Recycled Carbonyl Iron-Biodegradable PLA Auxetic Composites: Investigating Mechanical Properties of 3D-Printed Parts Under Quasi-Static Loading
Bibliographic record
Abstract
Auxetic structures showcase notable properties such as high indentation resistance, shear stiffness, fracture toughness, and acoustic energy absorption. Recent advancements in additive manufacturing have facilitated the creation of complex auxetic designs, but there has been less emphasis on developing new materials. This study focuses on using recycled iron powders mixed with biodegradable polymers by using the solution casting method to create sustainable, 3D-printable materials for energy absorption applications. This research involved examining a 2D re-entrant structure, evaluating the effects of varying iron powder concentrations in the polymer. The analyses included thermogravimetric analyses, differential scanning calorimetry, and microstructural examination, alongside compression tests to assess strength and absorption capabilities. The most effective 3D-printed composite, containing 10% iron powders, demonstrated a substantial improvement in specific energy absorption (SEA of 2.051 kJ/kg compared to neat PLA with an SEA of 0.160 kJ/kg) and exhibited favorable mechanical and thermal properties. The TGA showed that adding iron powder reduced PLA’s onset degradation temperature from 340 °C to 310 °C, 295 °C, and 270 °C for 5%, 10%, and 15% iron, respectively, confirming iron’s catalytic effect on PLA degradation. The DSC analysis showed that adding iron powder increased the degree of crystallinity from 5.63% for pure PLA to 5.77%, 6.79%, and 6.91% for 5%, 10%, and 15% iron, respectively, indicating iron’s role as a nucleation agent. These results highlight the potential of novel iron/PLA 2D re-entrant composites for energy-absorbent applications, emphasizing sustainability and cost-effectiveness.
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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".