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Record W4400337085 · doi:10.1016/j.isci.2024.110449

Impact of adsorption on thermal conductivity dynamics of adsorbate and adsorbent: Molecular dynamics study of methane and Cu-BTC

2024· article· en· W4400337085 on OpenAlexaff
Haonan Chen, Sagar Saren, Xuetao Liu, Ji Hwan Jeong, Takahiko Miyazaki, Young‐Deuk Kim, Kyaw Thu

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsNexen (Canada)
FundersMinistry of Science and ICT, South KoreaMinistry of Trade, Industry and Energy
KeywordsAdsorptionMolecular dynamicsMethaneThermal conductivityChemical physicsChemistryIntermolecular forceThermodynamicsMoleculeMetal-organic frameworkPhase (matter)Materials sciencePhysical chemistryChemical engineeringComputational chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.269
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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