Toluene Cluster Formation in Laval Expansions: Nucleation and Growth
Bibliographic record
Abstract
Toluene cluster formation has been investigated in the postnozzle flows of Laval expansions at flow temperatures between ∼48 and 73 K, toluene number concentrations between ∼10 13 and 10 15 cm –3, and for growth times of up to ∼170 μs. The clusters were detected by soft ionization mass spectrometry to ensure minimum cluster fragmentation upon ionization. The optimum conditions were achieved with single-photon ionization using vacuum ultraviolet (VUV) photons of 13.3 eV energy and low fluences. The nature of the onset of toluene cluster formation hints at barrierless nucleation, which seems a likely scenario for the high supersaturations (>10 19 ) of the present experiments. This contrasts with the onset behavior observed for propane in earlier studies, which suggested nucleation in the presence of a barrier. Subsequent cluster growth has been studied as a function of the growth time for various toluene partial pressures. Size-resolved growth data have been recorded for all cluster sizes from the dimer to aggregates composed of ∼2400 monomers (∼4.4 nm in size), revealing general trends in the growth behavior. The current experiments provide systematic size- and time-resolved data on cluster formation at high supersaturations as a possible benchmark for the understanding of cluster formation under such conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".