Probing hydrogen bond network formation in anion–water clusters through high energy collision experiments
Bibliographic record
Abstract
In high-energy collisions (50 keV in the laboratory system) between anion–water clusters [X−⋅(H2O)n, X−=Cl−, CN−, O2−, NO2−, NO3−; n=1–6] and argon, H3O+ is formed with an abundance that is determined by the cluster size—the larger the cluster, the more H3O+. The mechanism for H3O+ formation is believed to be a nearly vertical ionization process (two-electron stripping) followed by an exothermic intracluster hydrogen transfer reaction between an ionized water and a neutral water. The abundance of H3O+ can be used as a probe to determine how extended the water hydrogen bond network is in the initial anion–water cluster and to distinguish between surface solvation (water network preserved) and internal solvation (water network broken). In this work, it is demonstrated that in the hydration of hexacyanoplatinate(IV) dianion complexes, surface solvation is important despite the large number of available water binding sites; however, the competition between a cyanide-bound water and a “naked” cyanide ligand for a water molecule favors the cyanide-bound water because of the splitting of the excess charges between six ligands (between −1/3 and −1 charge at each ligand on average). We also investigate anion–methanol clusters in which the hydrogen bond network is less extended with the result of a less abundant oxonium ion compared to the hydronium ion from similar size water clusters.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".