<scp>CO<sub>2</sub></scp> conversion through combined steam and <scp>CO<sub>2</sub></scp> reforming of methane reactions over Ni and Co catalysts
Bibliographic record
Abstract
Abstract Ni‐Co bimetallic and Ni or Co monometallic catalysts prepared for CO2 reforming of methane were tested with the stimulated biogas containing steam, CO2, CH4, H2, and CO. A mix of the prepared CO2 reforming catalyst and a commercial steam reforming catalyst was used in hopes of maximizing the CO2 conversion. Both CO2 reforming and steam reforming of CH4 occurred over the prepared Ni‐Co bimetallic and Ni or Co monometallic catalysts when the feed contained steam. However, CO2 reforming did not occur on the commercial steam reforming catalyst. There was a critical steam content limit above which the catalyst facilitated no more CO2 conversion but net CO2 production for steam reforming and water‐gas shift became the dominant reactions in the system. The Ni‐Co bimetallic catalyst can convert more than 70% of CO2 in a biogas feed that contains ~33 mol% of CH4, 21.5 mol% of CO2, 12 mol% of H2O, 3.5 mol% of H2, and 30 mol% of N2. The H2/CO ratio of the produced syngas was in the range of 1.8‐2. X‐ray absorption spectroscopy of the spent catalysts revealed that the metallic sites of Ni‐Co bimetallic, Ni and Co monometallic catalysts after the steam reforming of methane reaction with equimolar feed (CH4:H2O:N2 = 1:1:1) experienced severe oxidation, which led to the catalytic deactivation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".