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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".