Influence of polymer solution composition on the microstructure and thermal and mechanical characteristics of polycaprolactone films
Bibliographic record
Abstract
Abstract This study investigates the effects of addition of oils and alkanes to the polymer solution on the properties of polycaprolactone (PCL) films. The films were prepared by casting films onto a mold and evaporation in air. Films made without oils showed a dense microstructure. Most of the oils generated porous films, except for cyclohexane and castor oil. The neat PCL and cyclohexane films were stronger than other films, with a tensile strength of about ~11.5 MPa, followed by terpenes (~6.5 MPa) and the weakest films were obtained with hexadecane (~3.5 MPa). The elastic modulus was highest with limonene (~350 MPa), followed by cyclohexane (~150 MPa), and the lowest modulus (~65 MPa) was obtained with castor oil and hexadecane. All films showed high elongation at break (>400%). The degree of crystallinity was the highest with terpenes, whereas the lowest crystallinity was obtained with hexadecane. No significant effect of the oils on the thermal transition temperatures of the films could be observed. In summary, the microstructure and mechanical properties of the PCL films could be effectively fine‐tuned for various applications through the addition of oils and alkanes to the polymer solution. Highlights Oils and alkanes were added to polycaprolactone (PCL) casting solution. PCL films were prepared by casting films onto a mold and evaporation in air. Oils affected the microstructure and mechanical properties of the films. Addition of oils provides a flexible tool to tailor the film properties. PCL films with varying properties could be fabricated for different fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".