Analytic Correlation for the Thermodynamic Properties of Water at Low Temperatures (200–300 K) and High Pressures (0.1–400 MPa)
Bibliographic record
Abstract
Water is vital to all facets of life and is anomalously behaving in its condensed states, making it continually a substance of interest to researchers. Therefore, attempting to capture its properties via correlations and equations of state is extremely valuable. Liquid water has not been studied as extensively in the low-temperature and high-pressure region as in other regions. Some key applications for correlations in this region are cryopreservation (specifically in certain methods of cryopreservation such as hyperbaric (high-pressure) and isochoric (constant-volume) cryopreservation), deep oceans, hydrospheres, clouds, and precipitation. Although there are not nearly as many models for water at low temperatures and high pressures as there are in other temperature and pressure ranges, there are some models that do currently exist. However, these either do not extend to temperatures and pressures as extreme as the data that exist, or they are complex with large numbers of parameters making them more difficult for application. Herein, we present a new correlation for liquid water valid for the temperature range of 200-300 K (-73-27 °C) and pressure range of 0.1-400 MPa that can analytically calculate volume, isothermal compressibility, isobaric expansivity, constant pressure heat capacity, and speed of sound, using only 17 adjustable parameters. The analytical expressions that we derived, and the fitting method that we used can also be applied to other fluids of interest in the future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".