1. Introduction

The localized surface plasmon resonance (LSPR) is widely used in biomolecular identification and medical diagnosis [1–3]. Noble metal nanoparticles and nanostructures generate LSPR and induce resonant optical absorption with oscillations of electrons on the metal surfaces. This amplifies the electromagnetic field around the surface. Surface-enhanced Raman scattering (SERS) with unique fingerprint recognition characteristics and nano-scale surface structures is an important application area of these materials [4–7]. The SERS effect originates from a physical and chemical mechanism recognized by Van Duyne and Creighton [8]. The physical or electromagnetic mechanism (EM) of plasmon resonance enhances the electric field and enhances Raman scattering up to 10⁸–10¹⁰ times when the target molecules are in a gap between plasmonic nanoaggregates (the “hot spot”) [9–11]. The charge transfer between biomolecules and substrates is the chemical mechanism (CM). The enhancement ability is highly related to the number of “hot spot” and the spacing between nanostructures. Recent experiments have shown that three-dimensional (3D) nanostructures of several morphologies with sufficient “hot spots” can be manufactured with different preparation methods. The composite silver and gold nanoparticles (AgNPs and AuNPs) with high sensitivity and selectivity have been widely used in SERS since they were discovered by Mustafa et al [12–17]. Nevertheless, the detection of non-thiolated aromatic organic molecules including Rhodamine 6G (R6G) remains an enormous challenge mainly due to the lack of efficient adsorption of organic molecules on the metal nanostructure surface.

Hybrid 3D nanostructures formed by 2D nanomaterials and metal nanostructures are excellent substrates for SERS due to their high surface-to-volume ratio, effective adsorption of organic molecules, and established tunability of the surface plasmon resonance across a range of wavelengths to match excitation lasers. In particular, the unique physical-chemical properties of 2D nanomaterials such as graphene oxide (GO) and molybdenum disulfide (MoS₂) can promote electrostatic interactions with nanostructures. The fabrication of GO/AgNPs and GO/AuNPs as well as their application in SERS has been reported by Wang et al. and Huang et al [15,18]. Recently, the formation of MoS₂ and AuNPs has been widely studied including MoS₂-microspheres decorated with AuNPs and 3D-MoS₂ nanoflowers decorated with AuNPs [19]. The reducibility of MoS₂ plays a major role in bonding with AuNPs. However, aggregation always occurs due to the AuNPs or AgNPs with large specific surface areas and surface energies. Thus, methods that could gap the space of AuNPs or AgNPs are urgently needed. Many methods have been reported to form uniform gaps between nanoparticles. Zhang et al reported the preparation of large-area Ag nanorod array substrates via porous anodic aluminum oxide (AAO) template-assisted electrochemical deposition [20]. Self-assembly technologies are simple, efficient, and easy to scale. They are diffusely adopted to form sbstrates with excellent Raman enhancement [21–22]. These simple and cost-effective large-scale self-assemblies of large colloidal AuNPs arrays with regular ≈1 nm gaps has also been reported by Chen et al. However, manufacturing extended arrays of metal NPs via 2D material templates is more difficult than other methods.

Here, we prepared GO@MoS₂ that reacted with HAuCl₄ to form a uniform GO mesh structure on the surface of AuNPs. The AgNPs were then bonded to form AuNPs@GO mesh@AgNPs via spin coating. The GO mesh template provides landing sites for AgNPs and ensures their spacing. In addition, the mesh formed by the rupture and systole of GO can adsorb and concentrate target molecules to the nanogaps of AgNPs making it an attractive candidate for SERS applications because the AgNPs fall into meshes for suitable spacing and hot-spot formation. DNA was then transferred to the surface of the substrate and can bind to the AgNPs for sensitive, rapid, and label-free detection of DNA. Due to the excellent adsorption capacity and excellent biocompatibility to various molecules of GO mesh, the DNA were effectively aggregated on the GO mesh. The ability to detect analytes creates excellent conditions for single molecule detection. This work uses different nucleotides (A, T, C) of DNA, and the differences in the Raman peaks of these DNA sequences were distinguished by SERS. The SERS-based detection limits (DNA: 10⁻¹⁰ M; CV: 10⁻¹⁵ M) confirm the enhancement of AuNPs@GO mesh@AgNPs. This was further validated with finite-difference time-domain (FDTD) calculations. The high sensitivity, good reliability, and stability of this method confirms that it has utility as a SERS substrate for biomolecule detection.

