Synthesis, characterization, and efficacy of alkali-activated materials from mine tailings: A review
Bibliographic record
Abstract
Annually, over 5 billion metric tons of tailings are produced worldwide as byproducts of mining processes, posing significant environmental risks due to their potential to pollute and disrupt ecosystems. Concurrently, the production of portland cement (PC), the primary binder in cementitious materials is a major contributor to global anthropogenic carbon dioxide emissions. With the escalating demand for PC, a corresponding surge in carbon emissions is inevitable. Alkali-activated materials (AAMs) present a greener alternative to PC, given their production primarily utilizes industrial wastes. Traditional precursors for AAMs, such as fly ash and slag, however, are not universally available-Canada, for instance, faces a scarcity of fly ash for AAM production. In response to the dual challenges of managing mine tailings and reducing PC's environmental footprint, this review proposes the innovative use of mine tailings as an alternative binder to PC. This paper offers a thorough examination of mine tailings' properties, methodologies to enhance their suitability for AAM synthesis, and an analysis of AAMs produced from diverse tailing sources. Additionally, this paper explores the associated challenges and future prospects, providing a rounded overview of this promising avenue in sustainable construction materials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".