Gradient‐Interpenetrating Polymer Networks in 3D Printed Lattices for Tunable and Enhanced Energy Absorption
Bibliographic record
Abstract
Abstract 3D printing provides the potential to enhance mechanical properties by fabricating complex structures with diverse materials; however, most high‐resolution 3D printing techniques require custom printers to incorporate multiple materials and/or result in poor material interfacial bonding. Here, energy absorption properties are enhanced with 3D lattice structures fabricated via vat photopolymerization comprising multiple materials forming a gradient‐interpenetrating polymer network (gradient‐IPN). The gradient‐IPN is incorporated by swelling the 3D printed elastomeric lattice in a photoresin that yields a stiff shell‐soft core structure. This straightforward post‐3D printing technique delivers an unprecedented degree of structural property customization through polymer gradients in lattice struts with shells of tunable stiffness and flexible elastomeric cores to achieve a broad continuum spectrum of mechanical properties within one simple system. The gradient aids in the distribution of stress and limits fracture between materials typically observed in multimaterial lattices. The gradient‐IPN lattices are fully recoverable and exhibit over 4 to 33 times higher toughness after compression, compared to copolymer (same composition as the gradient‐IPN) or purely elastomeric lattices, respectively. This highly versatile approach to modifying 3D printed lattices yields the unique combination of load bearing capabilities with viscoelasticity desirable for high performance materials in impact protection.
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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".