Juang, Ruey ShinRuey ShinJuangWang, Kuan SyunKuan SyunWangKuan, Tsai YuTsai YuKuanChu, Yu JuYu JuChuRU-JONG JENGHardiansyah, AndriAndriHardiansyahLiu, Shou HsuanShou HsuanLiuLiu, Ting YuTing YuLiu2023-10-252023-10-252023-01-0118761070https://scholars.lib.ntu.edu.tw/handle/123456789/636514Background: The detection of uremic toxins is important for early-stage diagnosis of chronic kidney disease. However, Raman spectroscopy is shortcoming of the sensitivity for detecting the trace analytes. The electric field-induced chemical surface-enhanced Raman scattering (SERS) enhancement (EC-SERS) detection is great way to solve and investigates uremic toxins. Methods: In this work, various thickness (5–25 nm) of Au nano-islands were deposited on the 3D laser-scribed graphene (LSG) substrate for increasing the sensitivity and reproducibility of SERS detection. Particularly, applying the electric-field stimulus in Au-LSG SERS substrate allows to further amplify the SERS signals, and measure the EC-SERS signals of biomolecules. Significant findings: The results show that 20 nm of Au nano-islands coated on LSG substrate obtains the highest SERS enhancement effects, and successfully detects the dye molecules (rhodamine 6G, R6G) and uremic toxins (urea and creatinine). The EC-SERS signals of R6G would enhance 17 times at the potential of −1.3 V, compared to SERS signals without applying an electric field. Moreover, the urea also displays 4 times higher at the potential of −0.2 V. The detecting molecules could be selected to enhance SERS signals by different voltages, showing the capability of selectively detecting biomolecules, which can solve the problem of complex sample pretreatment.Electric field-induced chemical surface-enhanced Raman scattering enhancement (EC-SERS) detection | Gold nano-islands | Laser-scribed grapheme | Uremic toxins[SDGs]SDG3Electric field-stimulated Raman scattering enhancing biochips fabricated by Au nano-islands deposited on laser-scribed 3D graphene for uremic toxins detectionjournal article10.1016/j.jtice.2023.1051152-s2.0-85171678494https://api.elsevier.com/content/abstract/scopus_id/85171678494