Eco-friendly preparation of asbestos tailings based cementitious material: Grinding dynamics and structure formation mechanism
Bibliographic record
Abstract
The storage of asbestos tailings (ATs) presents significant health and environmental risks and has been officially classified as hazardous waste in China. This issue also affects major asbestos-producing countries, including the United States, Canada, and Australia. This study seeks to address these challenges through a novel treatment approach. The proposed method involves the mechanical activation of ATs via ball grinding, analysis of the relationship between particle structure and activation parameters through grinding kinetics and microstructural characterisation, and determination of the optimal activation conditions. In this work, asbestos tailings-based cementitious materials (ATs-C) were successfully produced through the synergistic incorporation of powdered ATs, silica fume, and lightly calcined magnesium oxide. Results indicated that grinded ATs conformed to Divas–Aliavden’s grinding kinetics theory, while their particle size distribution adhered to the Rosin–Rammler–Bennett (RRB) kinetics model. After 40 min of grinding, the specific surface area of ATs reached 37.97 m²/g, and the pore structure was markedly enhanced, leading to the formation of highly reactive “secondary particles”. The maximum compressive strength of ATs-C reached 23.4 MPa, and the flexural strength was 3.88 MPa, meeting the requirements specified in ASTM C62–17 (Specification for Building Brick, Solid Masonry Units Made from Clay or Shale). Mechanical collision disrupted and reorganised the Si–O and Mg–O bonds within ATs, resulting in the formation of amorphous or low-crystallinity hydrated magnesium silicate gel. This method enables efficient, low-cost, and harmless treatment of ATs at ambient temperature, eliminates granular dust emissions, and poses no risks to human health or the natural environment. It offers an economically viable resource-utilisation pathway for the millions of tonnes of ATs stockpiled in China. • Innovative mechanical ball milling activation process for asbestos tailings. • Revealing the structural evolution mechanism of activated secondary particles in asbestos tailings. • Low-carbon asbestos tailings based cementitious material are prepared for the first time. • Opening up new possibilities for the application in the fields of ecology and construction.
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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) | 0.000 | 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 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".