Fine-tuning the catalytic activity by applying nitrogen-doped carbon nanotubes as catalyst supports for the hydrogenation of olefins
Bibliographic record
Abstract
Abstract Nitrogen-free multi-wall carbon nanotubes (MWCNTs) and N-doped bamboo-like carbon nanotubes (BCNTs) were synthesized by using catalytic vapor deposition (CVD) and used as catalyst support materials. Pd, Rh, Ru, and Ir have been deposited onto the nanotubes to achieve metal/nanotube catalysts. The catalytic activity of the samples was fine-tuned by changing the type of support. BCNT supported Pd and Rh (Pd/BCNT, Rh/MWCNT) catalysts were found to be the most active for liquid phase hydrogenation of octadecene amongst these samples. The initial olefin hydrogenation rate of the Pd/BCNT sample was slightly higher than the corresponding MWCNT-supported catalyst. Based on the hydrogenation reaction, the performance of these catalyst had been ranked as follows: Pd/BCNT ≈ Rh/MWCNT > Pd/MWCNT > Rh/BCNT > > Ir/MWCNT > Ru/BCNT > Ir/BCNT > Ru/MWCNT. The structural properties of chemisorbed Pd on MWCNT and N- BCNT were also characterized by means of computational chemical methods in order to shed some light on the nature of metal binding properties of N-doped and undoped surfaces. The calculations shown preference towards the edges of the surfaces which is in good agreement with the experimental findings.
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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".