Highly Efficient Metal‐Free Visible Light Driven Photocatalyst: Graphene Oxide/Polythiophene Composite
Bibliographic record
Abstract
Abstract Graphene oxide/polythiophene (GO/PTh) composites were synthesized by in‐situ polymerization of thiophene (Th) monomers on GO surfaces. Remarkable performance of GO/PTh composites for methylene blue (MB) photo‐degradation under visible light has been achieved by tuning GO/Th ratio and graphene oxidation degrees. 100 % MB degradation was achieved by the composite within 30 min under visible light, its catalytic activity (0.1149 min −1 ) is 382 and 41 times higher than that of PTh (0.0003 min −1 ) and GO (0.0028 min −1 ), respectively. The results of MB adsorption experiment, ultraviolet‐visible (UV‐vis) and photoluminescence (PL) spectra show that combination of GO and PTh increases MB adsorption, decreases the band gap and enhances photo‐electron transfer. The composite with 36 % PTh (at the fed GO/Th weight ratio of 1:2) shows the highest catalytic activity where MB adsorption ability by GO and photo‐electron producing ability by PTh in the composite is well matched. The catalytic activity can be further enhanced by changing graphene oxidation degree by controlling graphite/KMnO 4 ratio and post‐reaction time during GO preparation. Fourier transform infrared (FTIR) spectroscopy and X‐ray photo‐electron (XPS) spectroscopy analyses have shown that increasing oxidation degree of GO leads to a stronger π‐π interaction between GO and PTh and a more electron‐rich PTh, resulting in higher catalytic activity.
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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".