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Limit of Detection of Raman Spectroscopy Using Polystyrene Particles from 25 to 1000 nm in Aqueous Suspensions
Cindy Mayorga1,2, Shreya Milind Athalye1,2, Miad Boodaghidizaji3
1Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, Indiana 47907, United States.
Raman spectroscopy can detect tiny particles like viruses. This study determined the detection limits for polystyrene particles, showing its potential for real-time monitoring in vaccine manufacturing.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Nanotechnology
Background:
- Raman spectroscopy is a powerful technique for identifying microorganisms and small particles.
- Polystyrene particles of 25-1000 nm size are comparable to viral particles and aggregates.
- Accurate detection limits are crucial for applying Raman spectroscopy in biological and manufacturing contexts.
Purpose of the Study:
- To determine the limit of detection (LOD) for polystyrene particles of varying sizes using confocal Raman microscopy.
- To characterize the Raman spectral signatures of polystyrene particles in aqueous suspensions.
- To assess the non-destructive nature of Raman spectroscopy for particle analysis.
Main Methods:
- Confocal Raman microscopy with a 785 nm laser and 5x objective lens.
- Kernel partial least-squares modeling to establish minimum and maximum LOD (LODmin and LODmax).
- Dynamic light scattering (DLS) to confirm non-destructive analysis.
Main Results:
- The prominent Raman peak at 1001 cm⁻¹ (ring breathing mode) was identified for polystyrene particles.
- LOD for 50 nm particles ranged from 1.80 × 10¹² to 8.31 × 10¹² particles/mL.
- LOD for 1000 nm particles ranged from 5.11 × 10⁸ to 2.53 × 10⁹ particles/mL.
- Raman spectroscopy was confirmed as non-destructive using DLS.
Conclusions:
- Polystyrene particle characterization provides foundational data for Raman spectroscopy applications.
- Raman spectroscopy demonstrates significant potential for detecting small particles in aqueous suspensions.
- This technique could be valuable for real-time monitoring in vaccine manufacturing.
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