Bibliographic record
Abstract
This paper presents a study on the relationship between transport properties and geometric free volume for a hard sphere (HS) system in a dense fluid region. First, a generic free volume distribution function is proposed based on recent simulation results on the HS geometric free volume by Maiti and Sastry [J. Chem. Phys. 141(4), 044510 (2014)] and Maiti et al. [Eur. Phys. J. E 36(1), 5 (2013)]. Combining the new distribution function with a local particle transportation model, we obtain a power law for the HS transport properties. Then, a relation between the geometric free volume and thermodynamic free volume is established, which makes it possible to use well-developed equations of state (EoS) for the expressions of the geometric free volume. The new power law models are tested with molecular dynamic simulation results for HS viscosity, diffusivity and thermal conductivity, respectively, and the results are very satisfactory. Moreover, using the power law, we are able to reproduce several equations obtained from different approaches, such as the entropy scaling laws [Bell et al., J. Phys. Chem. B 123(29), 6345–6363 (2019]), mode coupling theory [Barrat et al., J. Phys. Condens. Matter 1, 7163–7170 (1989)], or empirical correlations [Sigurgeirsson and Heyes, J. Mol. Phys. 101(3), 469–482 (2003)]. In particular, a long-standing controversy regarding the well-known Cohen–Turnbull–Doolittle free volume model [Cohen and Turnbull, J. Chem. Phys. 31(3), 1164–1169 (1959); Doolittle, J. Appl. Phys. 22(12), 1471–1475 (1951)] is resolved by using the power law combined with the Heyes and Woodcock EoS [Heyes and Woodcock, Mol. Phys. 59(6), 1369–1388 (1986)].
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".