Preparation and Hydration Mechanism of Mine Cemented Paste Backfill Material for Secondary Smelting Water-Granulated Nickel Slag
Bibliographic record
Abstract
Using secondary smelting water-granulated nickel slag (SWNS) after iron extraction as the main raw material, made composite activator (flue gas desulphurization gypsum (FGDG) and carbide slag (CS) are the main activators, Na2SO4 and cement clinker (CC) are the auxiliary activators) with cementing agents (CA), and then made into mine cemented paste backfill (CPB) material with iron ore tailings (IOT). The factors affecting the mechanical properties such as the composition of raw materials and the amount of activator were studied by means of mechanical property test, X-ray diffraction (XRD) and scanning electron microscope (SEM), the ratio of CPB material was optimized, and the hydration mechanism of CPB material was analyzed. The results show that when the mass fraction of SWNS, FGDG, CS, Na2SO4 and CC in CA is 85%, 5%, 5%, 3% and 2%. The 28d flexural strength and compressive strength of CPB material of SWNS reach 1.99 MPa and 3.38 MPa, which meet the strength requirements of mine CPB material. The results of X-ray diffraction (XRD), scanning electron microscope (SEM) and hydration mechanism analysis showed that both vitreous and crystalline substances in, CA of SWNS could be hydrated, and the main hydration products were ettringite (AFt) and Ca 2+ , Mg 2+ containing hydrated aluminosilicate (C-M-S-H) gel.
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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".