2. Experimental methods

2.1 Materials

We used foam Ni (1 mm of thickness), ammonium tetrathiomolybdate ((NH₄)₂MoS₄, 99.99%, Aladdin), graphene oxide (GO; obtained from oxidation treatment of graphite based on our previous method), gold chloride trihydrate (HAuCl₄, 99%, Aladdin), silver nitrate (AgNO₃, 99.99%, Aladdin), and polyvinylpyrrolidone (PVP, Mw = 55000, Aladdin). Label-free DNA, and adenine and cytosine bases were purchased from Sangon Biotech (Shanghai) Co. Ltd.

2.2 Preparation of GO/MoS₂ materials

We first dissolved (NH₄)₂MoS₄ powder (0.01 g) into 1 ml of ID water to form the solution (0.01 g/ml). A solution of GO (0.01 g/ml) was then added to form a mixed solution of 2 ml. The mixed solution was then added to the surface of foam Ni that had been cleaned by acetone, alcohol, and ID water. Finally, the foam Ni with a mixture of GO and (NH₄)₂MoS₄ was treated at 500°C (10⁻³ Pa) for annealing with 100 sccm Ar and 20 sccm H₂. The GO/MoS₂ hybrid materials were successfully formed on the foam Ni after heating for 120 min. The proper weight ratio of GO and (NH₄)₂MoS₄ has been discussed in our prior work. In fact, thanks to the hydrophobic domains of pristine unfunctionalized sp2 carbon, only a few layers of MoS₂ were formed via the decomposition of (NH₄)₂MoS₄. These layers were absorbed on the underlying surface [23].

2.3 Formation structure of AuNPs@GO mesh

The HAuCl4 powder (0.001 g) was added to 10 ml of ID water. GO/MoS2 materials were immersed in a solution of HAuCl₄ for 5 min. The AuNPs with a diameter of ∼40 nm were formed due to the reductive reactivity of MoS₂ with HAuCl₄ (Fig. 1). During this process, GO was stretched to varying degrees due to the growth of AuNPs leading to wrinkles and cracks. A uniform mesh structure was then formed on the surface. Finally, the AuNPs@GO mesh was treated in a vacuum at 50°C for 30 min to dry.

 

Fig. 1. Preparation steps of AuNPs@GOmesh@AgNPs and principle of analyte detection.

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2.4 Synthesis of AgNPs and the formation of AuNPs@GO mesh@AgNPs

The preparation of AgNPs has been elaborated in detail previously [24]. Here, we used 0.15 g of AgNO₃ to prepare AgNPs with a diameter of ∼50 nm. In addition, polyvinylpyrrolidone (PVP), as a stabilizer in the reaction, was added for the uniform dispersion of AgNPs. To obtain AuNPs@GO mesh@AgNPs, we dispersed the AgNPs in water to form a 3 mg/ml solution that was spin-coated on the surface of AuNPs@GO mesh surface at 800 rpm for 30s to form a AuNPs@GO mesh@AgNPs nanostructure (Fig. 1).

2.5 Structural characterization and Raman detection

Scanning electron microscopy (SEM, Zeiss Gemini Ultra-55), Ultraviolet visible spectrophotometer (UV, Shimadzu UV-3000) and Energy Dispersive Spectrometer (EDS, Bruker XFlash 630) were used to characterize the morphologies and elemental composition of the sample. Crystal violet (CV) was purchased from Mai-kun Chemical Co. Ltd. The Raman instrument was a Horiba HR Evolution 800 with laser excitation at 532 nm. The laser power was set to 0.5 mW to avoid sample heating and photo-induced damage. The 50× objective was used for detection, and the laser spot was 1 um². The Raman map was measured over 20 mm × 20 mm area with a step of 2 mm. 15 randomly selected spots on the substrate are measured and averaged to obtain the Raman spectra at each concentration. We have chosen to base stored in a thermostatic oven to reduce its oxidation caused by exposure in the air.

2.6 FDTD simulations

Theoretical modelling of AuNPs@GO mesh@AgNPs was done using commercial software Lumerical Solutions, which is based on Finite Difference Time Domain (FDTD) method, to visualise the electric field enhancement around the fabricated substrate. FDTD method is a convenient approach used to simulate electromagnetic field distribution around objects of arbitrary geometry and complex shapes using discretization of Maxwell's equation (as shown in supporting information). In this work, the region of computation is selected to be a square grid points with a resolution of x = 0.4 nm, y = 0.4 nm and z = 0.4 nm. A total field scattering field plane wave source propagating along z- axis was used for 3D simulation. The diameter of the AuNPs and AgNPs were set to 50 nm based on SEM; the diameter of the mesh was set to 40 nm, the branches of GO were 10 nm and the distance between the adjacent AgNPs was 10 nm. Plane wave polarized light at 532 nm was used along the z-axis.

