Effect of nitrogen doping on reactivity of coal char in reducing NO
Bibliographic record
Abstract
Mixing coal char into coal powder and enhancing its reactivity with NO in the combustion process of a layer burning furnace is an effective method to realize low‐cost resource reduction of NOx. To enhance NO reducibility of char at high temperatures, this study used Shuozhou bituminous coal to prepare different nitriding chars (char modified by nitrogen doping) by changing the nitrogen agent, the dosage of nitrogen agent, and the treatment method. The effects of different nitriding conditions on the NO reducibility of char were evaluated using a programmed temperature rising method. The results show that NO reducibility of char is improved in different degrees after nitriding treatment, which is related to the improvement of pore structure and the formation of nitrogen‐containing functional groups. The NO reducibility of char is enhanced in the high‐temperature region when increasing the dosage of urea, but excessive urea amounts can hinder the porosity development of char, resulting in the decline of NO reduction efficiency. Ammonium bicarbonate as a nitrogen agent shows better effects on NO reduction below 725 °C compared with urea, but weaker effects above 725 °C. Furthermore, heat treatment can weaken the effect of nitrogen doping, but the overall trend of NO reduction curve is almost unchanged.
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.
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) | 0.000 | 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 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".