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Processing, Adhesion and Corrosion-inhibiting Properties of Poly[2-methoxy-5-(2’-ethylhexyloxy)-1,4-phenylene vinylene], (MEH-PPV) on Aerospace Aluminum Alloys

2021· article· en· W2139795503 on OpenAlexvenueno aff
Peter Zarras, Diane Buhrmaster, John D. Stenger‐Smith, Cindy Webber, Nicole Anderson, Paul A. Goodman, Matthew C. Davis

Bibliographic record

VenueJournal of Coating Science and Technology · 2021
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsnot available
FundersEnvironmental Security Technology Certification ProgramU.S. Department of Defense
KeywordsMaterials sciencePhenyleneAluminiumCorrosionAerospaceAdhesionChemical engineeringPolymer chemistryPolymerComposite material

Abstract

fetched live from OpenAlex

Researchers at the Naval Air Warfare Center Weapons Division (NAWCWD) and Wright-Patterson Air Force Base (WPAFB) investigated poly[2-methoxy-5-(2’-ethylhexyloxy)-1,4-phenylene vinylene], (MEH-PPV) for its potential corrosion-inhibition properties on aerospace aluminum alloy AA2024-T3. Solution processing of the polymer, as well as adhesion testing and accelerated weathering tests were performed on MEH-PPV full military aerospace coatings. Wet and dry tape adhesion testing, as well as pencil hardness, impact flexibility and pneumatic adhesion tensile test instrument (PATTI) testing were used to demonstrate the adhesion performance of MEH-PPV on aluminum substrates. The results showed that MEH-PPV had acceptable adhesion characteristics when compared to hexavalent chromium (Cr(VI)) based coatings in all of these tests. Accelerated weathering analysis was performed on MEH-PPV coatings to determine their corrosion protection and weathering resistance capabilities. These tests included neutral salt spray (NSS) exposure and xenon-arc lamp testing. The results showed that while MEH-PPV does not exhibit significant color change after 500 hours of xenon arc lamp exposure, the polymer has poor corrosion protection performance under aggressive salt environments.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.034
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.269
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, 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
Published2021
Admission routes1
Has abstractyes

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