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Record W4410211710 · doi:10.3390/cryst15050443

Finding Crystal Orientations in Uniplanar Textures

2025· article· en· W4410211710 on OpenAlexaff
Josef Simbrunner, F Gasser, Ingo Salzmann, Roland Resel

Bibliographic record

VenueCrystals · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsConcordia University
FundersDeutsches Elektronen-SynchrotronAustrian Science FundHORIZON EUROPE Framework ProgrammeElettra-Sincrotrone Trieste
KeywordsMaterials scienceCrystal (programming language)CrystallographyGeologyComputer scienceChemistry

Abstract

fetched live from OpenAlex

The crystallization of molecular materials on isotropic substrates typically results in a so-called fiber or uniplanar texture that comprises crystallites that share a common fiber axis perpendicular to the substrate surface, but that are azimuthally randomly oriented. The crystallographic characterization of such films is commonly performed by grazing-incidence X-ray diffraction. Thereby, two-dimensional reciprocal space maps are obtained that incorporate the in-plane component qxy and the out-of-plane component qz for each diffraction peak. The exact position of each diffraction peak depends on the crystallographic lattice and on the orientation of the unit cell relative to the substrate surface. The unit cell orientation can be characterized either by two rotation angles or by the Miller indices of the crystallographic plane (contact plane) parallel to the substrate surface. Equations are derived that allow the calculation of these orientation parameters and describe the relations between them. Depending on the crystallographic system of the underlying unit cell and its contact plane, manifold possible orientations may exist due to the multiplicity of planes contributing to the same reflections. Examples based on molecular crystals of pentacenequinone, diindenoperylene, and binaphthalene are discussed, which are illustrative examples comprising triclinic, monoclinic, and tetragonal unit cells having two, four, and sixteen possible crystal orientations, respectively.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.997

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)0.0040.000

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 head0.031
GPT teacher head0.273
Teacher spread0.242 · 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 designNot applicable
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 routes1
Has abstractyes

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