Paper-Based Surface-Enhanced Raman Spectroscopy for Diagnosing Prenatal Diseases in Women

Abstract

We report the development of a surface-enhanced Raman spectroscopy sensor chip by decorating gold nanoparticles (AuNPs) on ZnO nanorod (ZnO NR) arrays vertically grown on cellulose paper (C). We show that these chips can enhance the Raman signal by 1.25 × 10⁷ with an excellent reproducibility of <6%. We show that we can measure trace amounts of human amniotic fluids of patients with subclinical intra-amniotic infection (IAI) and preterm delivery (PTD) using the chip in combination with a multivariate statistics-derived machine-learning-trained bioclassification method. We can detect the presence of prenatal diseases and identify the types of diseases from amniotic fluids with >92% clinical sensitivity and specificity. Our technology has the potential to be used for the early detection of prenatal diseases and can be adapted for point-of-care applications.