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Record W4381798474 · doi:10.3390/su15139950

Study on the Reflective Principle and Long-Term Skid Resistance of a Sustainable Hydrophobic Hot-Melt Marking Paint

2023· article· en· W4381798474 on OpenAlexaff
Jun Chen, Rui Li, Yang Zhang, Yi Wu, Haiqi He

Bibliographic record

VenueSustainability · 2023
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversité Laval
FundersDepartment of Transportation of Zhejiang ProvinceU.S. Department of Transportation
KeywordsSkid (aerodynamics)RetroreflectorMaterials scienceDurabilityCoatingComposite materialAbrasion (mechanical)Coefficient of frictionAsphaltTileForensic engineeringEngineeringOptics

Abstract

fetched live from OpenAlex

Road marking is very important for driving safety and reducing the accident rate as a basic component of highway construction. However, traditional road marking paints are prone to be worn after short-term application and have poor durability and reflective performance. To address these problems, the marking paint was modified using the organic polymer material polytetrafluoroethylene to create a durable hydrophobic hot-melt marking paint. The factors affecting the reflective performance of marking lines are analyzed, and artificial accelerated abrasion tests were carried out to analyze the skid resistance and marking retroreflection coefficient of hydrophobic coatings. Results show that the texture of the glass beads and the quality of the coating plays a major role in the reflective performance of the marking line. The friction coefficient value of the modified marking paint is 4.62% higher than that of the traditional hot-melt marking paint. The retroreflection performance of the marking paint with 4% hydrophobic material is 8.45% higher than the initial value of the retroreflection coefficient of the traditional hot-melt marking paints. This sustainable hydrophobic hot-melt marking paint is safer and more durable than traditional pavement marking paints, which may save follow-up maintenance resources and cost from the sustainable aspect.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.306
Teacher spread0.287 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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