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Record W2053894199 · doi:10.1103/physrevlett.92.145503

Bridging the Gap between Small Clusters and Nanodroplets: Spectroscopic Study and Computer Simulation of Carbon Dioxide Solvated with Helium Atoms

2004· article· en· W2053894199 on OpenAlexaff
Jian Tang, A. R. W. McKellar, Fabio Mezzacapo, Saverio Moroni

Bibliographic record

VenuePhysical Review Letters · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsHeliumAtomic physicsPhysicsQuantum Monte CarloSpectral lineCluster (spacecraft)SolvationMoleculeInfraredMolecular physicsMonte Carlo methodQuantum mechanics

Abstract

fetched live from OpenAlex

High resolution infrared spectra of He(N)-CO2 clusters with N up to 17 have been studied in the region of the CO2 nu(3) fundamental band. The B rotational constant initially drops as expected for a normal molecule, reaching a minimum for N=5. Its subsequent rise for N=6 to 11 can be interpreted as the transition from a normal (though floppy) molecule to a quantum solvation regime. For N>13, the B value becomes approximately constant with a value about 17% larger than that measured in much larger helium nanodroplets. Quantum Monte Carlo calculations of pure rotational spectra are in excellent agreement with the measured B in this size range and complement the experimental study with detailed structural information. For a larger cluster size (N=30-50) the simulations show a clear sign of convergence towards the nanodroplet B value.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.261
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations108
Published2004
Admission routes1
Has abstractyes

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