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Record W2150332097 · doi:10.1002/qua.560180862

Quantum statistical theory of localized physisorption

2009· article· en· W2150332097 on OpenAlexaff
Zbigniew W. Gortel, H. J. Kreuzer

Bibliographic record

VenueInternational Journal of Quantum Chemistry · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDesorptionPhysisorptionIsothermal processPhononThermodynamicsChemistryRange (aeronautics)QuantumNon-equilibrium thermodynamicsRelaxation (psychology)Arrhenius equationAdsorptionAtomic physicsPhysicsQuantum mechanicsMaterials scienceActivation energyPhysical chemistry

Abstract

fetched live from OpenAlex

A quantum statistical theory of phonon-mediated localized physisorption has been developed by setting up the initial-value problem within nonequilibrium statistical mechanics and calculating the desorption times appropriate for the various experimental procedures, i.e., virgin adsorption, isothermal desorption, and flash desorption. We are able to delimit the temperature range over which the Arrhenius-Frenkel parametrization of the desorption time td = t0d exp(Q/kT) is acceptable. We calculate desorption times for the He/Constantan system which develops one weak bound state at an energy E0/kB = −25 K and show by comparison with experiments that the rather long flash desorption times (t0d ˜ 10−7 sec at temperatures between 4 and 20 K) are the result of the weak coupling (range of surface potential ca. 2.5 Å) between the gas and the phonons of the solid. We can correlate isothermal and flash desorption times and suggest the temperature range where differences in these times can be detected. A complete fourth-order calculation enables us to delineate the range of validity of the one-phonon (second-order) theory and of the relaxation time approach to desorption. Extensive numerical results are shown and discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.274
Teacher spread0.265 · 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.

Study designTheoretical or conceptual
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

Citations3
Published2009
Admission routes1
Has abstractyes

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