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Biosensors (Basel)
2023 Dec 22;141:. doi: 10.3390/bios14010008.
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Asterias forbesi-Inspired SERS Substrates for Wide-Range Detection of Uric Acid.
Park H
,
Chai K
,
Kim W
,
Park J
,
Lee W
,
Park J
.
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Uric acid (UA), the final metabolite of purine, is primarily excreted through urine to maintain an appropriate concentration in the bloodstream. However, any malfunction in this process can lead to complications due to either deficiency or excess amount of UA. Hence, the development of a sensor platform with a wide-range detection is crucial. To realize this, we fabricated a surface-enhanced Raman spectroscopy (SERS) substrate inspired by a type of starfish with numerous protrusions, Asterias forbesi. The Asterias forbesi-inspired SERS (AF-SERS) substrate utilized an Au@Ag nanostructure and gold nanoparticles to mimic the leg and protrusion morphology of the starfish. This substrate exhibited excellent Raman performance due to numerous hotspots, demonstrating outstanding stability, reproducibility, and repeatability. In laboratory settings, we successfully detected UA down to a concentration of 1.16 nM (limit of detection) and demonstrated selectivity against various metabolites. In the experiments designed for real-world application, the AF-SERS substrate detected a broad range of UA concentrations, covering deficiencies and excesses, in both serum and urine samples. These results underscore the potential of the developed AF-SERS substrate as a practical detection platform for UA in real-world applications.
NRF-2023R1A2C2004964 National Research Foundation of Korea, NRF-2022R1I1A1A01066196 National Research Foundation of Korea, NRF-2022R1A2C4001990 National Research Foundation of Korea, RS-2023-00222737 National Research Foundation of Korea
Scheme 1. Schematic of Raman spectroscopic detection of UA derived from serum and urine using an Asterias forbesi-inspired SERS substrate.
Figure 1. SEM images of (a) SLNS-Ag, (b) SLNS-Au@Ag, and (c) AF-SERS substrate (scale bar: 100 nm). (d) Image of the legs and protrusion structures of the Asterias forbesi. Raman spectrum comparison data for 100 μM R6G, a Raman emitter: (e) Bare Au plate and SLNS-Ag substrate (Gray scale: SLNS-Ag, Black dotted line: Au plate), (f) SLNS-Ag and SLNS-Au@Ag substrate (Gray scale: SLNS-Au@Ag, Black dotted line: SLNS-Ag), and (g) SLNS-Au@Ag and AF-SERS substrate (Blue scale: AF-SERS, Black dotted line: SLNS-Au@Ag), respectively. (h) Raman intensities at 1508 cm−1, a specific peak of R6G on various SERS substrates (Au plate, SLNS-Ag, SLNS-Au@Ag, and AF-SERS, respectively). FEM-based electromagnetic simulation results and RGB value graph for each area of (i) SLNS-Ag, (j) SLNS-Au@Ag, and (k) AF-SERS substrate. (l) Ratio of the red region (in (i–k)) for each substrate.
Figure 2. Optimization and SERS performance evaluation of AF-SERS with R6G. (a) SERS spectra of R6G depending on GNP concentration. (b) Measured values of the SERS peak intensities at 1508 cm−1 depending on the GNP concentration. Intensity at specific Raman peaks of R6G in (c) AF-SERS and (d) GNP + SLNS-Ag substrate measured under extreme conditions (10× PBS solution) for 7 days. (e) SERS sensitivity of R6G detection at various concentrations (10–14–10–4 M). (f) Measured values of SERS peak intensities at 1508 cm−1 depending on the R6G concentrations. Inset depicts the magnified SERS peak intensities of R6G at a low concentration (0–10–10 M).
Figure 3. Efficiency and sensitivity analysis of the AF-SERS-based UA sensor. (a) R6G Raman spectra of six different AF-SERS substrates and (b) Heat map image of R6G Raman spectra at 50 random spots. (c) Raman spectra for UA detection at various concentrations on AF-SERS SERS substrate. Inset image is the molecular structure of UA. (d) Raman intensity at 1508 cm−1, which is the specific peak of R6G for each substrate. (e) Raman intensity measurements at 1508 cm−1 for 50 random data points extracted from heat map data. (f) Measured value of the SERS peak intensities at 640 cm−1 depending on the UA concentration. Inset image shows the results in the low concentration range, presented at an optimal scale.
Figure 4. Selectivity analysis of the AF-SERS-based UA sensor. (a) Structural formula of selective groups: uric acid (I), ascorbic acid (II), creatine (III), dopamine (IV), glucose (V), and L-cysteine (VI). (b) SERS spectra and (c) SERS peak intensities at 640 cm−1 of UA and the selective groups.
Figure 5. UA detection using the AF-SERS substrate in human serum and artificial urine. (a) Schematic of UA sampling in 10% diluted human serum. (b) SERS spectra and (c) SERS peak intensities at 640 cm−1 of 10% diluted human serum in UA in the concentration range of 1 μM–1 mM. (d) Schematic of UA sampling in urine. (e) SERS spectra and (f) SERS peak intensities at 640 cm−1 of various UA concentration ranges (deficiency, normal, excess) in urine. Each detection stage can be distinctly differentiated statistically (**** p-value < 0.0001).
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