Impact of adsorption on thermal conductivity dynamics of adsorbate and adsorbent: Molecular dynamics study of methane and Cu-BTC
Bibliographic record
Abstract
Understanding changes in thermodynamic and transport properties during adsorption is crucial for the thermal management of metal-organic frameworks, which also imposes significant challenges for improved performance and energy density of adsorption system. Because of phase transitions in the intermolecular interactions involved in the adsorption phenomena, transport properties including the thermal conductivity exhibit interesting behaviors, yet fully understood. This study employs detailed molecular dynamics simulations to replicate the methane/Cu-BTC adsorption phenomenon for the evaluation of their thermal conductivities across different pressures and temperatures. The molecular simulations show that the thermal conductivities of both the adsorbent (Cu-BTC) and adsorbate (methane, adsorbed phase) vary notably during adsorption processes. Using the concepts of the change in the degree of free movements of the adsorbate molecules and atomic vibration of adsorbent, the reduction of the adsorbate thermal conductivity (∼70–93%) and increase thermal conductivity of the adsorbent (up to 3 times) in Cu-BTC+CH 4 pair are explained.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".