Sustainable ammonia synthesis: An in-depth review of non-thermal plasma technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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