The effects of solvent casting temperature and physical aging on <scp>polyhydroxybutyrate‐graphene</scp> nanoplatelet composites
Bibliographic record
Abstract
Abstract Due to their unique set of properties, polymer composites reinforced with graphenic nanoparticles are materials of interest for applications such as actuators, sensors, and degradable electronic components. To implement polymer nanocomposites in such applications, it is necessary to understand how both processing conditions and aging affect their properties. This is especially important when the matrix is composed of a semicrystalline polymer susceptible to transformations due to aging. In this study, we investigate the physical properties of a biodegradable polymer nanocomposite, comprising a polyhydroxybutyrate (PHB) matrix loaded with graphene nanoplatelets (GNP) as a conductive filler. PHB/GNP nanocomposite films were prepared at different solvent casting temperatures ranging from 80 to 140°C. Results show that electrical resistivity decreased— from 42.3 Ω cm for 80°C to 3.01 Ω cm for 110°C and 1.5 Ω cm for 140°C—with increasing solvent casting temperature. Moreover, for nanocomposite films containing less than 10 wt% of GNP and processed at 80°C, we observed significant decrease in resistivity (>50%) over time when the sample was aged at room temperature. We postulate that this decrease in resistivity arises from the cold‐crystallization of PHB, as observed by X‐ray diffraction analysis, and the densification of the polymer matrix, which is a direct consequence of an increase in crystallinity of nearly 20% over 168 h of aging. These results show that understanding the aging behavior of nanocomposites made from semicrystalline polymers such as PHB is crucial when designing conductive polymer composites and active devices.
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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 | 0.001 | 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".