Quantitative Surface-Enhanced Raman Spectroscopy through the Interface-Assisted Self-Assembly of Three- Dimensional Silver Nanorod Substrates

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1 SUPPORTING INFORMATION Quantitative Surface-Enhanced Raman Spectroscopy through the Interface-Assisted Self-Assembly of Three- Dimensional Silver Nanorod Substrates Si-Ying Liu,, Xiang-Dong Tian, *,, Yun Zhang, *,, Jian-Feng Li CAS Key Laboratory of Design and Assembly of Functional Nanostructures, and Fujian Provincial Key Laboratory of Nanomaterials, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou , China Xiamen Institute of Rare Earth Materials, Haixi Institute, Chinese Academy of Sciences, Xiamen , China State Key Laboratory of Physical Chemistry of Solid Surfaces and Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen , China * * Page S1

2 Table of Content 1. Figure S1. Normal Raman spectra of the molecules (urea, melamine, Phe and Trp). 2. Figure S2. Distribution of the diameter and TEM image of the thickness of PVP of AgNRs and SEM images of other substrates. 3. Figure S3. AFM topographic images of substrates with layers from Figure S4. SERS spectra and mapping images of assembled AgNR substrates (NL: 1-20) with 532 nm laser. 5. Figure S5. SERS spectra and mapping images of assembled AgNR substrates (NL: 1-20) with 633 nm laser. 6. Figure S6. SERS spectra and mapping images of assembled AgNR substrates (NL: 1-20) with 785 nm laser. 7. Figure S7. UV-vis-NIR spectra of AgNRs and SEM images of other 3D AgNR substrates with longer lengths. 8. Figure S8. Normal Raman spectrum and SERS spectrum of 4-MBA. 9. Figure S9. Stability of the 3D substrate. 10. Table S1. RSD of SERS mapping intensity under three lasers. 11. Table S2. Comparison of the enhancement reproducibility among different SERS substrates. 12. Table S3. Parameters for the calculation of ASEF. 13. Table S4. ASEF of all substrates with different NL. 14. Table S5. Comparison between 2D and 3D AgNR SERS substrates. 15. Table S6. Calculation of the LODs for the different analytes. 16.Table S7. Comparation of the detection sensitivity of the 3D AgNR SERS with other methods. Page S2

3 Figure S1. Normal Raman spectra of powders of molecules. (A) urea: 1.76 mw (532 nm), exposure time: 1 s, (B) melamine: 0.76 mw (532 nm), exposure time: 1 s, (C) Phe: 1.09 mw (785 nm), exposure time: 10 s, (D) Trp: 1.09 mw (785 nm), exposure time: 10 s. All the Raman scattering lights were collected through the 10 objective (NA=0.25). Page S3

4 Figure S2. (A) Distribution of the diameter of AgNRs. (B) Thickness of the PVP. (C-J) SEM images of the AgNR substrates with NL of (C) 1, (D) 3, (E) 5, (F) 7, (G) 8, (H) 14, (I) 16, (J) 18, respectively. Insets: The cross-section images of the 3D AgNR (labeled with red dashed line) and the high-resolution images of the surface of the substrates. The scale bar is 10 μm for the large-area images. The scale bar for both cross-section and high-resolution images is 500 nm. Page S4

5 Figure S3. AFM topographic images of substrates with layers from (A) 1 layer. (B) 2 layers. (C) 3 layers. (D) 4 layers. (E) 5 layers. (F) 6 layers. (G) 7 layers. (H) 8 layers. (I) 9 layers. (J) 10 layers. (K) 12 layers. (L) 14 layers. (M) 16 layers. (N) 18 layers. (O) 20 layers. (P) Relationship between Rq and NL. Page S5

6 Page S6

7 Figure S4. SERS spectra (A) and mapping images (B) of AgNR substrates with NL of The mapping area is μm 2 with step of 5 μm. The laser line is 532 nm and the acquisition time is 1 s for each pixel. Page S7

8 Page S8

9 Figure S5. SERS spectra (A) and mapping images (B) of AgNR substrates with NL of The mapping area is μm 2 with step of 5 μm. The laser line is 633 nm and the acquisition time is 1 s for each pixel. Page S9

