Bacterial Ice Nucleation in Monodisperse D<sub>2</sub>O and H<sub>2</sub>O-in-Oil Emulsions
Bibliographic record
Abstract
Ice nucleation is of fundamental significance in many areas, including atmospheric science, food technology, and cryobiology. In this study, we investigated the ice-nucleation characteristics of picoliter-sized drops consisting of different D2O and H2O mixtures with and without the ice-nucleating bacteria Pseudomonas syringae. We also studied the effects of commonly used cryoprotectants such as ethylene glycol, propylene glycol, and trehalose on the nucleation characteristics of D2O and H2O mixtures. The results show that the median freezing temperature of the suspension containing 1 mg/mL of a lyophilized preparation of P. syringae is as high as -4.6 °C for 100% D2O, compared to -8.9 °C for 100% H2O. As the D2O concentration increases every 25% (v/v), the profile of the ice-nucleation kinetics of D2O + H2O mixtures containing 1 mg/mL Snomax shifts by about 1 °C, suggesting an ideal mixing behavior of D2O and H2O. Furthermore, all of the cryoprotectants investigated in this study are found to depress the freezing phenomenon. Both the homogeneous and heterogeneous freezing temperatures of these aqueous solutions depend on the water activity and are independent of the nature of the solute. These findings enrich our fundamental knowledge of D2O-related ice nucleation and suggest that the combination of D2O and ice-nucleating agents could be a potential self-ice-nucleating formulation. The implications of self-nucleation include a higher, precisely controlled ice seeding temperature for slow freezing that would significantly improve the viability of many ice-assisted cryopreservation protocols.
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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 | 0.000 | 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) | 0.000 | 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 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".