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Record W2193380825 · doi:10.1061/9780784414255.010

Test Procedure of Utilizing Atomic Force Microscopy to Characterize Bitumen

2015· article· en· W2193380825 on OpenAlexaff
Ming Wang, Liping Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsMinistry of Education and Child Care
FundersScience and Technology Commission of Shanghai Municipality
KeywordsMicrostructureAsphaltMaterials scienceComposite materialAtomic force microscopyNanometreCantileverNanoscopic scaleOpacityModulusRheologyNanotechnologyOptics

Abstract

fetched live from OpenAlex

The microstructure of bitumen is responsible for the rheological properties and pavement performance, the change of microstructure had a significant impact on the bitumen behavior. Atomic Force Microscopy (AFM) technology has a great advantage on microstructure analysis and the microstructure had been proven to be a unique and reproducible property of a bitumen, but the challenge in applying AFM technology is to obtain samples which can reflect the true microstructure of bitumen. The paper mainly investigated the sample preparation, probe selection, and the structural characterization of the bitumen at the nanoscale by AFM. The results showed that molding method can guarantee the reproducibility and stability of sample and the AFM images obtained could reflect true microstructure of bitumen. According to basic modulus principle, nominal spring constant of AFM probe should be lower than 5 N/m at room temperature in tapping-mode. In addition, AFM cantilever material with back coating was recommended as results of the opacity of bitumen. “Bee-shaped” structure with several micrometers in length and 47–55 nanometers in height randomly distributing presented in base bitumen and SBS modified bitumen, the results were in agreement with previous studies. Although many studies reported the component of bee-shaped structure, it still remained unclear at present.

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 score0.111
Threshold uncertainty score0.426

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 head0.034
GPT teacher head0.277
Teacher spread0.243 · 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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