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Antioxidants (Basel, Switzerland) · 2026

Nanoantioxidants: Structure-Activity Relationships, Redox Mechanisms, Applications, and Safety.

СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Kim Min Ook

Аннотация

Oxidative stress contributes to disease progression, food deterioration, agricultural losses, and material degradation, whereas many conventional antioxidants are limited by poor stability, low bioavailability, rapid consumption, or inadequate delivery. In this review, nanoantioxidants are defined as nanoscale systems intentionally designed to suppress or regulate damaging oxidative processes, and four functional categories are distinguished: intrinsically antioxidant nanomaterials, antioxidant nanozymes, antioxidant-functionalized nanoparticles, and nanocarriers used to deliver conventional antioxidants. The review systematically examines how particle size and surface area, morphology and exposed facets, oxidation state and defect density, surface chemistry and charge, aggregation, dissolution or ion release, dose, and the surrounding environment determine antioxidant activity. Particular emphasis is placed on the context-dependent transition from antioxidant to pro-oxidant behavior, the distinction between enzyme-like nanozyme activity and true enzymatic activity, and the need for nanoparticle-specific controls and complementary assays to avoid analytical interference. Evidence from chemical assays, cell culture, animal studies, and clinical research is considered separately because activity in a simplified chemical system does not by itself establish therapeutic efficacy or safety. Applications in nanomedicine, food preservation, agriculture, environmental protection, and engineering materials are critically compared. Long-term toxicity, biodistribution, clearance, degradation, environmental fate, and life-cycle impacts are highlighted as essential translational criteria; likewise, green synthesis is not treated as synonymous with sustainability without consideration of energy, solvents, purification, scalability, waste, and end-of-life behavior. Future progress will require standardized datasets and experimental protocols, mechanism-guided design, realistic validation, and carefully governed artificial-intelligence-assisted optimization.