Selectivity/Specificity Improvement Strategies in Surface-Enhanced Raman Spectroscopy Analysis
<p>Scheme for SERS-based hydrazine detection derivatized with a phthaldialdehyde probe [<a href="#B6-sensors-17-02689" class="html-bibr">6</a>].</p> "> Figure 2
<p>Derivatization reaction of HCHO and 4-amino-5-hydrazino-3-mercapto-1,2,4-triazole (AHMT) under alkaline conditions (<b>A</b>) [<a href="#B18-sensors-17-02689" class="html-bibr">18</a>]. Synthesis of p-([8-hydroxyquinoline]azo)benzenethiol (SHQ) (<b>B</b>) [<a href="#B19-sensors-17-02689" class="html-bibr">19</a>].</p> "> Figure 3
<p>(<b>A</b>) Chemical derivatization reaction between MBTH hydrochloride and formaldehyde azine [<a href="#B20-sensors-17-02689" class="html-bibr">20</a>]; (<b>B</b>) Chemical derivatization reaction between acetone and 2,4-dinitrophenylhydrazine to form 2,4-dinitrophenyl hydrazine [<a href="#B17-sensors-17-02689" class="html-bibr">17</a>]; (<b>C</b>) Chemical derivatization reaction between thiobarbituric acid and malondialdehyde [<a href="#B21-sensors-17-02689" class="html-bibr">21</a>]; (<b>D</b>) Correlation between the SERS intensity at 1265 cm<sup>−1</sup> of HPLC-purified TBA–MDA adduct and its concentration in picomolar, both in logarithm scales. Inset: representative SERS spectra of TBA–MDA with concentration of (a) 4.5 μM; (b) 0.45 μM; (c) 45 nM; (d) 4.5 nM; and (e) 0.45 nM [<a href="#B21-sensors-17-02689" class="html-bibr">21</a>].</p> "> Figure 4
<p>SERS-based immunoassay platform [<a href="#B35-sensors-17-02689" class="html-bibr">35</a>]. Antibodies were fixed on a platform to capture antigen, Ag or Au NPs conjugated with both Raman tags and detection antibodies can then be captured for SERS detection.</p> "> Figure 5
<p>The formation of thymine (T)-Hg<sup>2+</sup>-T caused the aggregation of AgNPs [<a href="#B65-sensors-17-02689" class="html-bibr">65</a>].</p> "> Figure 6
<p>(<b>A</b>) A polyadenine mediated core-satellite nanoassembly immobilized on the silicon wafer [<a href="#B66-sensors-17-02689" class="html-bibr">66</a>]. (<b>B</b>–<b>D</b>) are SEM images of the interparticle gaps of satellite Au NPs of Au-Au NPs mediated by Poly A10, Poly A30, and Poly A50.</p> "> Figure 7
<p>A recycling SERS-based aptasensor chip using the exonuclease to amplify the Raman signal [<a href="#B73-sensors-17-02689" class="html-bibr">73</a>].</p> "> Figure 8
<p>(<b>A</b>) Mucin-1 detection based on SERS with bimetallic core (gold nanorods)—satellite (silver nanoparticles) assemblies [<a href="#B77-sensors-17-02689" class="html-bibr">77</a>]. (<b>B</b>) SERS spectra of Mucin-1 detection; (<b>C</b>) Standard curve for Mucin-1 detection with corresponding peak intensities at 1142 cm<sup>−1</sup>.</p> "> Figure 9
<p>Schematic of the one-pot controlled synthesis of AuNPs@MIPs for improved SERS sensing (<b>a</b>) [<a href="#B89-sensors-17-02689" class="html-bibr">89</a>]. After the template removal, target molecules can selectively rebind with the cavities, and the analytes can closely contact with the Au NPs in the imprinted layer (<b>b</b>), which significantly improves the selectivity and sensitivity.</p> "> Figure 10
<p>The TEM images of SiO<sub>2</sub>/Ag/MIPs with different shell thickness: 40 nm (<b>a</b>), 170 nm (<b>b</b>) and (<b>c</b>) the SERS detections of different thicknesses of SiO2/Ag/MIPs to the same concentration of R6G at 10<sup>−6</sup> mol L<sup>−1</sup> (40 nm (<b>a</b>), 100 nm (<b>b</b>), 170 nm (<b>c</b>)) [<a href="#B106-sensors-17-02689" class="html-bibr">106</a>].</p> "> Figure 11
