CuCl Complexation in the Vapor Phase: Insights from Ab Initio Molecular Dynamics Simulations
Bibliographic record
Abstract
We investigated the hydration of the CuCl0 complex in HCl-bearing water vapor at 350°C and a vapor-like fluid density between 0.02 and 0.09 g/cm3 using ab initio molecular dynamics (MD) simulations. The simulations reveal that one water molecule is strongly bonded to Cu(I) (first coordination shell), forming a linear [H2O-Cu-Cl]0 moiety. The second hydration shell is highly dynamic in nature, and individual configurations have short life-spans in such low-density vapors, resulting in large fluctuations in instantaneous hydration numbers over a timescale of picoseconds. The average hydration number in the second shell (m) increased from ~0.5 to ~3.5 and the calculated number of hydrogen bonds per water molecule increased from 0.09 to 0.25 when fluid density (which is correlated to water activity) increased from 0.02 to 0.09 g/cm3 ( f H2O 1.72 to 2.05). These changes of hydration number are qualitatively consistent with previous solubility studies under similar conditions, although the absolute hydration numbers from MD were much lower than the values inferred by correlating experimental Cu fugacity with water fugacity. This could be due to the uncertainties in the MD simulations and uncertainty in the estimation of the fugacity coefficients for these highly nonideal “vapors” in the experiments. Our study provides the first theoretical confirmation that beyond-first-shell hydrated metal complexes play an important role in metal transport in low-density hydrothermal fluids, even if it is highly disordered and dynamic in nature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".