Non-Thermal Plasma-Catalytic Conversion of Biogas to Value-Added Liquid Chemicals via Ni-Fe/Al2O3 Catalyst
Bibliographic record
Abstract
This study investigates the transformation of biogas (methane and carbon dioxide) into high-value liquid products using Ni/Al2O3, Fe/Al2O3, and Ni-Fe/Al2O3 catalysts in a non-thermal plasma (NTP)-assisted process within a dielectric barrier discharge (DBD) reactor, operating at room temperature and atmospheric pressure. We compared the effectiveness of these three catalysts, with the Ni-Fe/Al2O3 catalyst showing the highest enhancement in conversion rates, achieving 34.8% for CH4 and 19.7% for CO2. This catalyst also promoted the highest liquid yield observed at 38.6% and facilitated a significant reduction in coke formation to 10.4%, minimizing deactivation and loss of efficiency. These improvements underscore the catalyst’s pivotal role in enhancing the overall process efficiency, leading to the production of key gas products such as hydrogen (H2) and carbon monoxide (CO), alongside valuable liquid oxygenates including methanol, ethanol, formaldehyde, acetic acid, and propanoic acid. The findings from this study highlight the efficacy of combining NTP with the Ni-Fe/Al2O3 catalyst as a promising approach for boosting the production of valuable chemicals from biogas, offering a sustainable pathway for energy and chemical manufacturing.
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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".