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Optical characterization of temperature- and composition-dependent microstructure in asphalt binders
1Department of Physics, The University of Texas at Austin, Austin, Texas, U.S.A.
Journal of Microscopy
|November 24, 2015
Summary
Optical microscopy reveals distinct asphalt binder microstructures, "bees" and "ants," whose behavior during heating and cooling cycles correlates with changes in bulk properties. These findings offer insights into asphalt binder performance.
Area of Science:
- Materials Science
- Chemical Engineering
- Physical Chemistry
Background:
- Asphalt binder microstructure significantly influences its performance and aging characteristics.
- Understanding the dynamic behavior of these microstructures under thermal stress is crucial for predicting asphalt durability.
Purpose of the Study:
- To characterize asphalt binder microstructure using noncontact optical methods.
- To correlate microstructural changes with bulk rheological properties over a wide temperature range (15°C to 85°C).
Main Methods:
- Noncontact optical microscopy and optical scattering techniques were employed.
- Measurements were benchmarked against rheometric measurements of the complex shear modulus |G*(T)|.
- Two compositionally different asphalt binders were analyzed across thermal cycles.
Main Results:
- Elongated 'bees' microstructures were resolved optically at the surface, disappearing after thermal cycling.
- Smaller, bulk-residing 'ants' microstructures persisted through thermal cycles and scattered light strongly.
- Optical scattering from 'ants' exhibited hysteresis with temperature, mirroring rheometric |G*(T)| hysteresis, suggesting microstructural volume fraction changes.
Conclusions:
- Optical methods can effectively characterize asphalt binder microstructures and their thermal responses.
- The observed hysteresis in optical scattering and rheology indicates thermally driven microstructural changes impact bulk binder properties.
- These findings provide a new optical approach to assess asphalt binder behavior and potential for performance prediction.
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