Magnetic GO‐PANI decorated with Au NPs: A highly efficient and reusable catalyst for reduction of dyes and nitro aromatic compounds
Bibliographic record
Abstract
Due to the high activity of Au nanoparticles (NPs) for various reactions, many researchers have tried to develop heterogeneous catalysts in order to prevent irreversible agglomeration of Au NPs. Herein, magnetic graphene oxide modified with polyaniline (PANI) was used as a support for Au NPs that brings together advantages including: uniform dispersal of the catalyst in water,alarge surface area related to the graphene oxide; easy electron transfer in chemical reactions and good attachment of Au NPs to the support associated with PANI; and finally facile recovery in the presence of a magnetic field. Catalytic reduction of different analytes (Congo red, methylene blue, rhodamine B and 4‐nitro phenol) was evaluated in the presence of NaBH 4 and the results show high catalytic activity of the catalyst. The catalyst was thoroughly characterized using various methods including FTIR, XRD, XPS, FE‐SEM and HRTEM analyses while its catalytic activity was evaluated via reduction of different analytes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".