The Mode of Incorporation of As(-I) and Se(-I) in Natural Pyrite Revisited
Bibliographic record
Abstract
Pyrite (FeS 2 ) from coal, sedimentary rocks, and hydrothermal ore deposits generally contains hazardous selenium (Se) and arsenic (As) that are released in natural waters through oxidative dissolution of the host. Knowing how As and Se are structurally incorporated into pyrite has important implications in controlling or preventing their release because trace metal(loid) substitution accelerates the dissolution of pyrite. Previous extended X-ray absorption fine structure (EXAFS) studies have reported that nominally monovalent arsenic clusters at the sulfur site form As–As pairs at 3.2 Å, whereas monovalent Se does not form Se–Se pairs at this distance for unknown reasons. Here, we revisit this question using As and Se K-edge X-ray absorption near-edge structure (XANES) and EXAFS spectroscopy complemented with atomistic calculations. We find that neither As nor Se atoms can be differentiated from a S atom at 3.2–3.3 Å with the cluster and dilute model-fits to As- and Se-EXAFS data yielding equivalent least-squares solutions. Thermodynamic calculations of Fe 48 As 3 S 93 (3.8 wt % As) and Fe 48 Se 3 S 93 (4.0 wt % Se) structures show that the formation of As–As pairs is energetically favorable and the formation of Se–Se pairs is unfavorable. Thus, the equilibrium distribution of As and Se predicted by calculation agrees with published EXAFS data. However, this agreement is incidental because EXAFS fits are ambiguous with the same EXAFS spectra being fit indifferently with a cluster and a dilute model. Regarding Se, the dilute model-fit is probably correct since Se–Se pairs are precluded thermodynamically. The situation is less clear for As. The lowest energy atomic arrangement of As in Fe 48 S 93 As 3 is similar to the local structure of As in arsenopyrite (FeAsS), thus supporting the cluster model. However, the energy gain to total energy provided by the formation of As clusters decreases with decreasing As concentration, making them thermodynamically less favorable below 1.0 wt %.
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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".