Four-Dimensional Printing of Auxetic Structures Using Nanocellulose-Reinforced PLA/PETG Blends
Bibliographic record
Abstract
This study explores the development of 4D-printed smart structures based on PLA/PETG (75/25) polymer blends reinforced with nanocellulose (0–3 wt%), processed using fused filament fabrication (FFF). Both conventional U-shaped specimens and anti-tri-chiral auxetic architectures were fabricated to evaluate the effects of nanocellulose on mechanical performance and shape memory behavior. Tensile tests demonstrated that nanocellulose reinforcement enhanced both strength and stiffness, with the highest values observed at 2 wt% (tensile strength of 56 MPa and Young’s modulus of 3.3 GPa). In standard U-shaped samples, all compositions showed excellent shape fixity and recovery (100%). For auxetic structures, shape memory behavior and deformation response varied with nanocellulose content. Notably, 2 wt% nanocellulose yielded the highest shape recovery ratio (90.8%) and fixity (99.8%), indicating improved elasticity and structural responsiveness. Meanwhile, 1 wt% nanocellulose resulted in the highest energy absorption and more controlled deformation under compression, suggesting enhanced energy dissipation and stress distribution. A slight decrease in performance at 3 wt% is attributed to nanocellulose agglomeration and reduced polymer chain mobility. These findings highlight nanocellulose as a multifunctional additive that enables fine-tuning of mechanical and functional properties in 4D-printed structures. Depending on the intended application whether focused on energy absorption, mechanical strength, or shape recovery nanocellulose content can be strategically adjusted. This approach opens pathways for designing responsive materials suited for biomedical engineering, adaptive devices, and advanced environmental technologies.
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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 | 0.000 |
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 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".