Performance Enhancement of Asphalt Mixtures Enabled by Bamboo Fibers and Acrylated Epoxidized Soybean Oil
Bibliographic record
Abstract
Bamboo fibers (BFs) have attracted much attention as a potential sustainable reinforcement material for asphalt pavements due to their high specific strength and modulus, low cost, renewability, and biodegradability. However, the key challenge of using BFs in asphalt mixtures is the weak interfacial adhesion between intrinsically hydrophilic BFs and the hydrophobic asphalt matrix. Therefore, this study proposed a surface modification method of BFs using acrylated epoxidized soybean oil (AESO) and 4,4-methylenediphenyl diisocyanate (MDI) to improve the fiber–matrix adhesion of the BFs/asphalt mixture. Fourier transform infrared spectroscopy and nuclear magnetic resonance analyses confirmed that the modified BF surface was grafted with AESO, and both of them were linked by MDI, thereby leading to the formation of a hydrophobic layer on the fiber surface. The water absorption of BFs significantly decreased after modification, and the interfacial adhesion between the modified BFs and the asphalt matrix remarkably improved. Adding the modified BFs to the mixture improved its mechanical properties, high-temperature stability, and low-temperature cracking resistance and decreased its moisture susceptibility. The work provides a feasible and industrial-scale method to enhance the road performance of the asphalt mixture by using renewable natural fibers and vegetable oils as modifying materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".