A label-free cellulose SERS biosensor chip with improvement of nanoparticle-enhanced LSPR effects for early diagnosis of subarachnoid hemorrhage-induced complications
Wansun Kim, Sung Ho Lee, Yong Jin Ahn, Seung Ho Lee, Jiwook Ryu, Seok Keun Choi, Samjin Choi
Index: 10.1016/j.bios.2018.04.003
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Abstract
It is very difficult to predict some complications after subarachnoid hemorrhage (SAH), despite rapid advances in medical science. Herein, we introduce a label-free cellulose surface-enhanced Raman spectroscopy (SERS) biosensor chip with pH-functionalized, gold nanoparticle (AuNP)-enhanced localized surface plasmon resonance (LSPR) effects for identification of SAH-induced cerebral vasospasm and hydrocephalus caused by cerebrospinal fluid (CSF). The SERS biosensor chip was implemented by the synthesis reaction of the AuNPs, which were charged positively through pH level adjustment, onto a negatively-charged cellulose substrate with ξ=–30.7 mV. The zeta potential, nanostructural properties, nanocrystallinity, and computational calculation-based electric field distributions of the cellulose-originated AuNPs were optimized to maximize LSPR phenomena and then characterized. Additionally, the performance of the SERS biosensor was compared under two representative excitation laser sources in the visible region (532 nm) and near-infrared region (785 nm). The Raman activities of our SERS biosensor chip were evaluated by trace small molecules (crystal violet, 2 µL), and the biosensor achieved an enhancement factor of 3.29×109 for the analytic concept with an excellent reproducibility of 8.5% relative standard deviation and a detection limit of 0.74 pM. Furthermore, the experimental results revealed that the five proposed SERS-based biomarkers could provide important information for identifying and predicting SAH-induced cerebral vasospasm and hydrocephalus complications (91.1% reliability and 19.3% reproducibility). Therefore, this facile and effective principle of our SERS biosensor chip may inspire the basis and strategies for the development of sensing platforms to predict critical complications in various neurosurgical diagnoses.