Mineralogical Study of a Biologically-Based Treatment System That Removes Arsenic, Zinc and Copper from Landfill Leachate
Bibliographic record
Abstract
Mineralogical characterization by X-ray diffraction (XRD) and a high throughput automated quantitative evaluation of minerals by scanning electron microscopy (QEMSCAN) was conducted on samples from a sulphate-reducing biochemical reactor (BCR) treating high concentrations of metals (As, Zn, Cu) in smelter waste landfill seepage. The samples were also subjected to energy dispersive X-ray (EDX) analysis of specific particles. The bulk analysis results revealed that the samples consisted mainly of silicate and carbonate minerals. More detailed phase analysis indicated four different classes: zinc-arsenic sulphosalts/sulphates, zinc-arsenic oxides, zinc phosphates and zinc-lead sulphosalts/sulphates. This suggests that sulphates and sulphides are the predominant types of Zn and As minerals formed in the BCR. Sphalerite (ZnS) was a common mineral observed in many of the samples. In addition, X-ray point analysis showed evidence of As and Zn coating around feldspar and amphibole particles. The presence of arsenic-zinc-iron, with or without cadmium particles, indicated arsenopyrite minerals. Copper-iron-sulphide particles suggested chalcopyrite (CuFeS2) and tennantite (Cu,Fe)12As4S13. Microbial communities found in each sample were correlated with metal content to describe taxonomic groups associated with high-metal samples. The research results highlight mineral grains that were present or formed at the site that might be the predominant forms of immobilized arsenic, zinc and copper.
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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".