Atta S, Vo-Dinh T. Improved solution-based SERS detection of creatinine by inducing hydrogen-bonding interaction for effective analyte capture.
Talanta 2024;
278:126373. [PMID:
38901075 DOI:
10.1016/j.talanta.2024.126373]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/20/2024] [Accepted: 06/05/2024] [Indexed: 06/22/2024]
Abstract
Recently, solution-based surface-enhanced Raman scattering (SERS) detection technique has been widely recognized due to its cost-effectiveness, simplicity, and ease of use. However, solution-based SERS is limited for practical applications mainly because of the weak adsorption affinity of the target biomolecules to the surface of plasmonic nanoparticles. Herein, we developed a highly sensitive solution-based SERS sensing platform based on mercaptopropionic acid (MPA)-capped silver-coated gold nanostars (SGNS@MPA), which allows efficient enrichment on the nanostars surface for improved detection of an analyte: creatinine, a potential biomarker of chronic kidney disease (CKD). The SGNS@MPA exhibited high enrichment ability towards creatinine molecules in alkaline medium (pH-9) through multiple hydrogen bonding interaction, which causes aggregation of the nanoparticles and enhances the SERS signal of creatinine. The detection limit for creatinine was achieved at 0.1 nM, with a limit of detection (LOD) value of 14.6 pM. As a proof-of-concept demonstration, we conducted the first quantitative detection of creatinine in noninvasive human fluids, such as saliva and sweat, under separation-free conditions. We achieved a detection limit of up to 1 nM for both saliva and sweat, with LOD values as low as 0.136 nM for saliva and 0.266 nM for sweat. Overall, our molecular enrichment strategy offers a new way to improve the solution-based SERS detection technique for real-world practical applications in point-of-care settings and low-resource settings.
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