10 Page S10

11 Figure S6. SERS spectra (A) and mapping images (B) of AgNR substrates with NL of The mapping area is μm 2 with step of 5 μm. The laser line is 785 nm and the acquisition time is 1 s for each pixel. Page S11

12 Table S1. RSD of the intensity over the mapped area shown above. RSD NL nm 8% 5% 3% 6% 4% 6% 5% 4% 4% 3% 5% 6% 6% 6% 3% 633 nm 11% 7% 5% 8% 9% 7% 8% 8% 7% 5% 5% 5% 8% 9% 7% 785 nm 12% 8% 5% 6% 6% 7% 6% 9% 5% 5% 6% 9% 8% 7% 8% NOTE Mapping area: μm 2 ; Step: 5 μm; Acquisition time: 1 s; Laser power: 532 nm-0.76 mw, 633 nm-0.44 mw, 785 nm-4.34 mw Table S2. Comparison of the enhancement reproducibility among different SERS substrates SERS substrates Fabrication Mapping areas RSD Reference methods (μm 2 ) 1 3D AgNR substrates in this Bottom-up (with 5 6% (at 532 nm); This report report (NL: 6) Self-assembly μm step) 7% (both at 633 and 785 nm) 2 2D hexagonal close-packed Bottom-up (with 4.3% (at 532 nm) [1] superlattices of Ag nanoparticles Self-assembly 0.75 μm step) 3 Au nanoparticle monolayer Bottom-up Self-assembly From 105 different locations 3.1% (at 785 nm) [2] 4 gold nanoparticles sliding on Top-down (with 10% (at 780 nm) [3] recyclable nanohoodoos microfabrication 5 μm step) (GNRH) 5 Metal Insulator Metal Top-down (with > 10% (at 633 [4] Capped Polymer Nanopillars microfabrication 5 μm step) nm) 6 Gold film over nanosphere Template (with 5 15%-20% (at 633 [5] (AuFON) techniques μm step) nm) 7 gold silica core shell Template From 15 9% (at 785 nm) [6] patterning of a large format Au ZnO forest techniques randomly selected spots 8 Au-Nanoparticle- Template From % (at [7] Functionalized Si Nanorod Arrays techniques randomly selected spots 633 nm) Page S12

13 Figure S7. (A) Extinction spectra of the AgNRs (length from 53 nm to 245 nm). (B-F) SEM images of the 3D AgNR substrates with AgNR lengths of (B) 148 nm, (C) 170 nm, (D) 200 nm, (E) 212 nm, (F) 245 nm, respectively. Insets: The cross-section images of the 3D AgNR (labeled with red dashed line). The scale bars are 500 nm. Derivation of the averaged surface enhancement factor (ASEF): Here we provide a method to calculate the ASEF for the situation where silver nanorods as SERS substrates. We calculated the ASEF on the optimized three-dimensional silver nanorods substrate. The ASEF can be calculated by taking the ratio of the SERS signal contributed by a surface-adsorbed molecule to the normal Raman signal contributed by a molecule in a solution or in the bulk: 8 ASEF = I surf I bulk where Isurf and Ibulk denote the integrated intensities for the strongest band of the surface and solution species, respectively, Nsurf and Nbulk represent the numbers of the corresponding surface and solution molecules effectively excited by the laser beam, respectively. Isurf and Ibulk can be directly obtained by integrating over the Raman spectra obtained in Fig S8. The Nbulk is calculated by: N bulk = S laser h c N A (2) Where c is the concentration of normal solution with 0.1 M, NA is the Avogadro constant, h is the thickness of solution layer, here is 2508 μm, and Slaser is the laser spot area which can be calculated by follows: S laser = πr 2 (3) And then r (the radius of laser spot) is calculated by the following equation: N surf N bulk (1) r = 1.22λ 2NA (4) where λ is wavelength of the incident light (here is 532 nm) and NA is numerical aperture of objective we used (10, NA=0.25). As for solid substrate, Nsurf is calculated by: Where Np is calculated by: N surf = N p S p σ (5) Page S13

