Characterisation of slag leachability in support of slag recycling: Development of experimental methodology
Bibliographic record
Abstract
While the amount of metallurgical slag grows rapidly, its recycling has great potential. Main concern associated with the use of slags in various applications is their susceptibility to natural leaching. Characterisation and prediction of the leaching phenomena are essential to minimize the environmental impact of the slag recycling. The current study focuses on fundamentals of leaching behaviour of synthetic amorphous slags with the CaO-“FeO”-MgO-SiO 2 matrix with heavy elements Pb and Cu, added to imitate the common impurities in the copper and lead smelting slags. The original experimental methodology has been developed with an aim to derive the mechanisms of the reactions between slag and aqueous solution. It consists of high-temperature slag synthesis and quenching, bulk leaching or macro leaching type of experiments, and micro-leaching experiments investigating controlled leachability of well characterised surface, followed by characterisation using advanced analytical methods including high-resolution Scanning Electron Microscopy (SEM), Electron Probe X-ray Microanalysis (EPMA), Inductively Coupled Plasma Mass Spectroscopy (ICP-MS), Focus Ion Beam (FIB) sample preparation, and Transmission Electron Microscopy (TEM) equipped with Energy Dispersive Spectrometry (EDS). Leachability of Fe, Mg, Si, Pb and Cu was investigated and analysed. The effect of pH on the slag leachability kinetics has been established. Initial results show the formation of altered layer of approximately 300 nm thickness on the surface of the amorphous slag subjected to leaching in acidic conditions, which is an important finding for further modelling of leaching process combining thermodynamics and kinetics. • Ecological feasibility of reusing and recycling metallurgical residues is studied using advanced analytical techniques. • Dissolution of copper from amorphous fayalite slag is higher than of other elements and does not exceed 3 %. • Potential mechanisms of interaction between amorphous glass and aqueous solutions are proposed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".