材料科学
堆积
纳米线
光电流
纳米技术
各向异性
基质(水族馆)
光电子学
异质结
半导体
生物传感器
热空气
密度泛函理论
对偶(语法数字)
量子点
作者
Wenchao Geng,Mengjiao Mei,Shang Chen,Jiangying Ji,Wenbo Liu,Zhiyi Yan,Jiarui Wei,Siyu Liu,Sirong Li,Chengzhou Zhu,Ruiying Yang
出处
期刊:Small
[Wiley]
日期:2026-08-01
卷期号:22 (47): e74483-e74483
摘要
ABSTRACT The random stacking and intrinsic high symmetry of substrate materials seriously affect the photoelectrochemical (PEC) detection performance. Herein, taking silver nanowires as templates, the silver‐silver sulfide (Ag‐Ag 2 S) asymmetric ordered nanowires (AONs) are prepared by the interfacial self‐assembly and interface‐confined sulfurization strategies. Based on the anisotropic characteristics of Ag‐Ag 2 S AONs on two sides, a novel PEC platform is reported for dual long non‐coding RNAs (lncRNAs) co‐detection in plasma exosomes, with machine learning‐assisted data analysis for cancer intelligent diagnosis. The photocurrent response of Ag‐Ag 2 S AONs is 16 times that of disordered Ag‐Ag 2 S, while finite‐difference time‐domain and density functional theory simulations confirm the optical anisotropy of Ag‐Ag 2 S AONs. Employing magnetic separation to differentiate signals, this developed PEC biosensor achieves selective recognition for lncRNAs MALAT1 and HOTAIR with low detection limits of 1.5 and 1.7 fM, respectively. Importantly, machine learning‐assisted Ag‐Ag 2 S AONs in distinguishing lung cancer patients from healthy individuals can reach 90% accuracy. The study combined the integration of advanced sensing materials and machine learning model, providing a new approach for intelligent monitoring of biomarkers.
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