The University of Toronto Continuous Flow Diffusion Chamber (UT-CFDC): A Simple Design for Ice Nucleation Studies
Bibliographic record
Abstract
A new instrument, the University of Toronto Continuous Flow Diffusion Chamber (UT-CFDC), has been designed to study ice nucleation at low temperatures. Based on previous continuous flow instruments, it is a parallel plate model that minimizes convective instabilities by operating horizontally with the warmer plate on top. A variable position sample injector can account for effects arising from gravitational settling of ice particles that form. The residence time in the chamber can vary between 2.6 to 25 s and ice particle formation is monitored with a two-channel optical particle counter. Observation of homogeneous freezing of 100 nm sulfuric acid aerosols was used to verify the accuracy of the calculated relative humidities (RHs) in the chamber to be ±4%, where we report onset RHs for 0.1% of the particles freezing in the temperature range of 218 to 243 K. We also show that the chamber accurately establishes conditions of water saturation by conducting water uptake studies onto sulfuric acid aerosol at 243 K. The two channel OPC allows for ice and water droplet formation to be distinguished under such conditions. The chamber is a simple, cheap, and small design that can be readily assembled for laboratory studies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".