3D Polymeric Nanonetworks: From Self-Assembly to Advanced Fabrication [Dataset]

Three-dimensional polymeric nanonetworks combine high surface area, interconnected porosity, and tunable mechanics to enable advanced functions in catalysis, sensing, energy storage, and biomedicine. While existing reviews focus on individual fabrication techniques, this work provides the first systematic cross-method comparison and practical decision framework for method selection. We evaluate five fundamentally different fabrication strategies—block copolymer self-assembly, hyper-crosslinking, template-assisted methods, 3D printing, and nanolithography—across four critical metrics: resolution, throughput, scalability, and material compatibility. Unlike method-specific reviews, this work presents a quantitative decision matrix that operationalizes these metrics for application-driven method selection, bridging the gap between laboratory capabilities and industrial requirements. We showcase hybrid approaches that integrate multiple techniques (e.g., polymerization-induced phase separation with vat photopolymerization) to achieve hierarchical structures combining nanoscale precision with manufacturability. The dataset refers to this review which includes a concise primer on block copolymer self-assembly fundamentals (χN, segregation strength; ƒ, volume fraction), updated polymer topology terminology, and quantified performance envelopes for photopolymerization and two-photon techniques. Critically, we address real-world translation challenges—scalability bottlenecks, defect control, device integration, and material limitations—that are often overlooked in technique-focused reviews. Finally, we discuss future trends in eco-friendly, scalable fabrication and AI-driven design tools to accelerate the translation of 3D polymeric nanonetworks into practical applications. By providing integrated cross-method guidance rather than isolated technique descriptions, this review enables researchers to navigate the complex fabrication landscape and select optimal strategies for their specific performance targets.