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Record W4200125464 · doi:10.1061/9780784483565.081

The Thermoelectric Effect and High-Temperature Characteristics of Carbon Nanotubes Modified Asphalt Concrete

2021· article· en· W4200125464 on OpenAlexaff
Yuhao Liu, Hui Liao, Fang Zhou, Xiaoming Huang

Bibliographic record

VenueCICTP 2021 · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceThermoelectric effectAsphaltAsphalt concreteComposite materialCarbon fibersEngineering physicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

The addition of Polymer or nano-particle generally improves the viscosity, rheological properties, and fatigue resistance of asphalt binder. However, the thermoelectric effect of nano-modified asphalt concrete has been barely investigated. In some regions, the surface temperature of asphalt pavement can reach 55°C, which is due to consistent solar radiation, whereas the base of road maintains a relatively stable temperature (i.e. 27°C to 33°C). The notable temperature gradient between the surface and base may be utilized to generate electrical power. Asphalt binder and concrete behave as insulators, thus carbon nanotube (CNT), a conductive nanomaterial, is probably a promising modifier. Various contents of CNTs were added into asphalt binder to examine the high-temperature characteristics, including penetration, ductility, and softening point. Rutting laboratory experiments were also carried out on modeled specimens to simulate field circumstances. Furthermore, an innovative thermoelectric effect experiment was conducted. The results indicate the addition of CNTs effectively improve the high temperature stability of modified asphalt concrete. The thermoelectric performance of modified asphalt concrete is considerable, with an electromotive force rate of 21.5 µV/°C.

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 score0.182
Threshold uncertainty score0.330

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 head0.003
GPT teacher head0.184
Teacher spread0.180 · 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
Published2021
Admission routes1
Has abstractyes

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