ArticleTalanta2024
Improved solution-based SERS detection of creatinine by inducing hydrogen-bonding interaction for effective analyte capture.
Article in Talanta, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
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Authors and funding
2 authors.
Funding
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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