High-performance PEEK for 4D printing by confining the PEEK powder within photocurable polymer network
Bibliographic record
Abstract
Poly(ether-ether-ketone) (PEEK) is widely used in aerospace applications as a self-lubricating material owing to its exceptional mechanical strength and thermal stability. Although fused deposition modeling (FDM) 3D printing is commonly employed for PEEK fabrication, the precise printing of complex and intricate structures remains challenging. In this study, we developed a novel 3D printing strategy for the precise molding of high-performance PEEK using digital light processing (DLP) to print a photocurable resin, in which ACMO-PEGDA (PACMO) was blended with commercial PEEK powder. In the photocured structure, PEEK powder was confined within the elastic crosslinking network. Each 3D printed PEEK sample underwent individual phase transitions in response to temperature changes. Experimental results confirmed an exceptional shape memory performance, with shape fixation and recovery ratios exceeding 94%. The 4D printed PEEK components exhibited high strength and facilitated the transition from 2D to 3D complex structures. Furthermore, the fabricated components demonstrated an exceptionally low coefficient of friction of below 0.1, offering a groundbreaking solution for customized production of advanced wear-resistant mechanical components. This study represents a significant advancement in PEEK processing technology, establishes new paradigms for high-performance PEEK 3D/4D printing, and opens new avenues for the development of next-generation intelligent devices with enhanced performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".