Interfacial adhesion mechanism between asphalt and aggregate with different lithology in acid–alkaline aqueous solutions
Bibliographic record
Abstract
Strong adhesion at the asphalt–aggregate interface is vital for asphalt mixtures’ mechanical properties, but water damage can erode this adhesion, causing pavement distress. This study analyzed the effect of aggregate lithology (basalt, granite, and two limestones) on the water damage resistance of asphalt interfaces in acid–alkaline solutions using the boiling method, atomic force microscopy (AFM), and zeta potential measurements. AFM-measured interaction forces aligned with Derjaguin–Landau–Verwey–Overbeek theory. Acidic conditions showed higher adhesion forces and lower repulsive forces compared to alkaline conditions. Zeta potential values and long-range forces decreased with rising pH. Adhesion order in solution was limestone A > limestone B > basalt > granite. These findings reveal how aggregate lithology influences asphalt adhesion, aiding material selection for durable pavements.
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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".