Application of carbon nanotubes prepared from CH4/CO2 over Ni/MgO catalysts in CO2 capture using a BEA–AMP bi-solvent blend
Bibliographic record
Abstract
Abstract Carbon nanotubes (CNTs) were synthesized by the chemical vapour deposition of methane and carbon dioxide over a Ni/MgO catalyst. The synthesized CNTs were then mixed with K/MgO catalyst at different ratios and used as the catalyst for CO2 absorption in butylethanolamine-2-amino-2-methyl-l-propanol bi-solvent blend. The catalysts were characterized using X-ray diffraction, scanning electron microscopy, butylethanolamine, thermal gravimetric analysis and temperature-programmed desorption of carbon dioxide in order to determine the characteristics responsible for good CO2-absorption performance. The results showed that, with the addition of a catalyst into the amine solution, the amine reached equilibrium CO2 loading faster than without a catalyst. Also, the increase in the CNT content of the KMgO/CNTs catalyst made the CO2 absorption reach equilibrium much more quickly compared with just KMgO alone and without a catalyst. The KMgO/CNTs at a ratio of 1:4 yielded the fastest time to reach CO2-loading equilibrium at 240 min, which was mainly due to the increase in strong basic sites as well as the highest total basic sites with an increase in CNT content. In addition, because of the extremely large specific surface area and pore volume generated due to the CNT, the number of exposed active centres per unit mass increased tremendously, leading to a corresponding tremendous increase in CO2 absorption.
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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 | 0.001 | 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".