Study on preparation of eco-friendly autoclaved aerated concrete from low silicon and high iron ore tailings
Bibliographic record
Abstract
In order to realize the resource utilization of solid waste, autoclaved aerated concrete (ACC) was prepared with low silicon and high iron tailings (IOT) as the main siliceous materials.The effects of fineness and content of siliceous materials, and static curing temperature on the AAC properties, hydration products and microstructures of AAC were investigated by physical and mechanical properties test, X-ray diffraction analysis (XRD), and scanning electron microscope (SEM).The result shows that the AAC containing 40% IOT (in mass percentage) with a specific surface area (SSA) of 311 m 2 •kg-1 can achieve a compressive strength of 4.15 MPa and bulk density of 587 kg•m -3 , which qualifies the requirements of B06, A3.5 of AAC sample regulated by the composition and morphology GB/T 11969-2008.And the main phases in the system contain hydration products, ferrotschermakite, hornblende, anhydrite, calcite, dolomite and residual quartz.The main hydration products are 0.9 nm, 1.1 nm and 1.4 nm tobermorite and C-S-H gels.And the strength mainly results from cementation between hydration products tobermorite and C-S-H gels and unreacted components.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".