Application of differential scanning calorimetry as advanced asphalt testing technology: A comprehensive review
Bibliographic record
Abstract
Understanding thermal mechanisms is crucial for the selection, modification, and application of asphalt binders. Differential scanning calorimetry (DSC), a sensitive calorimetric technique, enables quantitative assessment of heat-flow responses and reveals underlying processes. This review summarizes the principles and parameter choices of conventional DSC and modulated temperature DSC, covering specimen mass, heating and cooling rates, purge gas, and baseline treatment, and outlines practical workflows for erasing or preserving thermal history, configuring thermal cycles, and implementing isothermal holds. In terms of applications, DSC determines glass transition temperature, crystallization and melting behavior, enthalpy relaxation, and heat capacity; relates thermal signatures to rheology and to performance at low and high temperatures; and investigates oxidative and thermoreversible aging through kinetic analysis. For modified binders, DSC elucidates modification mechanisms, estimates modifier content, assesses compatibility, and evaluates storage stability and the tendency toward phase separation. The technique offers high precision with small sample requirements, enabling differentiation among asphalt sources and grades, analysis of thermal history, and rapid screening. Nevertheless, limitations persist, including thermal gradients, volatilization losses, and baseline drift. Coupling with dynamic shear rheometry, thermogravimetric analysis, infrared spectroscopy, and microscopy further connects thermodynamic features to microstructure and functional performance. Future directions include establishing standardized test protocols, extracting more detailed information from DSC and linking it more directly to asphalt pavement performance, building thermal fingerprint databases for identification and quality control, and developing efficient workflows that support materials design.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".