14 Where Sp (the single nanorod area) is calculated by: N p = S laser S p NL (6) S p = πdh (7) Where d is the diameter of the nanorod, here is 20 nm, h is the length of the nanorod, here is 200 nm. Where NL is the layer of the substrates. With the eq 1-7, the ASEF of solid substrate can be expressed as follow: ASEF = I surf hcσn A NL I bulk (8) Raman intensity of the optimized 3D substrate (NL: 6) is demonstrated in Table S3 and the ASEF of all substrates with different NL is shown in Table S4. Figure S8. Normal Raman spectrum of 4-MBA (black line) and the SERS spectrum of it collected through the 3D AgNR substrate with NL of 6 (red line). Table S3. Parameters for ASEF calculation and ASEF of optimized 3D substrates. Parameter Isurf h c σ NA Ibulk cps 2508 μm 0.1 M 0.22 nm cps ASEF Page S14

15 Table S4. ASEF (at 532 nm laser line) of the 3D AgNR substrates with different number of layers (NL). NL EF NL EF NL EF Table S5. Comparation between 2D and 3D AgNR SERS substrates. SERS substrate RSD Normalized SERS Intensity Normalized EF Plasmonic Coupling 2D >10%(15%) 1 1 Intralayer (in-plane) coupling 6.09 (532 laser) 1.92 (532 laser) 3D <10%(6%) (633 laser) 4.05 (633 laser) (785 laser) 8.00 (785 laser) Interlayer (out-of-plane) and intralayer coupling Figure S9. (A-J) SEM images of the 3D substrate. (K) The SERS intensity of the 3D substrate. (L) The UV-vis-NIR spectra of the 3D substrate. The scale bar is 10 μm for the large-area images. The scale bar is 500 nm for high-resolution images. Page S15

16 The LODs of the 3D AgNR substrate for the detection of different analytes: The LODs were calculated according to the following expressions: 9 LOD = 3SD σ (1) Where SD is the standard deviation of SERS intensity of the blank substrate, σ is the slope of the calibration curve. Here, the SD and σ of urea, melamine, Phe and Trp are listed in the Table S6. It should be pointed out that we have measured the SERS intensity of several concentrations of each analyst around the lowest detection concentration. The detection sensitivity of the 3D AgNR substrate is proved by comparing the LODs of the four analysts with the reported values. As shown in table S7, the 3D AgNR substrate improves the LOD of urea and melamine by a factor of 13 and 4, respectively. Table S6. Parameters for the calculation of the LODs of the different analytes. Analytes Urea Melamine Phe Trp Parameters (at 1009 cm -1 ) (at 686 cm -1 ) (at 1000 cm -1 ) (at 1010 cm -1 ) Mean cps cps cps cps SD cps cps cps cps LOD μm pm 5.38 μm 10.6 μm Table S7. Comparation of the detection sensitivity of the 3D AgNR SERS with other methods. Analytes Methods Linear range LOD Reference Electrochemistry mm 0.2 mm [10] Fluorimetry mm mm [11] Urea Microfluidic - 3 mm [12] SERS 1-20 mm 0.67 mm [13] SERS (this work) mm mm this work Fluorimetry 0-20 μm 0.03 μm [14] Colorimetry μm 0.01 μm [15] Melamine HPLC μm μm [16] SERS μm μm [17] SERS (this work) μm μm this work LC-MS/MS μm 0.12 μm [18] Phe SERS (this work) μm 5.38 μm this work LC-MS/MS μm 0.15 μm [18] Trp SERS (this work) μm 10.6 μm this work Page S16

