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Record W4399730830 · doi:10.3390/qubs8020015

Analysis of Avoided Level Crossing Muon Spin Resonance Spectra of Muoniated Radicals in Anisotropic Environments: Estimation of Muon Dipolar Hyperfine Parameters for Lorentzian-like Δ1 Resonances

2024· article· en· W4399730830 on OpenAlexafffund
Iain McKenzie, Victoria L. Karner, R. Scheuermann

Bibliographic record

VenueQuantum Beam Science · 2024
Typearticle
Languageen
FieldEngineering
TopicMuon and positron interactions and applications
Canadian institutionsSimon Fraser UniversityUniversity of WaterlooTRIUMF
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHyperfine structureMuonMuon spin spectroscopyDipoleAnisotropyLevel crossingResonance (particle physics)Atomic physicsPhysicsSpectral lineSpin (aerodynamics)MuoniumNuclear physicsOpticsQuantum mechanicsEngineering

Abstract

fetched live from OpenAlex

Avoided level crossing muon spin resonance (ALC-μSR) is used to characterize muoniated free radicals. These radicals are used as probes of the local environment and reorientational motion of specific components in complex systems. The parameter that provides information about the anisotropic motion is the motionally-averaged muon dipolar-hyperfine coupling constant (Dμ‖). The ALC-μSR spectra of muoniated radicals in anisotropic environments frequently have Lorentzian-like Δ1 resonances, which makes it challenging to extract Dμ‖. In this paper, we derive a means to estimate|Dμ‖| from ALC-μSR spectra with Lorentzian-like resonances by measuring the amplitude, width, and position of the Δ1 resonance and the amplitude, width, and position of a Δ0 resonance. Numerical simulations were used to test this relationship for radicals with a wide range of muon and proton hyperfine parameters. We use this methodology to determine |Dμ‖| for the Mu adducts of the cosurfactant 2-phenylethanol in C12E4 bilayers. From this we determined the amplitude of the anisotropic reorientational motion of the cosurfactant.

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 score0.540
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.026
GPT teacher head0.301
Teacher spread0.275 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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