliographic record
Abstract
Abstract BACKGROUND During the past two decades, aquatic polycyclic aromatic hydrocarbon (PAH) contamination has gained more and more attention, because of carcinogenicity or mutagenicity, toxicity, persistence and bioaccumulation, and a huge threat posed to entire ecosystems. RESULTS Sargassum horneri (SH)‐based carbon was pretreated with ethanol and formaldehyde, and obtained after pre‐carbonization using H2SO4. The SH carbon powder was used to immobilize nano‐TiO2 in aquatic environment by a facile sol–gel method. The resulting C/TiO2 catalyst was applied to photocatalytic aquatic naphthalene degradation under sunlight and showed the best naphthalene degradation performance of 83.77% under simulated sunlight, which could be attributed to the shape and structure contribution originating from SH. C/TiO2 served as both adsorbent and catalyst in the degradation process of naphthalene, and the photodegradation process conformed to a double‐exponential kinetic model, indicating that there are physical and chemical processes involved in the degradation. The mechanism for enhanced photocatalytic activity was verified as a synergistic mechanism resulting from SH‐based carbon and TiO2, and showed industrial potential for removal aquatic PAH pollutants. CONCLUSIONS This study verifies that the synergistic effect resulting from SH‐based carbon and TiO2 enhanced the photocatalytic performance. © 2021 Society of Chemical Industry (SCI).
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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".