Engineering biochar-supported nickel catalysts for efficient CO2 methanation
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it