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Record W4238425442 · doi:10.22215/etd/2015-11088

Development and Evaluation of Anti-corrosion and Anti-bacterial Polymer Coatings for Circulation Coins

2015· dissertation· en· W4238425442 on OpenAlexaboutno aff
Bingjie Xiao

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsCorrosionPolyurethaneAnti-corrosionCoatingMetallurgyMaterials sciencePolymerIdeal (ethics)Circulation (fluid dynamics)Composite materialEngineeringLawPolitical science

Abstract

fetched live from OpenAlex

Loonies and quarters, made of a carbon steel core with multi-ply brass plating and nickel plating, have been used as Canadian circulation coins for decades. More recently, the Royal Canadian Mint has proposed additional requirements for the coins to have anti-wear, anti-corrosion and anti-bacterial functions. Therefore an effective coating with such functions is explored in this study. Five candidate polymers are investigated; they are polyurethane, advanced liquid glass, silicone R-2180, polytetrafluoroethylene, and standard liquid glass. Among them, only polyurethane, advanced liquid glass and silicone R-2180 have passed the preliminary appearance examination and are subjected to further testing. Anti-corrosion test in artificial human sweat environment is conducted on the coated coins. The experimental results demonstrate that these three polymer coatings have good anti-corrosion ability on quarters, and for loonies, the advanced liquid glass and silicone coatings exhibit better performance. Anti-bacterial test is performed on the advanced liquid glass coating by adding E. coli onto the coin specimens, and then the amounts of bacteria left on the specimens are calculated after certain durations. The experimental results show that the advanced liquid glass coating has better bacterial resistance than uncoated quarters, but it is not as good as loonies with brass plating.

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)0.000
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 score0.010
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.055
GPT teacher head0.331
Teacher spread0.277 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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