Hydrogels at the Biology–Physics–Chemistry Interface: Design Principles Guiding Emerging Frontiers

Abstract

Abstract Hydrogels─three-dimensional networks retaining water contents while maintaining mechanical coherence─have become the preeminent soft material platform for applications spanning tissue engineering, drug delivery, flexible electronics, environmental remediation, and industrial manufacturing at scales exceeding thousands of metric tons annually. Yet, the transition from empirical discovery to rational design remains impeded by a fundamental characterization gap: macroscopic function emerges from molecular events at interfaces, but conventional techniques systematically fail to access these boundaries. This Review advances the work that trying to bridge this gap─from subnanometer hydrophobic driving forces to centimeter-scale tissue mechanics─constitutes the defining challenge for predictive hydrogel science. We critically examine the structural hierarchies distinguishing covalently cross-linked polymeric networks from supramolecular systems assembled through noncovalent patterns. The molecular building blocks enabling programmable assembly─peptide amphiphiles, nucleobase gelators, amino acid derivatives, carbohydrate organogelators, and metallosupramolecular architectures─are surveyed alongside design principles from molecular recognition to sample-spanning networks. The applications survey demonstrates universality across domains: biomedical systems; optoelectronic devices; catalytic platforms; nanocomposite architectures; and phase-selective systems for environmental remediation. We identify six interconnected challenges whose collective resolution will transform the field: achieving molecular-to-macroscopic prediction, integrating characterization across length scales, enabling measurement under native conditions, balancing mechanical robustness with responsive dynamics, establishing translational validation, and resolving interfacial phenomena at gel–cell boundaries. The vision for the coming decade centers on correlative, in-situ, multimodal platforms─augmented by machine learning on standardized datasets─these close the loop between molecular programming and macroscopic performance. Success will be measured not by new gelators discovered but by thecapacity to specify a desired function and design the molecular system that will achieve the transformation from hydrogel discovery to hydrogel design.

The authors' abstract, as published at the source. Langmuir, 2026 · DOI ↗