Quantitative detection of acyclovir by surface enhanced Raman spectroscopy using a portable Raman spectrometer...

Area of Science:

• Analytical Chemistry
• Nanotechnology
• Computational Chemistry

Background:

• Acyclovir (ACV) is a vital antiviral medication.
• ACV's significant nephrotoxicity necessitates precise monitoring.
• Current detection methods may lack the required sensitivity and cost-effectiveness.

Purpose of the Study:

• To develop an ultrasensitive detection method for Acyclovir (ACV).
• To explore the application of surface-enhanced Raman scattering (SERS) for ACV quantification.
• To investigate the interaction mechanism between ACV and metallic surfaces.

Main Methods:

• Preparation and comparison of various nanoparticle substrates for SERS.
• Utilizing citrate-reduced silver nanoparticles as optimal SERS substrates.
• Employing Density Functional Theory (DFT) for mechanistic analysis.
• Developing quantitative models using Partial Least-Squares (PLS) regression and Artificial Neural Network (ANN).

Main Results:

• Citrate-reduced silver nanoparticles demonstrated superior enhancement for ACV detection.
• DFT simulations aided in understanding ACV-metal surface interactions.
• PLS regression models outperformed ANN models for quantitative prediction.
• Achieved sensitive detection of ACV across concentrations from 10⁻¹ to 10⁻⁷ M.

Conclusions:

• SERS combined with multivariate analysis offers a sensitive and cost-effective approach for ACV detection.
• This technique has significant potential for clinical diagnostics of ACV and its metabolites in biological samples.
• The study provides a foundation for advanced SERS-based therapeutic drug monitoring.