Development and characterization of a laminar aerosol flow tube
Bibliographic record
Abstract
We have developed a new laminar aerosol flow tube (AFT) to study transformations such as ice nucleation, deliquescence, and efflorescence in model atmospheric aerosols. The apparatus consists of four sections which can be independently cooled to reproduce temperature profiles relevant to the troposphere and stratosphere. An automatic control system maintains the average axial temperature along each section between 100 and 300K, within ±0.1K. Changes in aerosol composition, phase, and size distribution are monitored at the tube exit using infrared spectroscopy (AFT-IR). We used computational fluid dynamics simulations to investigate flow velocity and temperature distributions within the flow tube. Based on these computations, the final design was formulated to eliminate turbulent mixing zones and buoyancy-driven convection cells. The latter can occur even under conditions where the Reynolds number indicates laminar flow. In either case, recirculation causes aerosol residence times and temperature histories to be poorly defined, leading to erroneous interpretation of experimental measurements. The resulting AFT design used copper fins to reduce temperature gradients and axial mixing of aerosol and carrier gas flows in the inlet section to reduce turbulence. The performance of the new AFT is significantly better than for previous designs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".