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Record W4307436733 · doi:10.3390/ma15217551

Shear Viscosity Overshoots in Polymer Modified Asphalts

2022· article· en· W4307436733 on OpenAlexafffund
Giovanni Polacco, Ludovit Zanzotto

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCenovus EnergyU.S. Department of Veterans Affairs
KeywordsAsphalteneAsphaltMaterials sciencePolymerComposite materialViscosityScanning electron microscopeRheologyShear (geology)Chemical engineering

Abstract

fetched live from OpenAlex

Polymer modification is one of the most common methods for improving the performance of asphalt binders. Despite in-depth research, the structural modifications induced by polymers are still not well understood. In this work, steady shear viscosity measurements and cryo-scanning electron microscopy (cryo-SEM) were used to better understand the internal structure of asphalts modified by styrene-butadiene-styrene with and without sulfur as a crosslinking agent, asphalts modified by polyphosphoric acid (PPA), and quaternary asphalt blends modified by SBS, sulfur, and PPA. The results showed that polymer and asphaltenes collaborate, thus SBS forms a three-dimensional network strengthened by asphaltenes clusters. The strength, extension, and physical nature of such a network is revealed by the appearance of overshoots in the viscosity curves. Moreover, the indirect information deduced from the magnitude and shape of the shear viscosity curves successfully correlated with direct observations of the internal structure by cryo-SEM. Steady shear viscosity is thus recommended as a useful tool in studying the structural development of asphalts modified by different technologies.

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 categoriesInsufficient payload (model declined to judge)
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 score0.121
Threshold uncertainty score0.997

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)0.0040.000

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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.

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

Citations7
Published2022
Admission routes2
Has abstractyes

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