Elevated melting of hydrogen-disordered ice confined by MoS2 nanotubes: A molecular dynamics study of dual confinement geometries
Bibliographic record
Abstract
ABSTRACT While carbon-based nanomaterials have been extensively studied for water confinement, far less is known about the influence of molybdenum disulfide (MoS 2 ) nanotubes on the phase behavior of ice. Using molecular dynamics (MD) simulations, this study unveils how MoS 2 nanotubes, owing to their unique semi-polar, hydrophilic surfaces, significantly affect the stability and melting of hexagonal ice under nanoconfinement. We investigate two distinct confinement modes: (1) within the nanotubes and (2) in the interstitial space between them for both hydrogen-ordered and defect-introduced ice structures. A striking ∼30 K upward shift in melting temperature is observed for defect-introduced ice confined between nanotubes. This effect is absent in comparable carbon nanostructures, highlighting the critical role of defect-introduced ice and surface interactions. Furthermore, we systematically assess the influence of nanotube diameter and heating rate, revealing that melting behavior is dominated more by molecular-level interactions than by geometrical confinement alone. These results demonstrate the novel potential of MoS 2 as a tunable platform for phase-change control, with broad implications for cryopreservation, energy storage, pharmaceuticals, and the design of nanostructured thermal materials.
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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 | 0.000 |
| 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) | 0.001 | 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".