17 REFERENCE (1) Chen, H. Y.; Lin, M. H.; Wang, C. Y.; Chang, Y. M.; Gwo, S. Large-Scale Hot Spot Engineering for Quantitative SERS at the Single-Molecule Scale, J. Am. Chem. Soc. 2015, 137, (2) Yang, G.; Nanda, J.; Wang, B.; Gang, C.; Hallinan, D. T. Self-Assembly of Large Gold Nanoparticles for Surface- Enhanced Raman Spectroscopy, ACS Appl. Mater. Interfaces 2017, 9, (3) Wu, K.; Li, T.; Schmidt, M. S.; Rindzevicius, T.; Boisen, A.; Ndoni, S. Gold Nanoparticles Sliding on Recyclable Nanohoodoos Engineered for Surface Enhanced Raman Spectroscopy, Adv. Funct. Mater. 2018, 28, (4) Hackett, L. P.; Li, W.; Ameen, A.; Goddard, L. L.; Liu, G. L. Plasmonic Metal Insulator Metal Capped Polymer Nanopillars for SERS Analysis of Protein Protein Interactions, J. Phys. Chem. C 2018, 122, (5) Štolcová, L.; Peksa, V.; Proška, J.; Procházka, M. Gold film over very small (107 nm) spheres as efficient substrate for sensitive and reproducible surface enhanced Raman scattering (SERS) detection of biologically important molecules, J. Raman Spectrosc. 2018, 49, (6) Hoang, P.; Khashab, N. M. Non-Resonant Large Format Surface Enhanced Raman Scattering Substrates for Selective Detection and Quantification of Xylene Isomers, Chem. Mater. 2017, 29, (7) Lin, D.; Wu, Z.; Li, S.; Zhao, W.; Ma, C.; Wang, J.; Jiang, Z.; Zhong, Z.; Zheng, Y.; Yang, X. Large-Area Au- Nanoparticle-Functionalized Si Nanorod Arrays for Spatially Uniform Surface-Enhanced Raman Spectroscopy, ACS Nano 2017, 11, (8) Lin, X. M.; Cui, Y.; Xu, Y. H.; Ren, B.; Tian, Z. Q. Surface-enhanced Raman spectroscopy: substrate-related issues, Anal. Bioanal. Chem. 2009, 394, (9) Xiaoyu Zhang, M. A. Y., Olga Lyandres, and Richard P. Van Duyne. Rapid Detection of an Anthrax Biomarker by Surface-Enhanced Raman Spectroscopy, J. Am. Chem. Soc. 2005, 127, (10) Emami Meibodi, A. S.; Haghjoo, S. Amperometric urea biosensor based on covalently immobilized urease on an electrochemically polymerized film of polyaniline containing MWCNTs, Synth. Met. 2014, 194, 1-6. (11) Deng, H. H.; Wu, G. W.; Zou, Z. Q.; Peng, H. P.; Liu, A. L.; Lin, X. H.; Xia, X. H.; Chen, W. ph-sensitive gold nanoclusters: preparation and analytical applications for urea, urease, and urease inhibitor detection, Chem. Commun. 2015, 51, (12) Khan, M.; Park, S.-Y. General Liquid-crystal droplets produced by microfluidics for urea detection, Sens. Actuators, B 2014, 202, (13) Ming Li, F. Z., Jianbo Zeng, Ji Qi, Jing Lu, and Wei-Chuan Shih*. Microfluidic surface-enhanced Raman scattering sensor with monolithically integrated nanoporous gold disk arrays for rapid and label-free biomolecular detection, J. Biomed. Opt. 2014, 19, (14) Li, N.; Liu, T.; Liu, S. G.; Lin, S. M.; Fan, Y. Z.; Luo, H. Q.; Li, N. B. Visible and fluorescent detection of melamine in raw milk with one-step synthesized silver nanoparticles using carbon dots as the reductant and stabilizer, Sens. Actuators, B 2017, 248, (15) Alam, M. F.; Laskar, A. A.; Ahmed, S.; Shaida, M. A.; Younus, H. Colorimetric method for the detection of melamine using in-situ formed silver nanoparticles via tannic acid, Spectrochim. Acta, Part A 2017, 183, (16) Qi, Y.; Li, G. Preparation of Magnetic Molecularly Imprinted Polymer for Melamine and its application in milk sample analysis by HPLC, Am. J. Biomed. Sci. 2016, 05, (17) Kim, A.; Barcelo, S. J.; Williams, R. S.; Li, Z. Melamine sensing in milk products by using surface enhanced Raman scattering, Anal. Chem. 2012, 84, (18) Marcos, J.; Renau, N.; Valverde, O.; Aznar-Lain, G.; Gracia-Rubio, I.; Gonzalez-Sepulveda, M.; Perez-Jurado, L. A.; Ventura, R.; Segura, J.; Pozo, O. J. Targeting tryptophan and tyrosine metabolism by liquid chromatography tandem mass spectrometry, J. Chromatogr. A 2016, 1434, Page S17

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