<p>Schematic representation of the boronate-affinity sandwich assay of glycoproteins [<a href="#B122-sensors-17-02689" class="html-bibr">122</a>]. Target glycoprotein can be captured specifically by a boronate-affinity MIP array. The captured glycoprotein is then labeled with boronate functionalized AgNPs for SERS detection.</p> "> Figure 12
<p>A typical microdroplet base SERS sensing platform: ports 1–5 are used for the injection of an aqueous solution containing the sample, the SERS active substrates, and their aggregation agent [<a href="#B126-sensors-17-02689" class="html-bibr">126</a>]; (<b>b</b>) Schematic illustration of the SERS-based microdroplet sensor for wash-free magnetic immunoassay. The sensor is composed of five compartments with the following functions: (i) droplet generation and reagent mixing; (ii) formation of magnetic immunocomplexes; (iii) magnetic barmediated isolation of immunocomplexes; (iv) generation of larger droplets containing the supernatant for SERS detection and (v) generation of smaller droplets containing magnetic immunocomplexes [<a href="#B129-sensors-17-02689" class="html-bibr">129</a>].</p> "> Figure 13
<p>Optofluidic SERS-CD platform and preconcentration mechanism. (<b>a</b>) Schematic illustration of the optofluidic SERS-CD platform with high-throughput sample preparation; (<b>b</b>) The mechanism of preconcentration consisting of 4 steps. I: Fabrication of SERS-active sites; II: Injection of a sample solution; III: Adsorption of target molecules on the SERS-active site during drying process prior to SERS measurement; IV: Accumulation of the adsorbed molecules by the repetition of the steps II and III. (<b>c</b>) The enhanced SERS intensity due to the preconcentration of the target molecule with increasing ‘filling–drying’ cycles [<a href="#B131-sensors-17-02689" class="html-bibr">131</a>].</p> "> Figure 14
<p>(<b>a</b>) The photograph of the switch-on-chip and (<b>b</b>) the enlarged view of the inlet and the pretreatment zone [<a href="#B139-sensors-17-02689" class="html-bibr">139</a>].</p> "> Scheme 1
<p>(<b>A</b>) Schematic diagram of Raman detection; (<b>B</b>) Schematic diagram of chemical reaction assisted SERS detection [<a href="#B17-sensors-17-02689" class="html-bibr">17</a>].</p> ">
Abstract
:1. Introduction
2. Reaction-SERS Method
2.1. Improving Analyte Affinity with SERS Substrate
2.2. Increasing the Raman Scattering Cross-Sectional Area
2.3. Reducing the Raman Scattering Cross-Sectional Area
2.4. Application of Reaction-SERS Method
3. Antibody-SERS
4. Aptamer-SERS
4.1. Metal Ion
4.2. Small Molecule
4.3. Biomacromolecules
4.4. Living Organisms
5. MIPs-SERS
6. Microfluidics-SERS
7. Final Statements
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Analytes | Reagents | Substrates | Matrix | LOD 1 | Linear Range 2 | Ref. |
---|---|---|---|---|---|---|
hydrazine | ortho-phthaldialdehyde | AgNPs | water | 8.5 × 10−11 M | 10−5–10−10 M | [6] |
ethylene and SO2 | bromine-thiourea and OPA-NH4+, respectively | AuNPs | fruits | 1.7 μg/L and 12.0 μg/L, respectively | 14.7–21.1 μg/L and 35.1–35.9 μg/L | [7] |
formaldehyde | 4-amino-5-hydrazino-3-mercapto-1,2,4-triazole | AgNPs | water and food | 0.15 μg/L | 1–1000 μg/L | [18] |
formaldehyde | 3-Methyl-2-benzothiazolinone hydrazone | Au/SiO2 NPs | aquatic products | 0.17μg/L | 0.40–4.8 μg/L | [20] |
acetone | 2,4-dinitrophenylhydrazine | iodide modified Ag nanoparticles (Ag IMNPs) | urine | 0.09 mM | 9.6–96 mg/L | [17] |
malondialdehyde | thiobarbituric acid | AgNPs | 0.45 nM | 0.45–4.5 μM | [21] | |
sodium dithionite | bis[4,4′-dithiodiphenyl azo-phenol] | A single cabbage-like Au microparticle | industry | 0.08 μM | 0.5–20 μM | [22] |
