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Record W2899900058 · doi:10.1063/1.5091103

Development of an adjustable Kirkpatrick-Baez microscope for laser driven x-ray sources

2019· article· en· W2899900058 on OpenAlexaff
G. Zeraouli, G. Gatti, Andrew Longman, J. A. Pérez-Hernández, D. Arana, D. Batani, K. Jakubowska, L. Volpe, L. Roso, R. Fedosejevs

Bibliographic record

VenueReview of Scientific Instruments · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOpticsLaserMicroscopeWaferMaterials scienceTotal external reflectionSiliconOptoelectronicsPhysicsTotal internal reflection

Abstract

fetched live from OpenAlex

A prototype of a highly adjustable Kirkpatrick-Baez (KB) microscope has been designed, built, and tested in a number of laser driven x-ray experiments using the high power (200 TW) VEGA-2 laser system of the Spanish Centre for Pulsed Lasers (CLPU). The presented KB version consists of two, perpendicularly mounted, 500 μm thick silicon wafers, coated with a layer of platinum, a few tens of nanometers thick. Unlike the usual millimeter thick glass substrate, this design allows for a larger bending flexibility and large adjustment range. According to simulations, this KB microscope offers broadband multikiloelectron volt reflection spectra (1 eV-20 keV), allowing more spectral tunability than conventional Bragg crystals. In addition to be vacuum compatible, this prototype is characterized by a relatively small size (21 cm × 31 cm × 27 cm) and permits remote control and modification both of the radii of curvature (down to 10 m) and of the grazing incidence angle (up to 60 mrad). A few examples of focusing performance tests and experimental results are discussed.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.290
Teacher spread0.276 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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