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Record W2892603877 · doi:10.1063/1.5039737

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

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.741

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 head0.005
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