Deposition of gold nanoparticles on organo-kaolinite — Application in electrocatalysis for carbon monoxide oxidation
Bibliographic record
Abstract
The clay mineral kaolinite was used as support of gold nanoparticles for heterogeneous catalysis of oxidation reactions, particularly of carbon monoxide oxidation. The application of clay minerals in the preparation of new functional materials provides an alternative approach for the use of these abundant raw materials. To improve the physicochemical properties of kaolinite, as well as to ensure a strong immobilization of the adsorbed species, kaolinite was functionalized by grafting 2-amino-2-methyl-1,3-propanediol on the internal and external surfaces of the octahedral sheets by reaction with the aluminol groups. Gold nanoparticles were then deposited on the external surfaces of the fine particles of the functionalized kaolinite. The resulting gold kaolinite nanohybrid material was characterized by various physicochemical techniques. X-ray diffraction, X-ray photoelectron spectroscopy, transmission electron microscopy, and energy-dispersive X-ray spectrometry confirmed that gold was effectively reduced to the metallic state during adsorption onto the external surfaces of the modified kaolinite. The gold nanoparticles have a narrow size distribution: more than 88% are less than 4 nm in diameter. Gold nanoparticles deposited on kaolinite catalyze the electro-oxidation of carbon monoxide in alkaline solution at room temperature.
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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) | 0.000 | 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 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".