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Record W2112762258 · doi:10.1039/c4fd00229f

Mapping water uptake in organic coatings using AFM-IR

2015· article· en· W2112762258 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

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.

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 score0.408
Threshold uncertainty score0.999

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)0.0020.001

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 head0.047
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