Ni-doped Carbon Nanofilaments (Ni-CNF): Preparation and Use as Reforming Catalyst
Bibliographic record
Abstract
The use of nanocarbons as heterogeneous catalyst support offers the possibility of achieving well-dispersed and thermally-stable catalysts.Because of their low internal diffusion resistance and relatively high specific surface, carbon nanotubes and carbon nanofilaments (CNF) are of special interest.This work reports on a CNF functionalization endeavour aimed at producing Ni-CNF as steam-reforming catalysts.Catalytic activity was studied parametrically on diesel and biodiesel steam reforming.Fresh and spent catalysts were investigated by scanning and transmission electron microscopy to visualize their morphology, by thermogravimetric analysis to evaluate metal (Ni) load, by Xray diffraction to assess the presence of and changes in crystalline (and amorphous) phases, and by Brunauer Emmet and Teller analysis to appraise the catalyst surfaces.Reactants conversion and reformate composition (product yields) were reported over time-on-stream under various reaction conditions.Finally, CNF-supported Ni-catalysts were compared to equivalent multiwall carbon nanotube (MWCNT)-supported catalysts (Ni-MWCNT).The results demonstrated excellent initial reforming activity which declined relatively rapidly over time for Ni-CNF.The fast deactivation observed was due to CNF instability under reforming conditions which led to nanometrically-distributed Ni grain sintering and, consequently, loss of specific surface.
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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".