Challenge and Solution of Characterizing Glass Transition Temperature for Conjugated Polymers by Differential Scanning Calorimetry
Bibliographic record
Abstract
ABSTRACT Thermomechanical properties of polymers highly depend on their glass transition temperature ( T g ). Differential scanning calorimetry (DSC) is commonly used to measure T g of polymers. However, many conjugated polymers (CPs), especially donor–acceptor CPs (D–A CPs), do not show a clear glass transition when measured by conventional DSC using simple heat and cool scan. In this work, we discuss the origin of the difficulty for measuring T g in such type of polymers. The changes in specific heat capacity (Δ c p ) at T g were accurately probed for a series of CPs by DSC. The results showed a significant decrease in Δ c p from flexible polymer (0.28 J g −1 K −1 for polystyrene) to rigid CPs (10 −3 J g −1 K −1 for a naphthalene diimide‐based D–A CP). When a conjugation breaker unit (flexible unit) is added to the D–A CPs, we observed restoration of the Δ c p at T g by a factor of 10, confirming that backbone rigidity reduces the Δ c p . Additionally, an increase in the crystalline fraction of the CPs further reduces Δ c p . We conclude that the difficulties of determining T g for CPs using DSC are mainly due to rigid backbone and semicrystalline nature. We also demonstrate that physical aging can be used on DSC to help locate and confirm the glass transition for D‐A CPs with weak transition signals. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019 , 57, 1635–1644
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".