MétaCan
Menu
Back to cohort
Record W2913969256 · doi:10.3390/coatings9020110

Investigation of the Performance of ATH Powders in Organic Powder Coatings

2019· article· en· W2913969256 on OpenAlexafffund
Weihong Li, Diego Cárdenas Franco, Marshall Shuai Yang, Xinping Zhu, Haiping Zhang, Yuanyuan Shao, Hui Zhang, Jesse Zhu

Bibliographic record

VenueCoatings · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGloss (optics)Materials scienceEpoxyComposite materialPowder coatingPolyesterPolyurethaneCoatingCorrosionPolymer

Abstract

fetched live from OpenAlex

Aluminum trihydrate (ATH) is one of the most widely used fillers in solvent-borne coatings and polymer industries. To investigate its performance in powder coatings, four different pigment to binder ratios (P/B) were used in respect to different ATH concentrations in four most widely used resin systems (epoxy, epoxy-polyester, polyurethane and polyester). The angle of repose (AOR) and specific gravity of powder paints increase accordingly with ATH concentration. The mechanical properties, corrosion and UV resistance performances of coating films are enhanced or only slightly decreased with the addition of ATH. Pencil scratch hardness increase significantly with the increase of ATH content. Impact resistance decreases within 5% of the initial resistance at the maximum ATH loading. There is a slightly decrease in corrosion resistance performances of less than 2 mm of failure at scribe in all formulations. The incorporation of ATH has slight effect on the UV resistance performance of all the samples. Moreover, the matting effect of ATH is observed for all the resin systems, especially epoxy, with the specular gloss decreasing more than 70% at P/B of 0.75.

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)0.000
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 score0.155
Threshold uncertainty score0.329

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)0.0000.000

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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCoatingsSame topicPolymer Nanocomposites and PropertiesFrench-language works237,207