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Record W2897134753 · doi:10.1063/1.5048521

A magnetically focused molecular beam source for deposition of spin-polarised molecular surface layers

2018· article· en· W2897134753 on OpenAlexaff
Constantin R. Krüger, E. Lisitsin-Baranovsky, Oren Ofer, Pierre-Alexandre Turgeon, Jonathan Vermette, Patrick Ayotte, Gil Alexandrowicz

Bibliographic record

VenueThe Journal of Chemical Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversité de Sherbrooke
FundersHorizon 2020 Framework ProgrammeFP7 Ideas: European Research CouncilIsrael Science Foundation
KeywordsSpin (aerodynamics)Relaxation (psychology)Molecular beamChemistryMolecular beam epitaxyChemical physicsMoleculeMolecular physicsAnalytical Chemistry (journal)Nuclear magnetic resonanceMaterials scienceLayer (electronics)NanotechnologyPhysics

Abstract

fetched live from OpenAlex

Separating molecular spin isomers is a challenging task, with potential applications in various fields ranging from astrochemistry to magnetic resonance imaging. A new promising method for spin-isomer separation is magnetic focusing, a method which was shown to be capable of producing a molecular beam of ortho-water. Here, we present results from a modified magnetic focusing apparatus and show that it can be used to separate the spin isomers of acetylene and methane. From the measured focused profiles of the molecular beams and a numerical simulation analysis, we provide estimations for the spin purity and the significantly improved molecular flux obtained with the new setup. Finally, we discuss the spin-relaxation conditions which will be needed to apply this new source for measuring nuclear magnetic resonance signals of a single surface layer.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.274
Teacher spread0.264 · 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 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

Citations24
Published2018
Admission routes1
Has abstractyes

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