Stability of biochar-supported Ni catalysts during carbon dioxide methanation: A characteristic analysis of deactivation mechanisms and catalyst longevity
Bibliographic record
Abstract
Biochar is being developed as a green catalyst support which can be formed from waste materials, contributing to a circular economy. While biochar is a promising substitute for conventional catalysts, its deactivation, which is not well understood, has limited widespread implementation. Carbon dioxide (CO 2 ) methanation is beneficial as it converts captured CO 2 and hydrogen produced from renewable energy into methane which can be used as a drop-in fuel. This provides a pathway for renewable energy storage in the form of stable fuels. In this work, biochar produced from forestry residues was activated with CO 2 , loaded with nickel, and employed as a catalyst for CO 2 methanation. Metal loadings of 10 wt% and 7 wt% Ni were investigated with catalysts achieving up to 77 % CO 2 conversion and 91 % methane selectivity at 500 °C. Despite good initial performance, catalysts showed significant deactivation over 10 h on stream, with methane selectivity decreasing from 91 % to 35 %. Scanning electron microscopic imaging showed agglomeration of nickel on the biochar surface. This finding was supported by poor dispersion as indicated by XRD analysis. Temperature programmed reduction showed lower reduction temperatures in spent catalysts suggesting weaker metal-support interactions, supporting the likelihood of sintering. • Biochar is a novel, sustainable catalyst support for CO 2 methanation. • Ni particle agglomeration was observed indicating deactivation via sintering. • Biochar with less Ni had higher CH 4 selectivity indicating better dispersion. • Metal-support interactions weaken during methanation, causing agglomeration of Ni.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".