Extraction of monomer-cluster association rate constants from water nucleation data measured at extreme supersaturations
Bibliographic record
Abstract
We utilize recently reported data for water nucleation in the uniform postnozzle flow of pulsed Laval expansions to derive water monomer association rates with clusters. The nucleation experiments are carried out at flow temperatures of 87.0 K and 47.5 K and supersaturations of lnS ∼ 41 and 104, respectively. The cluster size distributions are measured at different nucleation times by mass spectrometry coupled with soft single-photon ionization at 13.8 eV. The soft ionization method ensures that the original cluster size distributions are largely preserved upon ionization. We compare our experimental data with predictions by a kinetic model using rate coefficients from a previous ab initio calculation with a master equation approach. The prediction and our experimental data differ, in particular, at the temperature of 87.0 K. Assuming cluster evaporation to be negligible, we derive association rate coefficients between monomer and clusters purely based on our experimental data. The derived dimerization rate lies 2-3 orders of magnitude below the gas kinetic collision limit and agrees with the aforementioned ab initio calculation. Other than the dimerization rate, however, the derived rate coefficients between monomer and cluster j (j ≥ 3) are on the same order of magnitude as the kinetic collision limit. A kinetic model based on these results confirms that coagulation is indeed negligible in our experiments. We further present a detailed analysis of the uncertainties in our experiments and methodology for rate derivation and specify the dependency of the derived rates on uncertainties in monomer and cluster concentrations.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".