A Surface-Enhanced Raman Scattering-Digital Microfluidics Biosensing Platform Integrated with a 1D-Convolutional...

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Nanotechnology

Background:

• Early sepsis diagnosis is critical but challenging using noninvasive inflammatory markers (IMs).
• Current diagnostic methods often lack sensitivity, speed, or require invasive procedures.

Purpose of the Study:

• To develop an integrated sensing platform combining surface-enhanced Raman scattering (SERS) and digital microfluidics (DMF) for sensitive, automated detection of C-reactive protein (CRP) in sweat.
• To establish a robust analytical model for accurate CRP quantification in noninvasive samples.

Main Methods:

• Development of a SERS-DMF platform utilizing functionalized Au@Fe3O4 magnetic nanoparticles and SERS tags for sandwich immunocomplex formation.
• Automated droplet manipulation on a DMF chip for sample enrichment and in situ detection.
• Application of a one-dimensional convolutional neural network (1D-CNN) for quantitative CRP analysis.

Main Results:

• The SERS-DMF platform achieved rapid CRP detection (within 20 min) with a low limit of detection (0.77 pg/mL), significantly below clinical thresholds.
• The platform demonstrated high reliability, consistent with ELISA, and excellent quantitative accuracy (R²=0.994, RMSE=0.135) with the 1D-CNN model.
• The integrated system showed superior performance compared to traditional analytical methods for trace analyte detection.

Conclusions:

• The developed SERS-DMF platform offers a noninvasive, rapid, and automated solution for early sepsis screening by detecting inflammatory markers like CRP in sweat.
• This technology provides an innovative foundation for developing advanced diagnostic devices with ultralow sample consumption and high efficiency.