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Record W2912887380 · doi:10.1002/aic.16569

Preventing crude oil adhesion using fully waterborne coatings

2019· article· en· W2912887380 on OpenAlexaff
Xu Wu, Yichun Zhang, Minhuan Liu, Xiubin Xu, Zhengping Wang, Ian Wyman, Hui Yang, Fanghui Liu, Jinben Wang, Jiazhong Wu

Bibliographic record

VenueAIChE Journal · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsQueen's University
Fundersnot available
KeywordsCoatingMaterials scienceMelaminePolymerSurface modificationAdhesionRaw materialChemical engineeringExtraction (chemistry)PolyurethaneSiliconeCrude oilSilicone oilPulp and paper industryNanotechnologyChemistryComposite materialOrganic chemistryPetroleum engineering

Abstract

fetched live from OpenAlex

Abstract The petroleum industry has focused on the modification of crude oil composites to decrease their adhesion onto materials and thus facilitate oil extraction, transportation, storage, and processing. However, these methods such as heating, dilution, emulsification, or additives are often accompanied by significant costs and suffer from various limitations. Herein, we present a conceptually different coating strategy that allows many substrates to repel crude oil. The novel coating was achieved via a fully waterborne polymer crosslinkable system consisting of polymer particles, a silicone surfactant, and a melamine formaldehyde resin. Considering the unique anti‐crude‐oil‐adhesion properties, the outstanding physical and chemical stability, as well as the green and industrially‐viable process involved, we anticipate that this coating can provide a promising starting point toward the functionalization of the surfaces of equipment or pipelines that are routinely exposed to crude oils.

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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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