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Record W2177202128 · doi:10.1139/cjp-2013-0655

Comparative study of helical-cut notch–coil magnets for fast-field-cycling nuclear magnetic resonance

2014· article· en· W2177202128 on OpenAlexvenueno aff
S. Kruber, Germán Farrher, Esteban Anoardo

Bibliographic record

VenueCanadian Journal of Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsnot available
FundersFondo para la Investigación Científica y TecnológicaConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsMagnetElectromagnetic coilPhysicsNuclear magnetic resonanceHomogeneity (statistics)Magnetic fieldElectropermanent magnetElectromagnetRadiofrequency coilDipole magnetSuperconducting magnetComputer science

Abstract

fetched live from OpenAlex

In this manuscript we describe an α-helical-cut notch–coil magnet system designed for fast switching of the magnetic field. An attempt was made to determine the extent to which such a magnet configuration can be efficiently used for fast-field-cycling (FFC) nuclear magnetic resonance (NMR) instruments. In addition to the typical technical requirements (high field-to-power ratio, adequate electric performance for fast-switching of the magnetic field and NMR-compatible magnetic field homogeneity), a tunable homogeneity within the sample volume and more uniform heat dissipation along the magnet body are included. A helical-cut notch–coil machined in metallic cylinders with external movable pieces was found to fit these requirements very well. A key factor for the optimization of the magnet parameters is the use of a novel calculation procedure based on a more realistic model that consider a magnet geometry with broken azimuthal symmetry. The aim of this paper is to theoretically compare the proposed geometry with other existing designs. No particular prototype is presented here. A clear understanding of the notch–coil performance was found to be an essential step for its further consideration as a potential autoadaptive (electronically controlled) magnet system for FFC applications.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.473

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 head0.016
GPT teacher head0.304
Teacher spread0.288 · 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 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

Citations4
Published2014
Admission routes1
Has abstractyes

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