Machine learning-guided tuning of shape memory behavior in 4D-printed single-layer elastomer strip
Pankaj Kumar, Amritesh Kumar, Santosha Kumar Dwivedy, Subham Banerjee · Emerald · 2026
Machine learning-guided optimization of 4D-printed PU/Eudragit S100/MTZ elastomer strips enables strong mechanical performance and high shape recovery for pH-responsive, site-specific drug delivery.
Plain English summary
Why this matters
Key findings
- ML models were used to predict UTS, shape-memory programming, and activation stages from printing parameters.
- Optimized 4D-printed composite achieved a shape-recovery ratio of 89.43%.
- Optimized material achieved an ultimate tensile strength (UTS) of 11.15 MPa.
- A 3D-printed capsule fabricated under optimized conditions showed excellent mechanical stability and shape-memory behavior.
- Eudragit® S100 pH-responsiveness was used to motivate pharmaceutical/site-specific drug delivery potential.
Limitations
The abstract does not specify long-term durability, in vivo performance, biocompatibility/toxicity results, drug release kinetics, or comparative benchmarks against non-ML or alternative formulations.
Publication
- Publisher
- Emerald
- Publication date
- February 10, 2026
- Research type
- Paper
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