Microstructural engineering of high-content rubber asphalt via precision devulcanization for enhanced performance
Bibliographic record
Abstract
Introduction The practical deployment of high-content rubberized asphalt is often hindered by its compromised workability and unstable performance. Moving beyond conventional devulcanization approaches, this study introduces an integrated strategy of interface-controlled devulcanization and microstructural tailoring to address these challenges. Methods A bespoke devulcanizing agent (RubberSynth-AP) was synthesized to promote selective scission of sulfur-based crosslinks and improve interfacial adhesion. Coupled with an optimized production process, this method allows the stable integration of crumb rubber at concentrations up to 30% by binder weight. Multi-scale rheological analyses—encompassing temperature sweeps, multiple stress creep recovery (MSCR), and linear amplitude sweep (LAS) tests—were employed. Results An optimum rubber content of 26% was identified, exhibiting a superior combination of properties: a failure temperature of 76.5 °C, 40% lower viscosity, 53.12% recovery rate, and enhanced fatigue resistance. Mechanistic analysis uncovered a microstructural evolution from a heterogeneous, stress-concentrating system to a homogeneous, elastic-network-dominated morphology. This structural improvement supported the adoption of a dense-graded AC-13 mixture design, achieving a remarkable dynamic stability of 3,850 cycles/mm. Economically and environmentally, this technique promotes the consumption of 18 tons of waste rubber per lane-kilometer with a cost reduction of approximately ¥17,000. Discussion Collectively, this study demonstrates that the interface-controlled devulcanization strategy enables the production of high-content rubberized asphalt (up to 30%) with superior and balanced rheological properties, overcoming the longstanding workability-performance trade-off. The findings provide a scientifically-grounded and economically viable solution for developing sustainable pavement materials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".