Influence of quantum effects on the physisorption of molecular hydrogen in model carbon foams
Bibliographic record
Abstract
The physisorption of molecular hydrogen in model carbon foams has been investigated from \documentclass[12pt]{minimal}\begin{document}$50\text{ K}$\end{document}50K to room temperature. The study is carried out within the framework of the density functional theory for quantum liquids at finite temperatures. Calculations are performed in the grand canonical ensemble, i.e., the adsorbed fluid is assumed to be in equilibrium with an external gas of hydrogen molecules with concentrations ranging from \documentclass[12pt]{minimal}\begin{document}$8 \times 10^{-4}\; \text{kg}\, \text{m}^{-3}$\end{document}8×10−4kgm−3 to \documentclass[12pt]{minimal}\begin{document}$n=71\; \text{kg\,} \text{\emph {\emph {m}}}^{-3}$\end{document}n=71kgm−3. It is shown that, while strong zero-point energy effects are present even at room temperature, the adsorption isotherms exhibit only a weak dependence on the explicit incorporation of the bosonic exchange symmetry of hydrogen molecules. The increase of the average particle density prevents the deviations from the Maxwell-Boltzmann statistics to become noticeable if the system is cooled down. The volumetric storage capacity of these materials at low temperatures is about one half of the U. S. Department of Energy goal, while the gravimetric capacity is still far from the standards required by mobile applications. The relation between the microscopic structure of the hydrogen fluid and the calculated adsorption properties is also addressed.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".