Advanced Thermal Characterization of Styrene-Butadiene-Styrene (SBS)– Modified Asphalt Binders at Various SBS Contents and Long-Term Aging Levels
Bibliographic record
Abstract
Styrene-butadiene-styrene (SBS) is an elastomeric copolymer that improves asphalt materials resistance to thermal and fatigue cracking. Different SBS-modified asphalt binders were evaluated by means of unmodulated and modulated differential scanning calorimetry (DSC) to determine their glass transition temperature ( T g ), glass transition width ( T g , width ), and the existence of crystallizable fractions in the base binders. Furthermore, to assess the impacts of different SBS content and long-term aging on asphalt binders’ thermal characteristics, this study used the thermogravimetric analysis (TGA) method in air and under nitrogen purge. This research aims to improve the understanding of the compatibility of SBS with various base binders and their impact on binder thermal characteristics at various aging levels. To be precise, this systematic study investigated SBS-modified binders at various SBS concentrations (0%, 2%, 4%), for three base binders across various pressure aging vessel conditioning at 20, 40, and 60 h. Additionally, these 27 binders were evaluated using Superpave performance grading (PG) standard tests and the Fourier-transfer infrared spectroscopy. Results suggest that long-term aging generally increases binder T g , leading to stiffer binders, while the inclusion of higher SBS content was found to effectively lower T g , thereby enhancing the flexibility and low-temperature performance of the binders. Additionally, SBS increases the T g , width reflecting the increased heterogeneity of the multiphase modified binders. Although aging is expected to increase the T g , width , the presence of higher SBS content helps stabilize the T g , width across aging levels. Finally, the rate of maximum degradation from TGA was found to be useful for quantitative analysis of SBS after calibration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".