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Record W3131856853 · doi:10.1103/physreva.103.023112

Accurate determination of an alkali-vapor–inert-gas diffusion coefficient using coherent transient emission from a density grating

2021· article· en· W3131856853 on OpenAlexafffund
Alexander Pouliot, Gehrig Carlse, H. C. Beica, Thomas Vacheresse, A. Kumarakrishnan, U. Shim, S. B. Cahn, A. Turlapov, Tycho Sleator

Bibliographic record

VenuePhysical review. A/Physical review, A · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationOntario Innovation Trust
KeywordsBuffer gasDiffusionAlkali metalGratingPhysicsAtomic physicsInert gasAnalytical Chemistry (journal)ScalingMaterials scienceChemistryOpticsThermodynamicsLaserQuantum mechanics

Abstract

fetched live from OpenAlex

We demonstrate a technique for the accurate measurement of diffusion coefficients for alkali vapor in an inert buffer gas. The measurement was performed by establishing a spatially periodic density grating in isotopically pure $^{87}\mathrm{Rb}$ vapor and observing the decaying coherent emission from the grating due to the diffusive motion of the vapor through ${\mathrm{N}}_{2}$ buffer gas. We obtain a diffusion coefficient of $0.245\ifmmode\pm\else\textpm\fi{}0.002\phantom{\rule{0.16em}{0ex}}{\text{cm}}^{2}/\text{s}$ at $50{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}\mathrm{C}$ and 564 Torr. Scaling to atmospheric pressure, we obtain ${D}_{0}=0.1819\ifmmode\pm\else\textpm\fi{}0.0024\phantom{\rule{0.16em}{0ex}}{\text{cm}}^{2}/\text{s}$. To the best of our knowledge, this represents the most accurate determination of the $\mathrm{Rb}\text{\ensuremath{-}}{\mathrm{N}}_{2}$ diffusion coefficient to date. Our measurements can be extended to different buffer gases and alkali vapors used for magnetometry and can be used to constrain theoretical diffusion models for these systems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.399
Teacher spread0.363 · 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 designBench or experimental
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

Citations17
Published2021
Admission routes2
Has abstractyes

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