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Record W2096564984 · doi:10.5006/c2003-03057

EIS Investigations of Alkyd and Epoxy Coatings as They Are Chemically Stripped from Steel Panels

2003· article· en· W2096564984 on OpenAlexaff
Mike O’Donoghue, Ron Garrett, Vijay Datta, Terry J. Aben, C H Hare

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMaterial Properties and Processing
Canadian institutionsPowertech Labs (Canada)
Fundersnot available
KeywordsAlkydEpoxyMaterials scienceCorrosionComposite materialMetallurgyCoating

Abstract

fetched live from OpenAlex

Abstract Electrochemical Impedance Spectroscopy (EIS) has been employed to monitor, and model, real time chemical stripping and de-adhesion of select coatings from steel panels. Instead of the customary salt solution, a water based chemical stripper was used in a custom-built EIS cell. In effect, the stripper was simultaneously used as the electrolyte for the EIS test as well as the modus operandi for stripping the coated panels. Alkyd (3 coats) and epoxy polyamide coatings (2 coats) were compared and contrasted during the chemical stripping process. To examine the influence of pigment types in the coatings, a leafing aluminum alkyd finish in a 3 coat system was compared with an alkyd finish that contained titanium dioxide. The epoxy polyamide coating systems examined contained either non-leafing aluminum or titanium dioxide pigments. The EIS data is discussed in terms of such multivariate factors as the pigment used in the different coating types, the influence of coating chemistry and the unique chemistry of the water based chemical stripper.

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 score0.003

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 head0.016
GPT teacher head0.194
Teacher spread0.177 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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