Theoretical studies on clusters of carbonate with carbon dioxide, CO<sub>3</sub><sup>1–/2–</sup>(CO<sub>2</sub>)<i><sub>n</sub></i>, for <i>n</i> = 1–5 — Comparison of carbonate clusters with sulfate clusters
Bibliographic record
Abstract
Density functional theory (DFT) calculations were performed on the geometries and energies of CO31–/2–(CO2)n clusters with n = 1–5. For small clusters (n = 1 or 2), coupled cluster energies were obtained. Up to three CO2 molecules are bound covalently to the dianion. Only weak electrostatic bonds were found in the monoanions. Calculated binding energies for the monoanions are in reasonable agreement with experimental values. The calculated adiabatic electron detachment energy for the dianion is –0.07 eV at n = 5, indicating that at least six CO2 molecules will have to be added to CO32– before the dianionic cluster becomes, in the gas phase, more stable than the monoanionic one. In comparison, for sulfate – carbon dioxide clusters, stabilization occurs at n = 2. Carbonate clusters are compared with sulfate clusters for three solvent molecules: CO2, SO2, and H2O. Carbonate clusters have larger binding energies than sulfate clusters. For a given dianion, binding energies are largest for SO2 and smallest for H2O. However, in all cases, stabilization of the carbonate dianion by clustering is more difficult to achieve than stabilization of the sulfate dianion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".