Hydration Is the Key for Gold Transport in CO<sub>2</sub>–HCl–H<sub>2</sub>O Vapor
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) is a major component of volcanic gases and ore-forming hydrothermal fluids. However, CO 2 has contrasting effects on the speciation of different metal complexes and ore mineral solubility, but a molecular understanding of its effects is lacking. To address this deficiency, we conducted ab initio molecular dynamics (MD) simulations of the behavior of AuCl (aq) in the CO 2 –H 2 O system at 340 °C and 118–152 bar and 800 °C and 265–291 bar for CO 2 mole fractions ( X CO 2 ) of 0.1–0.9. The MD simulations indicate that the linear [H 2 O–Au–Cl] 0 structure of gold chloride is not affected by CO 2 at X CO 2 up to 0.8 at 340 °C and X CO 2 up to 0.5 at 800 °C, whereas the “dry” [AuCl] 0 species predominates at X CO 2 > 0.8 at 340 °C and X CO 2 > 0.5 at 800 °C. The number of water molecules hydrating the AuCl (aq) complex decreases systematically with an increasing CO 2 mole fraction and increasing temperature. Results of Au solubility experiments at 340 °C in CO 2 –H 2 O solutions show that the addition of CO 2 does not enhance Au solubility. We conclude that hydrated chloride species with linear geometry are the main means for transporting gold in CO 2 –H 2 O–HCl fluids and that Au solubility decreases in CO 2 -bearing hydrothermal fluids as a result of the decrease in hydration of the Au complexes. This contrasts with the behavior of divalent transition metals (e.g., Fe, Co, Ni, and Zn). We propose that the different solubility behaviors of Au and base metals are due to the changes in translational entropy as a result of the changes in coordination geometry (and associated hydration) of the complexes with increasing X CO 2 . The first-shell coordination of Au(I) complexes remains constant over wide ranges of X CO 2, whereas first-row divalent transition metal complexes undergo entropy-driven geometric changes with a decreasing water activity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".