The synthesis, characterization, and stability of yukonite: Implications in arsenic mobility
Bibliographic record
Abstract
The focus of this thesis is to investigate the stability of the mineral phase yukonite–a calcium ferric arsenate–which may be important in controlling arsenic pore water concentrations in mineral processing-derived tailings. Yukonite has been found in association with gold mine tailings in Nova Scotia and the Yukon Territory in Canada, and laboratory accelerated ageing experiments suggest yukonite may form ferric arsenate phases in the presence of gypsum. Hence, this work fills a void in knowledge regarding the solubility/stability of yukonite.The results of this thesis are presented in the form of two manuscripts. The first study describes the synthesis and characterization of yukonite as well as its stability under oxic conditions. An atmospheric precipitation method was used to produce material identified as yukonite (Ca2Fe3(AsO4)3(OH)4·(3+x)H2O) by XRD and chemical analysis. Long-term stability experiments indicated yukonite possesses low arsenic solubility under mildly acidic, neutral, and mildly alkaline conditions. The presence of gypsum was found to have a stabilizing effect, with As(V) solubility of 0.6-0.9 mg·L-1 at pH 7 and 0.6-2.4 mg·L-1 at pH 8. The second study investigates the stability of yukonite by reaction with CO2 and chemical-reducing agents. Sparging of CO2 as well as equilibration with various concentrations of NaHCO3 both caused destabilization of yukonite resulting in release of arsenate and pointed to the precipitation of calcite. Yukonite showed resilience to the mild reducing potential (ca. 200 mV) achieved by reaction with sulfite (SO32-) but underwent reductive dissolution in the presence of strong reducing conditions (ca. -200 mV) due to reaction with sulfide (S2-). The results indicate yukonite may play a role in arsenic immobilization where abundant CO2 and strong reducing conditions are not present.
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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) | 0.001 | 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".