Effects of stacking disorder on thermal conductivity of cubic ice
Bibliographic record
Abstract
Cubic ice is said to have stacking disorder when the H2O sequences in its structure (space group Fd3̄m) are interlaced with hexagonal ice (space group P6(3)/mmc) sequences, known as stacking faults. Diffraction methods have shown that the extent of this disorder varies in samples made by different methods, thermal history, and the temperature T, but other physical properties of cubic and hexagonal ices barely differ. We had found that at 160 K, the thermal conductivity, κ, of cubic ice is ∼20% less than that of hexagonal ice, and this difference varies for cubic ice samples prepared by different methods and/or subjected to different thermal history. After reviewing the methods of forming cubic ice, we report an investigation of the effects of stacking disorder and other features by using new data, and by analyzing our previous data on the dependence of κ on T and on the pressure. We conclude that the lower κ of cubic ice and its weaker T-dependence is due mainly to stacking disorder and small crystal sizes. On in situ heating at 20-50 MPa pressure, κ increases and cubic ice irreversibly transforms more sharply to ice Ih, and at a higher T of ∼220 K, than it does in ex situ studies. Cooling and heating between 115 and 130 K at 0.1 K min(-1) rate yield the same κ value, indicating that the state of cubic ice in these conditions does not change with time and T. The increase in κ of cubic ice observed on heat-annealing before its conversion to hexagonal ice is attributed to the loss of stacking faults and other types of disorders, and to grain growth. After discussing the consequences of our findings on other properties, we suggest that detailed studies of variation of a given property of cubic ice with the fraction of stacking faults in its structure may reveal more about the effect of this disorder. A similar disorder may occur in the mono-layers of H2O adsorbed on a substrate, in bulk materials comprised of two dimensional layers, in diamond and in Zirconium and in numerous other crystals.
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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 | 0.001 |
| 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".