FABRICATION OF ERI SILK–SERICIN BASED BIOSENSORS FOR DETECTION OF ENVIRONMENTAL TOXINS AND FOOD CONTAMINANTS

Background: The increasing prevalence of environmental toxins and food contaminants necessitates the development of sensitive, cost-effective, and sustainable sensing platforms. Silk sericin, a natural protein derived from silkworm cocoons, has emerged as a promising biomaterial for biosensor fabrication due to its abundant functional groups, biocompatibility, and aqueous processability. Methods: This review synthesizes current literature on Eri silk (Samia ricini) sericin-based biosensors, analyzing fabrication strategies, characterization methodologies, and detection performance for environmental and food contaminants. Data extraction focused on physicochemical properties, fabrication parameters, analytical performance metrics (detection limits, linear ranges, sensitivity), and validation results. Results: Eri silk sericin exhibits distinct molecular characteristics, including molecular weight of approximately 66 kDa and unique globular surface morphology with porous architecture. Fabrication techniques including film casting, microparticle formation, and electrospinning enable tunable sensor architectures. Crosslinking with polyethylene glycol (PEG) and glycerol enhances mechanical stability; PEG-crosslinked films demonstrate superior uniformity with contact angles of 75.4°. Spectroscopic characterization via FT-IR reveals characteristic amide I, II, and III peaks at 1650 cm⁻¹, 1546 cm⁻¹, and 1240 cm⁻¹, confirming protein secondary structures essential for functionality. Conclusions: Sericin-based biosensors achieve detection limits in the nanomolar range for pesticides (0.1-1.0 nM), heavy metals (0.5-10 nM), and mycotoxins (0.1-1.0 ng/mL). Emerging trends include smartphone-integrated platforms, wearable formats, and machine learning-assisted optimization. Key challenges include sericin solubility in aqueous media and batch-to-batch variability, requiring standardized extraction protocols and crosslinking optimization.

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