Development of a Surface-Enhanced Raman Spectroscopy (SERS) Technique for Blood Serum Using a Cost-Effective Silver Nanoparticle-Based Substrate
Keywords:
Surface-enhanced Raman spectroscopy (SERS), SERS substrate, Serum-based detectionAbstract
Surface-enhanced Raman spectroscopy (SERS) substrates for the measurement of biological samples are currently in high demand, but their cost is relatively high due to their complex manufacturing processes. This study designed and developed a SERS substrate for measuring human serum samples that can be readily assembled from four common, inexpensive industrial materials: filter paper, Blu-ray disc, graphene ink, and aluminum tape, with 60 nm silver nanoparticles for SERS signal enhancement. The measurements were conducted using a laser wavelength of 785 nm and an acquisition time of 10 seconds. Aluminum tape was found to be the most suitable material for use as a SERS substrate because it caused the greatest amplification of the serum's Raman signal with minimal variation, the detected SERS signal was consistent with prior research and was unaffected by the substrate's intrinsic Raman signal. The optimal concentration of the colloidal silver nanoparticles was found to be 1.85 x 10-2 molar. The biomolecular composition of serum was detected at 494 cm-1 (L-arginine), 634 cm-1 (tyrosine), 724 cm-1 (adenine), 884 cm-1 (tryptophan), 1,004 cm-1 (phenylalanine), and 1,334 cm-1 (nucleic acid). This SERS substrate produced enhancement factors of up to 4,096 (at the 1,618 cm-1 peak) and 164 (at the 634 cm-1 peak) for the measurement of crystal violet and serum samples, respectively. Thus, this study successfully developed a low-cost but effective SERS substrate that showed good performance for serum sample measurement and holds potential for application as a SERS substrate in disease diagnosis, especially in low-resource settings. Additionally, its potential for application in medicine relates to disease screening using serum sample analysis and integration with machine learning techniques to classify and diagnose diseases based on biomolecules.
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