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Record W3130949285 · doi:10.1107/s1600576720016428

Bent Laue crystal anatomy: new insights into focusing and energy-dispersion properties

2021· article· en· W3130949285 on OpenAlexafffund
Peng Qi, Xianbo Shi, Nazanin Samadi, Dean Chapman

Bibliographic record

VenueJournal of Applied Crystallography · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNational Research Council CanadaCanada Foundation for InnovationUniversity of SaskatchewanCanadian Light Source
KeywordsBent molecular geometryOpticsRay tracing (physics)DiffractionPhysicsBeam (structure)SynchrotronMonochromatic colorX-ray opticsCrystal (programming language)RefractionSynchrotron radiationDispersion (optics)X-rayMaterials scienceComputer science

Abstract

fetched live from OpenAlex

X-ray Laue-type monochromators are common and essential optical components at many high-power X-ray facilities, e.g. synchrotron facilities. The X-ray optics of bent Laue crystals is a well developed area. An incident X-ray beam penetrating a bent Laue crystal will result in a diffracted beam with different angles and energies. There is a need for a way of organizing the rays that allows one to sort out the energy and spatial properties of the diffracted beam. The present work introduces a new approach for describing the general behaviour of bent Laue crystals from a ray-tracing point of view. This quasi-monochromatic beam approach provides an intuitive view of bent-crystal diffraction and leads to deeper understanding. It explains the energy and spatial properties of common and special cases of bent Laue optics, predicts phenomena that can improve energy-dispersion-related X-ray imaging techniques and provides a theoretical framework that makes ray-tracing simulation easier to realize.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.746

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 head0.007
GPT teacher head0.220
Teacher spread0.214 · 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.

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

Citations5
Published2021
Admission routes2
Has abstractyes

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