The hydration structure of carbon monoxide by <i>ab initio</i> methods
Bibliographic record
Abstract
The solvation of carbon monoxide (CO) in liquid water is important for understanding its toxicological effects and biochemical roles. In this paper, we use ab initio molecular dynamics (AIMD) and CCSD(T)-F12 calculations to assess the accuracy of the Straub and Karplus molecular mechanical (MM) model for CO(aq). The CCSD(T)-F12 CO–H2O potential energy surfaces show that the most stable structure corresponds to water donating a hydrogen bond to the C center. The MM-calculated surface incorrectly predicts that the O atom is a stronger hydrogen bond acceptor than the C atom. The AIMD simulations indicate that CO is solvated like a hydrophobic solute, with very limited hydrogen bonding with water. The MM model tends to overestimate the degree of hydrogen bonding and overestimates the atomic radius of the C atom. The calculated Gibbs energy of hydration using the TIP3P water model is in good agreement with the experiment (9.3 kJ mol−1 expt. vs 10.7 kJ mol−1 calc.). The calculated diffusivity of CO (aq) in TIP3P-model water was 5.1×10−5 cm2/s calc., more than double the experimental value of 2.3×10−5 cm2/s. The hydration energy calculated using the TIP4P-FB water model is in poorer agreement with the experiment (ΔG = 6.8 kJ/mol) but the diffusivity is in better agreement (D=2.5±0.1×10−5 cm2/s).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".