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Record W268093852 · doi:10.1063/1.2234301

Molecular bond selective x-ray scattering for nanoscale analysis of soft matter

2006· article· en· W268093852 on OpenAlexaff
G. E. Mitchell, John G. Lyons, I. Koprinarov, E. M. Gullikson, J. B. Kortright

Bibliographic record

VenueApplied Physics Letters · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScatteringSoft matterNanometreCarbon fibersPolymerChemical physicsNanoscopic scaleAbsorption (acoustics)ChemistryChemical bondMaterials scienceNanotechnologyMolecular physicsAnalytical Chemistry (journal)OpticsPhysicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

We demonstrate the utility of resonant soft x-ray scattering in characterizing heterogeneous chemical structure at nanometer length scales in polymer films and nanostructures. Resonant enhancements near the carbon K edge bring bond specific contrast and increased sensitivity to bridge a gap between x-ray absorption contrast in chemical sensitive imaging and higher spatial resolution hard x-ray and neutron small-angle scattering. Chemical bond sensitivity is illustrated in the scattering from latex spheres of differing chemistry and size. Resonant enhancements are then shown to yield sensitivity to heterogeneity in two-phase polymer films for which hard x-ray and nondeuterated neutron scattering lack sensitivity due to low contrast.

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 categoriesMeta-epidemiology (narrow)
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 score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.213
Teacher spread0.209 · 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 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

Citations0
Published2006
Admission routes1
Has abstractyes

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