Effect of surface structure on the catalytic behavior of Ni:Cu/Al and Ni:Cu:K/Al catalysts for methane decomposition
Bibliographic record
Abstract
Methane decomposition using nickel,copper,and aluminum(Ni:Cu/Al)and nickel,copper,potassium,and alu- minum(Ni:Cu:K/Al)modified nano catalysts has been investigated for carbon fibers,hydrogen and hydrocarbon production. X-ray photoelectron spectroscopy(XPS),static secondary ion mass spectrometry(SSIMS),thermal gravimetric analysis(TGA), Fourier transform infrared(FT-IR),secondary electron microscopy/X-ray energy dispersive(SEM-EDX),and temperature pro- grammed desorption(TPD)were used to depict the chemistry of the catalytic results.These techniques revealed the changes in surface morphology and structure of Ni,Cu,Al,and K,and formation of bimetallic and trimetallic surface cationic sites with different cationic species,which resulted in the production of graphitic form of pure carbon on Ni:Cu/Al catalyst.The addition of K has a marked effect on the product selectivity and reactivity of the catalyst system.K addition restricts the formation of carbon on the surface and increases the production of hydrogen and C_2,C_3 hydrocarbons during the catalytic reaction whereas no hydrocarbons are produced on the sample without K.This study completely maps the modified surface structure and its re- lationship with the catalytic behavior of both systems.The process provides a flexible route for the production of carbon fibers and hydrogen on Ni:Cu/Al catalyst and hydrogen along with hydrocarbons on Ni:Cu:K/Al catalyst.The produced carbon fibers are imaged using a transmission electron microscope(TEM)for diameter size and wall structure determination.Hydrogen produced is CO_x free,which can be used directly in the fuel cell system.The effect of the addition of Cu and its transformation and interaction with Ni and K is responsible for the production of CO/CO_2 free hydrogen,thus producing an environmental friendly clean energy.
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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".