Rheological, Spectroscopic, and Chemical Characterization of Asphalt Binders Modified with Phase Change Materials, Polymers, and Glass Powder
Bibliographic record
Abstract
Recently, asphalt modifiers have increasingly gained attention for improving the mechanical and thermal characteristics of asphalt mixtures. As a result, innovative additives are being constantly developed to achieve this purpose. However, some modifiers can significantly impact the chemical and rheological properties of the asphalt binder. This paper investigates the rheological, spectroscopic, and chemical properties of asphalt binders modified with a bio-based phase change material (PCM) and phase change material mixed with glass powder (GPCM). Two binders were investigated, PG 58-28 and PG 70-28 polymer modified asphalt binder with 3% SBS. Two different percentages of GPCM (5% and 7%) were added to PG 58-28 and PG 70-28, and 5% PCM was added to PG 58-28. The results indicated that the PCMs effectively reduced the viscosity values of the asphalt binder. Moreover, testing the modified binders using differential scanning calorimetry (DSC) showed that the PCMs released the stored heat when the melting/freezing temperature was reached. However, adding glass powder with the PCMs negatively affected the thermal properties of PCMs in the asphalt mix. In addition, considerable changes in the stiffness of the binders modified with GPCM at an intermediate temperature were obtained when tested using DSR. Finally, the TGA results revealed that this specific type of PCM would not be suitable as a hot mix asphalt (HMA) modifier as its evaporation temperature is lower than the mixing temperature HMA. However, the use of PCM in warm mix asphalt (WMA) would be a more viable option. The results showed that the evaporation temperature for the PCMs was low; therefore, the PCMs cannot be used in HMA. In addition, modified binders with PCMs and GPCM showed lower viscosity compared to the control binder. The DSR rheological analysis showed that the control binder and 5%PCM, 5%GPCM, 7%GPCM, 5%GPCM, and 7%GPCM binders had similar overall properties. However, the addition of GPCM significantly decreases the stiffness at intermediate temperatures.
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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".