acetone | 4-(methylthio)benzaldehyde | Ag@AuNCs | [24] | |||
HNO3 and H2SO4 | ammonium hydroxide | Klarite substrate | 100 ppb | [25] | ||
NO | o-phenylenediamine | AuNPs | living cells | 10−7 M | [31] |
Target Analyte | Matrix | LOD | Ref. | |
---|---|---|---|---|
metal ion | Pb2+ | Water | 20 nM | [62] |
As3+ | Water | 59 ppt | [63] | |
As3+ | Lake water | 0.1 ppb | [64] | |
Hg2+ | Water | 5 nM | [65] | |
Hg2+ | Lake water | 100 fM | [66] | |
Hg2+ | River water | 2.5 nM | [67] | |
Small molecules | profenofos | Apple juice | 5 ppm | [69] |
phorate | Apple juice | 0.1 ppm | [69] | |
phorate | Apple skin | 0.05 mg/L | [85] | |
omethoate | Apple juice | 5 ppm | [69] | |
isocarbophos | Apple juice | 1 ppm | [69] | |
ATP | Water | 10 μM | [70] | |
AFB1 | Peanut milk | 0.48 pg/mL | [72] | |
AFB1 | Peanut | 0.4 fg/mL | [73] | |
biomacromolecules | PSA | Serum | 5 pg/mL | [74] |
PSA | Serum | 3.2 × 10−20 M | [75] | |
Thrombin | Serum | 5.7 × 10−17 M | [75] | |
DNA | Serum | 100 fM | [78] | |
MiRNA | Serum | 10 fM | [78] | |
Mucin-1 | Serum | 4.3 aM | [77] | |
Alpha fetoprotein | Serum | 0.097 aM | [76] | |
Lysozyme | [52] | |||
living organisms | S. typhimurium | Pork | 15 cfu/mL | [79] |
S. aureus | Pork | 35 cfu/mL | [79] | |
Hela cells | [81] | |||
CTC | Blood | 1 cell/mL | [83] | |
CTC | Blood | 20 cells/mL | [84] | |
mapping | ATP | intracellular ATP molecules | [70] | |
Lysozyme | different substrates | [52] | ||
membrane protein | Hela cells | [58] | ||
membrane protein | HepG2 cells | [81] |
Target Detector | MIPs-Sensor Type | Functional Monomer | Matrix | LOD | Ref. |
---|---|---|---|---|---|
Chlorpyrifos | MISPE-SERS | Methacrylic acid (MAA) | Apple juice | 0.01 mg/L | [91] |
Sudan dyes | MISPE-SERS | 3-(trimethoxysilyl)propyl methacrylate (TPM) | chili powder | 1.5 ng/g | [92] |
Melamine | MISPE-SERS | MAA | Milk | 0.005 mM | [93] |
Chloramphenicol | MISPE-SERS | Acrylamide (AM) | Honey and milk | 0.1 ppm | [94] |
Alpha-tocopherol | MISPE-SERS | MAA | Vegetable oils | 10 ppb | [95] |
Sudan IV | Au NPs doped MIPs | MAA | Sudan derivatives | [89] | |
Theophylline | Ag NPs doped MIPs | MAA | Green tea drinks | 3.5 μM | [97] |
Metformin | MIM-SERS | MAA | Hypoglycemic agents | 5 mg/mL | [104] |
R6G | Core-shell | AM | Water | 10−12 M | [106] |
Bisphenol A | Core-shell | 3-(triethoxysilyl)propyl isocyanate (TEPIC) | River water | 0.1 mg/L | [107] |
Transferrin | Core-shell | Polydopamine (PDA) | human serum | 10−8 M | [123] |
Glycoproteins | Sandwich structure | Boronic acids | human serum | 13.8 ± 3.3 ng/mL | [122] |
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Wang, F.; Cao, S.; Yan, R.; Wang, Z.; Wang, D.; Yang, H. Selectivity/Specificity Improvement Strategies in Surface-Enhanced Raman Spectroscopy Analysis. Sensors 2017, 17, 2689. https://doi.org/10.3390/s17112689
Wang F, Cao S, Yan R, Wang Z, Wang D, Yang H. Selectivity/Specificity Improvement Strategies in Surface-Enhanced Raman Spectroscopy Analysis. Sensors. 2017; 17(11):2689. https://doi.org/10.3390/s17112689
Chicago/Turabian StyleWang, Feng, Shiyu Cao, Ruxia Yan, Zewei Wang, Dan Wang, and Haifeng Yang. 2017. "Selectivity/Specificity Improvement Strategies in Surface-Enhanced Raman Spectroscopy Analysis" Sensors 17, no. 11: 2689. https://doi.org/10.3390/s17112689
APA StyleWang, F., Cao, S., Yan, R., Wang, Z., Wang, D., & Yang, H. (2017). Selectivity/Specificity Improvement Strategies in Surface-Enhanced Raman Spectroscopy Analysis. Sensors, 17(11), 2689. https://doi.org/10.3390/s17112689