Additive Manufacture of Recycled Poly(Ethylene Terephthalate) Using Pyromellitic Dianhydride Targeted for FDM 3D-Printing Applications
Bibliographic record
Abstract
The suitability of recycled poly(ethylene terephthalate) (R-PET) for 3D-printing applications was evaluated by studying the melt flow characteristics of the polymer. R-PET is known to experience significant deterioration in its mechanical properties when recycled due to molecular weight loss that results from reprocessing. Lower molecular weight hinders R-PET from being 3D-printable due to low viscosity and melt strength. The hypothesis was that R-PET can be modified with reasonable effort and resources to a 3D-printable thermoplastic if the low viscosity problem is tackled. Higher viscosity will enhance both the melt strength and the melt flow characteristic of the polymer, making it more suitable for processing and 3D printing. Reactive extrusion was selected as the method for modifying the polymer to achieve the objective via a coupling reaction with chain extender PMDA (pyromellitic dianhydride). A decrease in the melt flow index (MFI) from 90 to 1.2 (g/10 min) was recorded when PMDA was added at 0.75 wt% which lowered the MFI of modified R-PET to a comparable value to commercial 3D-printing filaments. Furthermore, FT-IR analysis was performed to investigate the chemical composition of the product. Finally, a 3D-printing filament was made from the modified R-PET by mimicking the main processing stations that exist in the filament-making process, which are the extrusion stage, water bath cooling stage and spooling stage. With 0.75 wt% PMDA, the melt strength was satisfactory for pulling the filament and, therefore, a filament with on-spec dimension was produced. Finally, a small object was successfully 3D-printed using the filament product at a minimum recommended temperature of 275 °C.
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 | 0.001 |
| 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 | 0.000 |
| 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".