Ro-translational dynamics of confined water. I. The confined asymmetric rotor model
Bibliographic record
Abstract
Confinement effects on the ro-translational (RT) dynamics of water, trapped in rare gas matrices or within endofullerenes (i.e., H2O@C60), can be experimentally assessed using rotationally resolved far-infrared, or mid-infrared, spectroscopy [Putaud et al., J. Chem. Phys. 156, 074305 (2022) (Paper II)]. The confined rotor model is used here to reveal how the quantized rotational and frustrated translational energy levels of confined water interact and mix by way of the confinement-induced rotation-translation coupling (RTC). An eccentric but otherwise isotropic 3D harmonic effective potential is used to account for confinement effects, thereby allowing the dependence of the magnitude of the RTC on the topology of the model confinement potential, the resulting intricate mixing schemes, and their impact on the RT energy levels to be examined in detail. The confined rotor model thus provides a convenient framework to investigate the matrix and isotope effects on the RT dynamics of water under extreme confinement probed spectroscopically, thereby potentially providing insight into the mechanisms and rates for ortho-H2O ↔ para-H2O nuclear spin isomer interconversion in confined water.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".