Microbial Mobilization and Reprecipitation of Transition Metals in Waste Rock from an Abandoned Pyrite Mine: Implications for Metal Recovery
Bibliographic record
Abstract
Abstract The Pyrite Mines in Sulphide, Ontario are a collection of historic mine workings representing one of more than 6000 abandoned mines in Ontario. Historic mines are receiving renewed interest as potential sources of critical minerals for use in low carbon technologies. This study characterizes waste rock from the Pyrite Mines in the context of metal distribution, microbial activity, bioleaching, and acid mine drainage (AMD) bioremediation for the recovery of metals. Acidophilic Fe-oxidizing bacteria cultured from the waste rock produced schwertmannite [Fe8O8(OH)8−2x(SO4)x·nH2O] and ammoniojarosite [NH4Fe3(SO4)2(OH)6] with similar morphologies to those often observed in acidic sulfidic mine settings. Fe- and S-oxidizing bacteria found naturally in the waste rock were used in waste rock bioleaching column experiments that demonstrated AMD formation and metal mobilization. The columns produced acidic leachates (pH = 1.75) containing dissolved constituents, including sulfur (1577 mg/L), iron (547.7 mg/L), nickel (12.6 mg/L), manganese (7.3 mg/L), copper (2.3 mg/L), zinc (2.0 mg/L), chromium (1.5 mg/L), and titanium (0.7 mg/L). The proportion of metals successfully leached from the waste rock was variable, with leaching efficiencies calculated for nickel (31%), manganese (10.5%), iron (1.5%), chromium (1.4%), and titanium (0.02%). The leachates produced by the bioleaching columns were amended in subsequent bioremediation columns using sulfate reducing bacteria cultured from the mine site. Remediation efficiencies for elements of interest were calculated as cobalt (100%), chromium (100%), copper (100%), iron (90%), titanium (68%), nickel (52%), manganese (52%), and sulfur (43%). Mapping elemental distributions in thin sections from one of the bioleaching columns using synchrotron X-ray fluorescence microscopy revealed the heterogeneity of the waste rock. Iron was observed in both euhedral mineral grains, likely pyrite, and in secondary cements coating grains in the waste rock. Nickel, manganese, and chromium were primarily co-located with the iron. Titanium was primarily co-located with calcium in titanite (CaTiSiO5), making it challenging to target with bioleaching. This study demonstrates the heterogeneous nature of metal distribution in waste rock from this historic mine site. It indicates that successful metal recovery from legacy mine waste will require such materials to be treated as anthropogenic mineral deposits that require “exploration” and characterization much like naturally occurring ore deposits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".