自愈水凝胶
纳米技术
生化工程
药物输送
材料科学
组织工程
生物材料
生物技术
计算机科学
工程类
生物
生物医学工程
高分子化学
作者
Valeria G. Oyervides‐Guajardo,Jesús A. Claudio‐Rizo,Denis A. Cabrera‐Munguía,Lucía F. Cano‐Salazar,María I. León‐Campos,María Concepción Tamayo‐Ordoñez
摘要
ABSTRACT Over the past decades, biopolymers—particularly proteins and polysaccharides—have emerged as foundational components in the development of advanced hydrogel systems for biotechnological applications. Their inherent biocompatibility, biodegradability, and tunable physicochemical properties have enabled the creation of next‐generation materials that dynamically interact with biological systems. Recent research has moved beyond conventional uses, focusing on hybrid protein–polysaccharide hydrogels that integrate the bioactivity of proteins with the structural versatility of polysaccharides, allowing for the modulation of cell behavior, targeted therapeutic delivery, and environmentally responsive functions. Innovative crosslinking strategies, including enzymatic, click chemistry, and stimuli‐responsive approaches, have been developed to fine‐tune the mechanical properties, degradation rates, and biofunctionality of these materials. In particular, advances from 2001 to 2025 demonstrate how the rational design has progressed in areas such as tissue‐specific scaffolding, plant–biomaterial interfaces, smart drug delivery, and pollutant capture. This review critically examines recent progress in the synthesis and characterization of protein–polysaccharide hydrogels. Special emphasis is placed on emerging technologies and interdisciplinary applications. Key areas of focus include animal and plant tissue engineering, site‐specific drug release platforms, and eco‐friendly strategies for bioremediation. Current limitations—such as batch‐to‐batch variability, scalability, and long‐term stability—are also discussed, along with future directions aimed at addressing these challenges to unlock the full potential of these bioinspired materials in modern biotechnology.
科研通智能强力驱动
Strongly Powered by AbleSci AI