Bibliographic record
Abstract
In this manuscript, we developed a screening system that employs the difference in density between liquid water and ice (0.9998 g/cm 3 vs 0.9168 g/cm 3 at 0 °C) to identify ice-nucleating agents (INAs) that are encapsulated into droplets of water suspended in silicone oil of intermediate density (0.939 g/cm 3 ). Droplets of liquid water stably reside at the interface of the silicone oil and perfluoro oil (1.6658 g/cm 3 ); freezing causes the aqueous droplets to float to the top of the silicone oil layer. We demonstrated the feasibility of this screening system by using droplets that contained well-defined ice-nucleator Snomax. The droplets with and without Snomax froze at different temperatures and separated into two groups in our system. We employed the screening system to test samples that have different ice-nucleating activities. Starting from known ice-nucleating active bacteria Pseudomonas syringae, we confirmed that droplets that contain an increasing amount of ice-nucleating bacteria per droplet exhibit a dose-dependent increase in ice nucleation. When droplets containing different amounts of P. syringae were separated using a freeze–float setup, we observed that the droplets that floated at higher temperature contained more ice-nucleating active bacteria. The outlined system, thus, permits simple power-free separation of droplets that contain effective INA from those that contain weak or no INA. Such a setup can be used as a starting point for the development of high-throughput approaches for the discovery of new INAs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".