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Record W2100900466 · doi:10.1142/s0218625x02001781

SOFT X-RAY MICROSCOPY OF SOFT MATTER — HARD INFORMATION FROM TWO SOFTS

2002· article· en· W2100900466 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSurface Review and Letters · 2002
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsMcMaster UniversityBrockhouse Institute for Materials Research
Fundersnot available
KeywordsSoft matterXANESContext (archaeology)Absorption (acoustics)MicroscopyMaterials scienceAbsorption edgePolymerNanotechnologyOpticsAnalytical Chemistry (journal)ChemistryOptoelectronicsPhysicsSpectroscopyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Scanning transmission X-ray microscopy (STXM) and X-ray photoelectron emission microscopy (X-PEEM) provide quantitative chemical analysis at a spatial resolution well below 100 nm. Soft X-ray absorption or near edge X-ray absorption (NEXAFS) contrast provides sensitive differentiation of species which have similar elemental composition but are chemically distinct. Due to the ability of soft X-rays at wavelengths below the O K-edge to penetrate water, and on account of lower radiation damage, soft X-ray microscopy is an ideal tool for providing quantitative information about soft matter in the context of biological, polymer and environmental studies. Examples are given from recent studies of: phase segregation in polyurethanes and polymer blends, protein adsorption on polymers relating to biomaterial optimization, and metal mapping in biofilms. These examples show that it is indeed possible to get quantitative (hard) information by combining soft X-rays and soft materials.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.258
Teacher spread0.247 · 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