Methods of coating ceramic supports with carbon and Ni‐based catalytically active formulations
Bibliographic record
Abstract
Abstract Nickel‐based catalysts have been widely investigated as they exhibit high activity for reforming reactions, particularly in order to produce syngas. Microwave‐assisted heating is gaining interest as an energy‐efficient heating solution for heterogenous catalytic reactions, and for its exciting prospects related to selective catalyst heating. The objective of this study is to partially or completely coat a fluidizable support with a carbon layer to enhance the support surface reactivity to microwaves, then deposit Ni/Al active species on the carbon‐coated silica surface. This configuration would provide a fine distribution of the catalyst on the surface and an enhanced heating performance with microwaves due to the presence of carbon. The following three synthesis methods have been studied: (a) plasma deposition, (b) hydrothermal synthesis, and (c) coating with organic compounds. The resultant materials have been characterized using scanning electron microscopy with energy dispersive x‐ray spectroscopy (SEM‐EDXS); specific surface area was determined using the multipoint Brunauer, Emmett, and Teller (BET) method and elemental analysis for carbon quantification. The results show that the type of carbon deposited on the surface depends on the preparation method and that the latter is a factor influencing the active particle's deposition. Coating with organic compounds has proven to be the method that provides the best deposition of the active phase, while the hydrothermal technique shows the best carbon surface adherence.
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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) | 0.001 | 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 | 0.001 |
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".