A new green synthesis approach using sodium palmitate in the synthesis of a Ni/ <scp> Al <sub>2</sub> O <sub>3</sub> </scp> catalyst for <scp> CO <sub>2</sub> </scp> methanation
Bibliographic record
Abstract
Abstract CO 2 methanation with Ni/Al 2 O 3 catalysts is a key technology for converting CO 2 emissions into sustainable methane. However, conventional impregnation synthesis often results in poor nickel dispersion and in the emission of gaseous NO x species. Thus, for the first time, sodium palmitate flocculant properties were used to isolate nickel nanoparticles and disperse them over an Al 2 O 3 support. Ni/Al 2 O 3 catalysts were synthesized by mechanochemical and wet impregnation methods (MI and WI, respectively) to evaluate the impact of the synthesis route on the catalytic performance. Nickel nanoparticles with 9.7–12.6 nm were produced. Influences of temperature (300–500°C) and gas hourly space velocity (GHSV, 2490–14,920 h −1 ) were evaluated, and a stability study was performed. Best performances were reached at 400°C and the lowest GHSV (2490 h −1 ). Ni/Al 2 O 3 ‐WI catalyst showed a slightly better performance in terms of CO 2 and H 2 conversion (, ), when compared to Ni/Al 2 O 3 ‐MI (, ). In addition, CH 4 selectivity was kept stable at >99% for all studies. The stability test showed that Ni/Al 2 O 3 ‐WI had a stable performance during the 50 h. These results indicate that this synthesis approach is a viable method for producing catalysts with strong activity and reduced environmental impact. Furthermore, the incorporation of sodium palmitate in the synthesis opens the possibility for the direct application of residual bio‐oils in the synthesis of Ni/Al 2 O 3 catalysts for CO 2 methanation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".