A compact setup to study homogeneous nucleation and condensation
Bibliographic record
Abstract
An experiment is presented to study homogeneous nucleation and the subsequent droplet growth at high temperatures and high pressures in a compact setup that does not use moving parts. Nucleation and condensation are induced in an adiabatic, stationary expansion of the vapor and an inert carrier gas through a Laval nozzle. The adiabatic expansion is driven against atmospheric pressure by pressurized inert gas its mass flow carefully controlled. This allows us to avoid large pumps or vacuum storage tanks. Because we eventually want to study the homogeneous nucleation and condensation of zinc, the use of carefully chosen materials is required that can withstand pressures of up to 10(6) Pa resulting from mass flow rates of up to 600 l(N) min(-1) and temperatures up to 1200 K in the presence of highly corrosive zinc vapor. To observe the formation of droplets a laser beam propagates along the axis of the nozzle and the light scattered by the droplets is detected perpendicularly to the nozzle axis. An ICCD camera allows to record the scattered light through fused silica windows in the diverging part of the nozzle spatially resolved and to detect nucleation and condensation coherently in a single exposure. For the data analysis, a model is needed to describe the isentropic core part of the flow along the nozzle axis. The model must incorporate the laws of fluid dynamics, the nucleation and condensation process, and has to predict the size distribution of the particles created (PSD) at every position along the nozzle axis. Assuming Rayleigh scattering, the intensity of the scattered light can then be calculated from the second moment of the PSD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.007 | 0.002 |
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".