MétaCan
Menu
Back to cohort
Record W2892603877 · doi:10.1063/1.5039737

Optical fiber-driven low energy electron gun for ultrafast streak diffraction

2018· article· en· W2892603877 on OpenAlexaff
Chiwon Lee, Günther Kassier, R. J. Dwayne Miller

Bibliographic record

VenueApplied Physics Letters · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersMax-Planck-Gesellschaft
KeywordsStreak cameraUltrafast electron diffractionStreakPicosecondOpticsReflection high-energy electron diffractionUltrashort pulseElectronElectrostatic lensElectron diffractionMaterials scienceStreakingElectron gunDiffractionPhysicsAtomic physicsCathode rayBeam (structure)Laser

Abstract

fetched live from OpenAlex

Here, we present an optical fiber-based electron gun designed for the ultrafast streaking of low-energy electron bunches. The temporal profile of the few tens of the picosecond long electron bunch composed of 200 electrons is well characterized using a customized streak camera. Detailed analysis reveals that the stretched optical trigger pulse owing to the dispersion effects inside the waveguide dominantly determines the temporal length of the low density electron bunch. This result illustrates the capability to control the observable time-window in the streak diffraction experiment by tailoring geometrical parameters of the fiber source and its coupling condition. With the electrostatic Einzel lens system integrated on the fiber-based cathode, we also demonstrate spatial focusing of the electron beam with the RMS spot size of 98 μm and imaging of the static low-energy electron diffraction pattern of monolayer graphene in the electron kinetic energy range of 1.0–2.0 keV.

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 score0.003

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 integrity0.0000.000
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 head0.005
GPT teacher head0.267
Teacher spread0.263 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physics LettersSame topicAdvanced Electron Microscopy Techniques and ApplicationsFrench-language works237,207