Light-Converting Polymer Coatings for Spectral Engineering in Sustainable Agriculture: Materials, Fabrication Routes and Photophysical Challenges

2026

Полный текст Открытый доступ: Gold Метаданные (CSL-JSON)
DOI 10.3390/coatings16070757
Достоверность данных 70 средняя
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Аннотация

Light-converting polymer coatings and films are emerging passive photonic materials for spectral engineering in sustainable and protected agriculture. By absorbing ultraviolet or weakly used spectral components and re-emitting in visible bands that overlap with photosynthetic pigments and plant photoreceptor action regions, these materials can modify the radiation environment without additional electrical energy input. This critical review analyses light-converting polymer films and coatings from a materials and coatings perspective, with emphasis on photophysical mechanisms, polymer matrices, luminophore families, coating fabrication routes, optical transparency, photoluminescence, aggregation phenomena, photostability and scalability. The photobiological background is included as a concise framework that justifies the spectral targets of the conversion process. Rare-earth complexes, inorganic phosphors, quantum dots, aggregation-induced-emission systems and organic dyes are compared as candidate luminophores. Particular attention is paid to an author-developed perylene diimide (PDI)-modified poly(methyl methacrylate) (PMMA) solution-cast coating system, used here as a representative case study to discuss dispersion, optical homogeneity and aggregation-related losses. Extrusion, solution casting, spin-coating, dip-coating and sol–gel processing are evaluated as fabrication strategies for laboratory and large-area greenhouse applications. The work concludes by identifying the main gaps that must be addressed before practical deployment: quantitative UV–Vis and photoluminescence characterization, absolute quantum yield, haze and scattering, thickness and morphology mapping, accelerated UV ageing, weathering resistance, toxicity assessment and crop-specific validation.