Advanced cold plasma-assisted technology for green and sustainable ammonia synthesis
Bibliographic record
Abstract
Ammonia (NH 3 ) is the most crucial industrial chemical feedstock for producing fertilizers and is a promising future hydrogen carrier. Recent research has spurred for the development of alternative green and sustainable ammonia production technologies. Cold plasma technology provides a clean, sustainable method for nitrogen (N 2 ) conversion into active species for ammonia synthesis. Synergistic action of cold plasma and catalyst has significantly improved the current production rate and selectivity. Present energy consumption (2.1 MJ mol N −1 ) for N 2 fixation via plasma-assisted technology is still higher than the commercial process (0.7 MJ mol N −1 ), while a further improvement would be game-changing. In this review, we explain the takeover by plasma-assisted technology and its potential for green and sustainable ammonia production. We briefly present that the major challenge in nitrogen fixation of N 2 to NO x as an intermediate pathway, can be addressed by plasma technology via NO x transformation into targeted NH 3 product. We discussed the emerging plasma and catalysis synergism, mechanisms involved and highlighted current research development in selective ammonia generation. Finally, we outlined the ways to achieve cleaner and sustainable ammonia production and challenges in future work. • Cold plasma technology enables clean and sustainable approach to ammonia synthesis. • Synergy of plasma technology and catalytic reactions realized nitrogen fixation. • Energy-efficient and selective NH 3 synthesis require interdisciplinary research. • Advanced cold plasma technology may provide green NH 3 supply with net-zero economy.
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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 | 0.001 |
| 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 | 0.001 |
| 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".