Geopolymer Mortar with the Tailings from the Fundão Dam Rupture
Bibliographic record
Abstract
The extraction of iron ore generates large amounts of tailings. In addition to other solutions to avoid storing this material in dams, it is important to use these tailings as raw materials for the manufacturing of consumer goods. One possible solution is to use geopolymer cement in mortars for civil construction with employment of the tailings as substitute for usual sands. Geopolymers are produced from alkaline activation of aluminosilicates. They can incorporate a larger amount of tailings than the Portland cement. The tailings from the rupture of the Fundão dam, which were retained in the water reservoir of the Candonga hydropower plant were characterized as a fine aggregate for civil construction and used to obtain geopolymer mortar. Metakaolin was mixed with an alkaline solution of sodium silicate and sodium hydroxide to form a paste. The tailings were added and uniformly mixed to this paste. Two series of experiments were conducted with addition of 40 and 60 wt% of tailings to the geopolymer paste. The fresh mortars were placed in cylindrical molds. The samples were left to harden at room temperature or at 60°C for 24 hours and demolded. After hardening, the samples were cured at room temperature for 7 and 28 days. The samples were characterized by their compressive strength, water absorption, and density. The results were analyzed according to a 23 factorial design with the factors: composition (amount of tailings added to the mortar), hardening conditions, and curing time. For all responses (compressive strength, water absorption, and density), the results show a complex behavior with influence of the factors and their interactions. Compressive strengths from 19 to 43 MPa, water absorptions from 19 to 41 wt%, and densities from 1.60 to 2.11 g/cm3 were observed. These properties can be controlled by adjusting the levels of the factors according to empirical models. This mortar can be considered for applications in civil construction.
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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 | 0.000 |
| 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 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".