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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".