Electrospinning and Rheological Characterization of Polyethylene Terephthalate and Polyvinyl Alcohol with Different Degrees of Hydrolysis Incorporating Molecularly Imprinted Polymers
Bibliographic record
Abstract
This study investigates the electrospinning and rheological properties of polyethylene terephthalate (PET) and polyvinyl alcohol (PVA) with varying degrees of hydrolysis (DH) for molecularly imprinted polymer (MIP) incorporation. The morphology and properties of the electrospun nanofibers were evaluated, revealing that PVA nanofibers exhibited smoother and more uniform structures compared to PET fibers. The rheological behavior of the polymer solutions was also characterized, showing that PVA 99 DH solution exhibited shear-thinning behavior due to the unique structural properties of the polymer chains. The introduction of MIP and NIP additives had no significant impact on the rheological properties, except for PVA 99 MIP and NIP solutions, which showed deviations from Newtonian behavior. The electrospun MIP nanofibers showed a conductivity of 1054 µS/cm for PVA (87-90% DH) and a viscosity of 165.5 mPa·s, leading to optimal fiber formation, while displaying a good adsorption capacity of 0.36 mg for PVA-MIP to effectively target pharmaceuticals such as emtricitabine and tenofovir disoproxil, showing their potential for advanced water treatment applications. The results suggest that the electrospinning process and rheological properties of the polymer solutions are influenced by the molecular structure and interactions within the polymer matrix, which can be exploited to tailor the properties of MIPs for specific applications.
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".