Pressure-regulated rotational guests in nano-confined spaces suppress heat transport in methane hydrates
Bibliographic record
Abstract
Materials with low lattice thermal conductivity are essential for various heat-related applications like thermoelectrics, and usual approaches for achieving this rely on specific crystalline structures. Here, we report a strategy for thermal conductivity reduction and regulation via guest rotational dynamics and their couplings with lattice vibrations. By applying pressure to manipulate rotational states, we find the intensified rotor-lattice couplings of compressed methane hydrate MH-III can trigger strong phonon scatterings and phonon localizations, enabling an almost three-fold suppression of thermal conductivity. Besides, the disorder in methane rotational dynamics results in anharmonic interactions and nonlinear pressure-dependent heat transport. The overall guest rotational dynamics and heat conduction changes can be flexibly regulated by the rotor-lattice coupling strength. We further underscore that this reduction mechanism can be extended to a wide range of systems with different structures. The results demonstrate a potentially universal method for reducing or controlling heat transport by developing a hybrid system with tailored molecular rotors. Molecular disorder plays an important role in the thermal conductivity of materials. Using atomistic simulations, the authors show that thermal conductivity can be pressure-regulated in methane hydrates by manipulating disordered guest rotational dynamics.
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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".