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Record W2504983777 · doi:10.1021/bk-2012-1120.ch034

Soft X-ray Spectromicroscopy of Protein Interactions with Phase-Segregated Polymer Surfaces

2012· book-chapter· en· W2504983777 on OpenAlexafffund
Adam P. Hitchcock, Bonnie Leung, John L. Brash, A. Schöll, Andrew Doran

Bibliographic record

VenueACS symposium series · 2012
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsAlberta Environment and Protected AreasMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAdvanced Foods and Materials NetworkU.S. Department of Energy
KeywordsPolymerSynchrotronMaterials scienceNanoscopic scalePhotoemission electron microscopyPolymer substrateNanotechnologyMicroscopyTransmission electron microscopySubstrate (aquarium)Phase (matter)ChemistryAnalytical Chemistry (journal)Electron microscopeOpticsChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Quantitative spectro-microscopic characterization of the interfaces between polymers and relevant proteins helps understand fundamental issues of protein – polymer interactions and can provide insights into biocompatibility. Synchrotron based X-ray photoemission electron microscopy (X-PEEM) and scanning transmission X-ray microscopy (STXM) are being used to study distributions of proteins adsorbed on chemically heterogeneous polymer surfaces with ~30 nm spatial resolution. The relevant contrast in each technique is X-ray absorption spectroscopy which provides speciation and quantitation of both adsorbed proteins or peptides (and in combinations), simultaneously with chemically sensitive imaging of the underlying polymer substrate. An overview of recent progress in this field is given, along with some comparisons to complementary techniques (AFM and TOF-SIMS) for investigating protein-polymer interfaces.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.252
Teacher spread0.243 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

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