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Electrochemical separation-enhanced SERS signals of urinary metabolites for improved pancreatic cancer detectionopen access

Authors
Sohng, WoosukNoh, Hyeong SinPark, CheolminJang, Eun-biHan, In WoongChang, JinhoLee, Min-YoungChung, Hoeil
Issue Date
Feb-2026
Publisher
ELSEVIER SCIENCE SA
Keywords
Electrochemical surface-enhanced Raman scat-; tering (EC-SERS); Ag nanowires (AgNWs); Urinary metabolites; Electro-reduction of ag oxides; Pancreatic cancer
Citation
CHEMICAL ENGINEERING JOURNAL, v.529, pp 1 - 12
Pages
12
Indexed
SCIE
SCOPUS
Journal Title
CHEMICAL ENGINEERING JOURNAL
Volume
529
Start Page
1
End Page
12
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/210848
DOI
10.1016/j.cej.2026.173117
ISSN
1385-8947
1873-3212
Abstract
An electrochemical surface-enhanced Raman scattering (EC-SERS) scheme analyzing urinary metabolites to identify pancreatic cancer was demonstrated. An EC-SERS platform of densely packed Ag nanowires loaded on a screen-printed electrode (AgNW@SPE) was employed for measurement. Upon the application of -700 mV, the intensities of SERS peaks of the normal subject samples were substantially increased owing to the strong adsorption of anionic (deprotonated) forms of some urine components such as xanthine and hypoxanthine on Ag surface. The deprotonation was driven by increment of the local pH in vicinity of the surface by the electroreduction of Ag oxides. In contrast, the increase of SERS intensity was not observed when the samples of pancreatic cancer subjects were measured. Interestingly, in their cyclic voltammograms (CVs), the electroreduction of Ag oxides to Ag was not observed, indicating no local pH change on the AgNW@SPE. This could likely be due to the competitive adsorption of cancer-related metabolites, such as tyrosine. Therefore, deprotonations of the urine components would less likely occur and their attractions to Ag surface became much weaker, thereby resulting in the low SERS intensity. With these causes, the intensity difference between the SERS spectra of normal and pancreatic cancer samples was maximized at -700 mV and the discrimination accuracy became excellent, 100%, when the principal component scores of the spectra were inputted for k-nearest neighbor (k-NN). In overall, the urine analysis using proposed EC-SERS scheme was highly versatile to identify pancreatic cancer patients.
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