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Record W4408404449 · doi:10.34133/adi.0088

Double-Illumination All-Optical Nanoalignment for Stacking X-Ray Fresnel Zone Plates

2025· article· en· W4408404449 on OpenAlexafffund
Siqi Wang, Cheng Jiang, Robert Peters, Mirwais Aktary, Jinyang Liang

Bibliographic record

VenueAdvanced Devices & Instrumentation · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsApplied Nanotools (Canada)Institut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsZone plateOpticsStackingFresnel zoneMaterials sciencePhysicsDiffraction

Abstract

fetched live from OpenAlex

Fresnel zone plate (FZP) stacking is an approach to enhance diffraction efficiency in x-ray focusing and imaging by physically aligning 2 or more FZPs to increase the effective thickness and optical efficiency. Existing methods face limitations in x-ray-source availability, controlled degrees of freedom, alignment accuracy, and contact-induced friction. To overcome these limitations, we develop a double-illumination all-optical nanoalignment (DIANA) system. Designed to leverage the x-ray FZP chip’s nanopatterned characteristics, DIANA uses coherent illumination in reflection mode to implement laser interferometry for accurate tilt and yaw adjustment. It also employs incoherent illumination in transmission mode for high-contrast nanoalignment using Vernier/main scales and moiré fringes. Using DIANA, we demonstrate the stacking of 2 x-ray FZPs with under 30-nm alignment accuracy.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.325
Teacher spread0.313 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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