Impact of Nanoclays on Chemical Fractions and Mechanical Performance of Asphalt Binders
Bibliographic record
Abstract
Asphalt binder often is modified to obtain better performance of the pavement. Some recent studies have considered nanoclay as an alternative to currently practiced styrene-butadiene-styrene (SBS) modification to reduce the asphalt binder’s overall cost. This study evaluated any notable changes in the chemical composition of the binder due to the nanoclay modification and investigated any correlation between the chemical composition and mechanistic properties of the asphalt binder. It was found that changes in the chemical composition of nanoclay-modified binders are crude source–dependent. After nanoclay modification, the binders that originated from the Arabian crude were found to be more acidic than the binders from the Canadian crude source. However, nanoclays did not have any notable impact on the polarity of the tested asphalt binders regardless of their crude sources. The saturates, aromatics, resins, and asphaltenes (SARA) analysis results of asphalt binders revealed that the n-heptane insoluble contents increased notably after modification with nanoclays, whereas the saturates content decreased due to aging. Some mechanical properties (e.g., viscosity and rutting factor) were found to be correlated with n-heptane insoluble contents. Moreover, the sessile drop analysis test result showed that the nanoclay-modified binders offered superior bonding with gravel than with sandstone. Multiple linear regression analyses suggest that there is a fair (R2=0.55) correlation between the binders’ SARA components and compatibility for gravel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".