Heat capacity of water in nanopores
Bibliographic record
Abstract
Heat capacity of controlled amounts of water in Vycor's 2 nm radius pores has been determined in real time during the course of water's isothermal nanoconfinement from bulk state at 358 K, by using temperature-modulated calorimetry. As water transfers from bulk to nanopores via the vapor phase, its heat capacity per molecule increases asymptotically toward a limiting value of 1.4 times the heat capacity of bulk water for 1.8 wt % water in Vycor and 1.04 times for 10.0 wt %. The observations indicate that vibrational and configurational contributions to the heat capacity are highest when the amount of water is insufficient to completely cover the pore wall, and they decrease as more water is present in the nanopores and water clusters form. The heat capacity of water in completely filled nanopores approaches the value for bulk water, thus indicating that the heat capacity varies with the water molecules' position in the nanopores.
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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 | 0.000 | 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 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".