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Record W3210435577 · doi:10.1063/5.0058939

A plastic feedthrough suitable for high-voltage DC femtosecond electron diffractometers

2021· article· en· W3210435577 on OpenAlexafffund
Patrick Gicala, Ariel A. Petruk, Nicolás Rivas, Sam Netzke, Kostyantyn Pichugin, Germán Sciaini

Bibliographic record

VenueReview of Scientific Instruments · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationOntario Research Foundation
KeywordsFeedthroughMaterials scienceVoltageHigh voltageFemtosecondUltrashort pulseOptoelectronicsElectronAcceleration voltageTorrCathode rayLaserOpticsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Highly energetic ultrashort electron bunches have the potential to reveal the ultrafast structural dynamics in relatively thicker in-liquid samples. However, direct current voltages higher than 100 kV are exponentially difficult to attain as surface and vacuum breakdown become an important problem as the electric field increases. One of the most demanding components in the design of a high-energy electrostatic ultrafast electron source is the high voltage feedthrough (HVFT), which must keep the electron gun from discharging against ground. Electrical discharges can cause irreversible component damage, while voltage instabilities render the instrument inoperative. We report the design, manufacturing, and conditioning process for a new HVFT that utilizes ultra-high molecular weight polyethylene as the insulating material. Our HVFT is highly customizable and inexpensive and has proven to be effective in high voltage applications. After a couple of weeks of gas and voltage conditioning, we achieved a maximum voltage of 180 kV with a progressively improved vacuum level of 1.8 × 10−8 Torr.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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.

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

Citations1
Published2021
Admission routes2
Has abstractyes

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