Engineering biochar-supported nickel catalysts for efficient CO2 methanation
Bibliographic record
Abstract
Carbon dioxide methanation is a promising approach to convert captured CO2 into green natural gas. Developing high performance biochar-supported nickel catalysts promotes a circular economy and the application of sustainable catalysts. Western red cedar biochar was produced via pyrolysis at 400, 500, and 600 °C and loaded with nickel via incipient wetness impregnation. Methanation was done at 400, 500, and 600 °C with the highest methane yield of 59% achieved at 500 °C with 10 wt.% Ni loading. This is comparable to a γ-Al2O3 supported catalyst prepared and tested similarly, which achieved a methane yield of 53%. Biochar-supported catalysts showed deactivation whereby methane yield decreased from 59% to 51% over 5 h, likely due to coking and/or the sintering of nickel. Various space velocities were tested, and results demonstrated that with a space velocity of 37.5 mL/g.min methane selectivity was 89% after 1 h on stream compared to methane selectivity of 42%, which was achieved at a space velocity of 112.5 mL/g.min. This shows that a much higher rate of deactivation is observed at higher space velocities. Increasing the nickel loading from 5 wt.% to 10 wt.% increased methane yield from 40% to 58% after 1 h on stream. The higher loading also showed significantly less deactivation. Future work focusing on the extent and impact of metal-support interactions and metal dispersion on catalytic performance and deactivation during CO2 methanation is recommended.
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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".