The Formation of Silicate-Stabilized Passivating Layers on Pyrite for Reduced Acid Rock Drainage
Bibliographic record
Abstract
Acid and metalliferous release occurring when sulfide (principally pyrite)-containing rock from mining activities and from natural environments is exposed to the elements is acknowledged as a major environmental problem. Acid rock drainage (ARD) management is both challenging and costly for operating and legacy mine sites. Current technological solutions are expensive and focused on treating ARD on release rather than preventing it at source. We describe here a viable, practical mechanism for reduced ARD through the formation of silicate-stabilized iron oxyhydroxide surface layers. Without silicate, oxidized pyrite particles form an overlayer of crystalline goethite or lepidocrocite with porous structure. With silicate addition, a smooth, continuous, coherent and apparently amorphous iron oxyhydroxide surface layer is observed, with consequent pyrite dissolution rates reduced by more than 90% at neutral pH. Silicate is structurally incorporated within this layer and inhibits the phase transformation from amorphous iron (oxy)hydroxide to goethite, resulting in pyrite surface passivation. This is confirmed by computational simulation, suggesting that silicate-doping of a pseudoamorphous iron oxyhydroxide (ferrihydrite structure) is thermodynamically more stable than the equivalent undoped structure. This mechanism and its controlling factors are described. As a consequence of the greatly reduced acid generation rate, neutralization from on-site available reactive silicate minerals may be used to maintain neutral pH, after initial limestone addition to achieve neutral pH, thus maintaining the integrity of these layers for effective ARD management.
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) | 0.000 | 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".