Mitigating degradation-induced artifacts in rheological modeling of biopolymers using time-resolved rheology
Bibliographic record
Abstract
• A systematic approach to constructing realistic rheological modeling is presented. • TRR used to mitigate degradation artifacts in rheological modeling of biopolymers. • Decoupling the time-dependent structural mutations from rheological data presented. Rheological analysis of biodegradable polymers is often complicated by structural mutations and thermal degradation during testing, leading to inaccurate data and unreliable modeling. These effects are particularly pronounced in conventional small-amplitude oscillatory shear (SAOS) experiments, which require extended exposure to elevated temperatures. In this study, an alternative approach is introduced based on time-resolved rheometry (TRR) to minimize the impact of degradation and isolate intrinsic rheological behavior. By capturing data across different timescales, this method decouples degradation kinetics from rheological responses, enabling the construction of more accurate flow curves and material functions. The effectiveness of this approach was validated by comparing it to conventional SAOS protocols across several polyhydroxyalkanoates. Our results show that TRR-based measurements yield more reliable predictions of viscoelastic properties, including relaxation moduli and startup shear viscosities. The improved data quality leads to superior fits in constitutive equation modeling. This methodology offers a more efficient and degradation-resistant strategy for rheological testing, with significant implications for optimizing the processing and performance of biodegradable polymers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".