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Record W2112762258 · doi:10.1039/c4fd00229f

Mapping water uptake in organic coatings using AFM-IR

2015· article· en· W2112762258 on OpenAlexaff
Suzanne Morsch, S.B. Lyon, Paula J. Greensmith, Steven D. Smith, Simon R. Gibbon

Bibliographic record

VenueFaraday Discussions · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsAkzoNobel (Canada)
FundersAkzoNobel
KeywordsPolymerMoistureChemical engineeringMaterials scienceHydrogen bondCorrosionIonic bondingNanoscopic scaleEpoxyImmersion (mathematics)Fourier transform infrared spectroscopySorptionChemistryNanotechnologyComposite materialMoleculeIonOrganic chemistry

Abstract

fetched live from OpenAlex

The long-term failure of seemingly intact corrosion resistant organic coatings is thought to occur via the development of ionic transport channels, which spontaneously evolve from hydrophilic regions on immersion, i.e., as a result of localized water uptake. To this end, we investigate water uptake characteristics for industrial epoxy-phenolic can coatings after immersion in deionized water and drying. Moisture sorption and the changing nature of polymer-water interactions are assessed using FTIR for dry and pre-soaked films. More water is found to be absorbed by the pre-soaked coatings on exposure to a humid environment, with a greater degree of hydrogen-bonding between the polymer and water. Furthermore, morphological changes are then correlated to localized water uptake using the AFM-IR technique. Nanoscale softened regions develop on soaking, and these are found to absorb a greater proportion of water from a humid environment.

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)0.0000.000
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)0.0010.000

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 head0.047
GPT teacher head0.271
Teacher spread0.224 · 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

Citations74
Published2015
Admission routes1
Has abstractyes

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