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Record W2515369749 · doi:10.1149/ma2016-02/14/1349

Corrosion Protection of Zinc Alloy Coated Steel By Organic Coatings Pigmented with Strontium Aluminium Polyphosphate

2016· article· en· W2515369749 on OpenAlexaff
Yanwen Liu, Xiaorong Zhou, S.B. Lyon, Seyedgholamreza Emad, Teruo Hashimoto, A. Gholinia, Derek Graham, Simon R. Gibbon, David K. Francis

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsAkzoNobel (Canada)
Fundersnot available
KeywordsCorrosionStrontiumZincPolyphosphateAluminiumAlloyMaterials scienceMetallurgyCorrosion inhibitorScanning electron microscopeZinc phosphateNuclear chemistryPhosphateChemistryComposite material

Abstract

fetched live from OpenAlex

Strontium aluminium polyphosphate (SAPP) inhibitive pigment formulated into an organic primer for corrosion protection of zinc alloy coated steel in 0.6 M sodium chloride solution was investigated by serial block face imaging using scanning electron microscopy, which provided detailed information on inhibitor releasing, transportation and reaction with zinc alloy substrate. It was found that SAPP contains at least two distinct particle types, one which is strontium rich and another which is aluminium rich. These particles have different release characteristics which are dependent on the local environment. The strontium rich particles release their components faster in near neutral and acidic environment compared with the aluminium rich particles whilst in an alkaline environment the reverse is found. The pH related inhibitor release process and the mixture of the inhibitive pigment with different strontium to aluminium ratios ensure the availability of the inhibitor at both anodic and cathodic locations, which consequently reduced/retarded the corrosion of zinc alloy coated steel.

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 head0.012
GPT teacher head0.222
Teacher spread0.209 · 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
Published2016
Admission routes1
Has abstractyes

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Same venueECS Meeting AbstractsSame topicCorrosion Behavior and InhibitionFrench-language works237,207