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Record W3028276709 · doi:10.3791/61130

Cryogenic Liquid Jets for High Repetition Rate Discovery Science

2020· article· en· W3028276709 on OpenAlexafffund
C. B. Curry, Christopher Schoenwaelder, Sebastian Goede, Jongjin B. Kim, Martin Rehwald, F. Treffert, Karl Zeil, S. H. Glenzer, M. Gauthier

Bibliographic record

VenueJournal of Visualized Experiments · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
FundersSLAC National Accelerator LaboratoryHorizon 2020 Framework ProgrammeOffice of ScienceLaserlab-EuropeFusion Energy SciencesU.S. Department of EnergyNatural Sciences and Engineering Research Council of CanadaNational Nuclear Security AdministrationNational Science Foundation
KeywordsCryogenicsJet (fluid)Nuclear engineeringAccelerationPhysicsParticle acceleratorLaserMechanicsMaterials scienceNuclear physicsOpticsThermodynamicsBeam (structure)Classical mechanicsEngineering

Abstract

fetched live from OpenAlex

This protocol presents a detailed procedure for the operation of continuous, micron-sized cryogenic cylindrical and planar liquid jets. When operated as described here, the jet exhibits high laminarity and stability for centimeters. Successful operation of a cryogenic liquid jet in the Rayleigh regime requires a basic understanding of fluid dynamics and thermodynamics at cryogenic temperatures. Theoretical calculations and typical empirical values are provided as a guide to design a comparable system. This report identifies the importance of both cleanliness during cryogenic source assembly and stability of the cryogenic source temperature once liquefied. The system can be used for high repetition rate laser-driven proton acceleration, with an envisioned application in proton therapy. Other applications include laboratory astrophysics, materials science, and next-generation particle accelerators.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.029
GPT teacher head0.395
Teacher spread0.366 · 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

Citations19
Published2020
Admission routes2
Has abstractyes

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