Enhanced visible light photocatalytic activity of <scp> Cl‐Bi <sub>2</sub> WO <sub>6</sub> </scp> / <scp> g‐C <sub>3</sub> N <sub>4</sub> </scp> composite photocatalyst
Bibliographic record
Abstract
Abstract In this paper, ultra‐thin g‐C 3 N 4 nanosheets were prepared by improved ultrasonic stripping method for the first time. Then, g‐C 3 N 4 nanosheets were pretreated into Bi + /g‐C 3 N 4 nanosheets by electrostatic adsorption principle, and a new type of Cl‐Bi 2 WO 6 /g‐C 3 N 4 heterostructure photocatalyst was prepared by using Bi + /g‐C 3 N 4 nanosheets as the substrate. The degradation of RhB under photocatalysis was studied with the compound amount of g‐C 3 N 4 and the amount of catalyst by using x‐ray diffraction (XRD), a scanning electron microscope (SEM), high‐resolution transmission electron microscopy (HRTEM), x‐ray photoelectron spectroscopy (XPS), Brunauer Emmett‐Teller (BET), and ultraviolet visible diffuse (UV‐vis DRS) methods on the structure, morphology, and optical characterization charge transfer characteristics, etc. The results of the degradation experiment show that the compound appropriate amount of g‐C 3 N 4 can significantly improve the activity of the photocatalyst. At the same time, different amounts of Cl‐Bi 2 WO 6 /g‐C 3 N 4 composite photocatalyst have a significant effect on the degradation performance, and the photocatalytic activity is the best when the input is 2 g/L. Combined with active species capture experiments, a possible degradation mechanism of Cl‐Bi 2 WO 6 /g‐C 3 N 4 Z‐type heterostructure photocatalyst was proposed.
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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".