Microphysical detection of nano-ice nuclei to ice crystals: a platform for ice nucleation research
Bibliographic record
Abstract
Atmospheric ice nucleation plays a crucial role in cloud formation, precipitation, and climate dynamics. However, the physicochemical properties of submicron ice nucleating particles (INPs) remain poorly understood, and distinguishing between nano- to micron-sized ice crystals and supercooled droplets in cloud microphysical processes remains a significant challenge. Here, we present the first detection of nano-sized ice crystals (390 nm) along with their physical properties using a portable platform for ice nucleation that integrates the McGill Real-time Ice Nucleation Chamber (MRINC) with advanced holographic microscopy and aerosol sizers. This platform enables real-time detection and differentiation of ice crystals and supercooled droplets, providing microphysical information into their spherical or non-spherical morphology, surface roughness, and phase characteristics, particularly for ice particles smaller than 500 nm. Automated algorithms facilitate the differentiation of individual and aggregated ice crystals within a size range of 390 nm to 100 µm, supporting time-resolved analyses of ice nucleation processes. Surface roughness (Rt, Ra) measurements and 3D structural data offer critical insights into light scattering and radiation interactions, with smaller ice crystals (<1 µm) exhibiting higher roughness and enhanced multidirectional scattering. Validation through computational fluid dynamics simulations and experiments demonstrates platform ability to differentiate silver iodide-nucleated ice crystals from supercooled droplets and to monitor aerosol growth, advancing our understanding of aerosol-cloud-radiation interactions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".