An investigation on the relationship between FTIR indices and surface free energy of RAP binders
Bibliographic record
Abstract
The performance of RAP bituminous mixes depends on the properties of the binder in the RAP material. Surface free energy of a binder, which is a fundamental property of materials, can be used to explain moisture damage, rutting, fracture and healing potential of bituminous mixes. Similarly, different chemical properties of binders can be used to estimate their mechanical properties. The relationship between the surface free energy of the binders and their chemical composition (in terms of functional groups) has been investigated in this study. For this purpose, blends of a virgin binder (VG30 grade) and the RAP binder extracted from a RAP material were prepared. Surface free energy of the binder blends was measured using sessile drop technique. The chemical composition of the blends was obtained using Fourier transform infrared spectroscopy (FTIR) analysis. Different ageing and healing indices were calculated from FIR analysis. Different ageing indices are found to have good correlation with surface free energy and its components. No significant relationship could be observed between healing indices and surface free energy of the binder blends. The correlations presented in the study between different indices estimated from FTIR analysis and the surface free energy components obtained by sessile drop method will be useful in furthering the understanding of the characteristics of binders, especially RAP binders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".