High-Performance Polymer Blends: Manufacturing of Polyetherimide (PEI)–Polycarbonate (PC)-Based Filaments for 3D Printing
Bibliographic record
Abstract
The demand for high-performance polymers in 3D printing continues to grow due to their ability to produce intricate and complex structures. However, commercially available high-temperature 3D printing materials often exhibit limitations such as brittleness, warping, thermal sensitivity, and high costs, highlighting the need for advanced filament development. This study investigates the fabrication of polyetherimide (PEI) and polycarbonate (PC) blends via melt extrusion to enhance material properties for stable additive manufacturing. The addition of PC improved the processability of the blends, enabling successful extrusion at temperatures ranging from 290 to 310 °C. Differential scanning calorimetry (DSC) confirmed a shift in the softening temperature (T) of PEI, indicating effective blending. To further improve the properties of the PEI:PC blends, 1 wt% of a compatibilizer was incorporated, resulting in homogeneous microstructures as observed through scanning electron microscopy (SEM). The optimized PEI:PC (70:30) blend with compatibilizer (1 wt%) demonstrated a 49% higher storage modulus than neat PEI and a 40% greater storage modulus than ULTEM9085. Moreover, reduced melt viscosity facilitated consistent and stable printing, making these materials highly suitable for applications in aerospace and transportation, where performance and reliability are critical.
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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 | 0.000 | 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".