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Record W4289542070 · doi:10.5281/zenodo.6385030

Generation of electron vortices using non-exact electric fields

2018· article· en· W4289542070 on OpenAlexafffund
Amir H. Tavabi, Hugo Larocque, Peng‐Han Lu, Martial Duchamp, Vincenzo Grillo, Ebrahim Karimi, Rafal E. Dunin‐Borkowski, Giulio Pozzi

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of Ottawa
FundersHorizon 2020 Framework ProgrammeCanada Research ChairsDeutsche ForschungsgemeinschaftEuropean Commission
KeywordsVortexElectronElectric fieldPhysicsQuantum electrodynamicsCondensed matter physicsQuantum mechanicsMechanics

Abstract

fetched live from OpenAlex

Vortices in electron beams can manifest several types of topological phenomena, such as the formation of<br> exotic structures or interactions with topologically structured electromagnetic fields. For instance, the wavefunction of an electron beam can acquire a phase vortex upon propagating through a magnetic monopole, which, in practice, provides a convenient method for generating electron vortex beams. Here, we show how an electric field must be structured in order to achieve a similar e ect. We find that, much as in the case of magnetic fields, closed but not exact electric fields can produce electron vortex beams. We proceed by fabricating a versatile near-obstruction-free device that is designed to approximately produce such fields and we systematically study their influence on incoming electron beams. With such a single device, electron vortex beams that are defined by a wide range of topological charges can be produced by means of a slight variation of an applied voltage. For this reason, this device is expected to be important in applications that rely on the sequential generation and manipulation of di erent types of electron vortices

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.237
Teacher spread0.212 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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