The combination of artificial intelligence (AI) and biomimetic design enables the development of materials that can adapt and dynamically respond to biological cues. This study has developed a thermal-responsive micro-nail (MN) platform inspired by Drosera capensis, integrating motion, surface, and functional imitation through AI-guided 4D printing. Shape memory polymers (SMPs) composed of tert-butyl acrylate (tBA) and 1,6-hexanediol diacrylate (HDDA) are designed to exhibit reversible shape recovery upon thermal stimulation. The complex shape memory behavior is quantitatively modeled using various machine learning (ML) algorithms, including support vector regression (SVR), extreme gradient boosting (XGB), and Gaussian process regression (GPR). Among them, GPR demonstrates excellent prediction accuracy (R2 > 0.99), providing predicted means and 95% confidence intervals, highlighting its reliability in modeling nonlinear thermal recovery behavior and its potential to guide process parameter optimization. The generated MNs exhibit writhing and grasping actions similar to those of thrips, achieving self-driven wound closure. Additionally, the MNs are functionalized through plasma immersion implantation (S-PIII) using adsorbed DNA (aDNA) and Zn nanolayers. The Zn nanolayers promote the sustained release of DNA and confer the MNs with inherent antibacterial activity. In a diabetic wound model, the AI-optimized biomimetic MN (BMMN) system significantly enhances epithelial regeneration, collagen remodeling, and neovascularization, demonstrating adaptive and intelligent wound healing materials. This research was published under the title "AI-Guided 4D Printing of Carnivorous Plant-Inspired Microneedles for Accelerated Wound Healing" in Advanced Materials.
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DOI: 10.1002/adma.202523665