Sustainable ammonia synthesis: An in-depth review of non-thermal plasma technologies
Bibliographic record
Abstract
Ammonia serves both as a widely used fertilizer and environmentally friendly energy source due to its high energy density, rich hydrogen content, and emissions-free combustion. Additionally, it offers convenient transportation and storage as a hydrogen carrier. The dominant method used for large-scale ammonia production is the Haber-Bosch process, which requires high temperatures and pressures and is energy-intensive. However, non-thermal plasma offers an eco-friendly alternative for ammonia synthesis, gaining significant attention. It enables ammonia production at lower temperatures and pressures using plasma technology. This review provides insights into the catalyst and reactor developments, which are pivotal for promoting ammonia efficiency and addressing existing challenges. At first, the reaction kinetics and mechanisms are introduced to gain a comprehensive understanding of the reaction pathways involved in plasma-assisted ammonia synthesis. Thereafter, the enhancement of ammonia synthesis efficiency is discussed by developing and optimizing plasma reactors and effective catalysts. The effect of other feeding sources, such as water and methane, instead of hydrogen is also presented. Finally, the challenges and possible solutions are outlined to facilitate energy-saving and enhance ammonia efficiency in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".