Influence of Na <sub>2</sub> CO <sub>3</sub> on the activation degree and microstructure of coal gasification coarse slag
Bibliographic record
Abstract
Silicon in coal gasification coarse slag (CGCS) accounts for more than 40%. CGCS can be developed into an inorganic silicon source to improve its utilization value. Alkali activation can increase extraction rate of silicon. In this study, the influence of Na2CO3 on the activation degree and microstructure of CGCS was investigated. The activation mechanism was discussed based on IR, XRD, SEM, and EDS data. The results showed that the dosage of Na2CO3 was positively correlated with SiO2/Al2O3 in CGCS, which is generally 0.8 ~ 1.3 times of CGCS. The activation temperature of CGCS was 800°C ~ 850°C. A small amount of residual carbon could slightly reduce the activation temperature. The alkali activation process involved depolymerizing the polymer structure into an oligomer, whereby the crystalline structure was transformed into an amorphous structure. Moreover, Q4 structure in CGCS could be more easily activated than Q3 or Q2 structure. The activation reaction was carried out under gas-liquid-solid three-phase conditions. The diffusion effect of CO2 gas could be fully utilized in the activation process to ensure the melting of heteroatoms and the formation of defects, which prevented the emergence of crystalline substances. This research provides a solid theoretical foundation for low-cost and high-quality extraction of silicon from CGCS.
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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".