2.7 DNA discrimination at the single-molecule level

The DNA sequences are listed below: 9-mer DNA (adenine (A)): 5′-AAA AAA AAA-3′; 9-mer DNA (cytosine (C)): 5′-CCC CCC CCC-3′ and 9-mer DNA (thymine (T)): 5′-TTT TTT TTT-3′; 12-mer Probe DNA: 5′-CGCCAATACGAC-3′; 12-mer complementary DNA: 5′-GTCGTATTGGCG-3′ and 12-mer non complementary DNA: 5′-GTCGAATCGTCG-3′. The combination of substrate and DNA was performed as follows: First, the DNA (50 µL, 200 nM) was mixed with AgNP via overnight incubation. A mixed solution of AgNPs and DNA was then spin-coated on the surface of AuNPs@GO mesh to form a sample that dried under vacuum.

3. Results and discussion

Figure 2(a) shows that the Ni foam is a 3D structure with multiple cascaded amplification mechanisms to provide stronger Raman signal. AuNPs@GO mesh area, as the product of the reaction between GO/MoS₂ and HAuCl₄, were imaged with SEM (Fig. 2(b)). The GO changed from sheet to mesh and was evenly distributed on the surface of the AuNPs. The GO mesh was composed of a number of gaps with a diameter of ∼10 nm. The reason for formation of the GO mesh is that the GO was stretched and wrinkled to form gaps during growth of AuNPs. As the reaction proceeded, these gaps overlapped to form uniform holes with a diameter of ∼40 nm. Interestingly, ∼40 nm holes and ∼10 nm gaps have been achieved in the structure. These provide great anchoring sites and overcome AgNPs aggregation.

 

Fig. 2. (a) and (b) are SEM images of the AuNPs@GO mesh; (c) SEM image of AuNPs@GO mesh@AgNPs; (d) schematic diagram of AuNPs@GO mesh@AgNPs structure; (e) Raman intensity of CV (10⁻⁷ M) detected with different substrates; and (f) Raman intensity of CV on three different substrates.

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The AuNPs@GO mesh@AgNPs was formed after spin-coating AgNPs on the surface of AuNPs@GO mesh (Fig. 2(c)). The schematic of AuNPs@GO mesh@AgNPs is also shown in Fig. 2(d). For comparison, their morphology is depicted together. AgNPs with uniform diameter are obviously distributed in the GO mesh to guarantee spacing between AgNPs. There is a significant increase in the “hot spots” on the surface upon addition of AgNPs. The better SERS enhancement would be achieved when a wavelength is corresponded to the absorption peak of the substrate at Raman test. In our experiment, the absorbance of AuNPs@GO mesh@AgNPs was shown in Fig. 8. The LSPR peak of the AuNPs@GO mesh@AgNPs is located at 488 nm. However, there are some reasons for using the wavelength of 532 nm rather than near 488 nm. Firstly, a crest of absorbance around 423 nm-550 nm implies that there is a broad absorption region for the sturcture, well matching with the exciting light (=532 nm) in Raman test. This is expected to enhance the SERS activity.

To explore the SERS effects upon addition of AgNPs, we conducted Raman spectroscopy before and after addition. Figure 2(e) shows the Raman performance of 10⁻⁷ M CV on different substrates (foam Ni, AuNPs@GO mesh, and AuNPs@GO mesh@AgNPs). Without the addition of NPs (e.g., AuNPs, AgNPs, and AuNPs@AgNPs), the enhancement effect of foam Ni is insufficient to achieve the highly sensitive CV detection. However, it can be detected under 532 nm laser when added to the AuNPs@GO mesh and AuNPs@GO mesh@AgNPs substrate. All of the quintessential vibrational modes of CV including 910, 1173, 1365, and 1580 cm⁻¹ are observed agree with prior reports. To compare their Raman enhancement, the characteristic peak intensities of CV were selected for comparison (Fig. 2(f)). When the AgNPs were mixed in the AuNPs@GO mesh, the Raman intensity increases by almost two-fold indicating a significant improvement in Raman performance. Therefore, the AuNPs@GO mesh@AgNPs structure combines the strong plasmonic electric field enhancement effect of AgNPs with the chemical stability of AuNPs. It then combines this with GO’s single-atom feature, mechanical flexibility, and biological compatibility. All components can enhance SERS activity [25–27].

One important point of the AuNPs@GO mesh@AgNPs structure is that the suitable diameter of holes on the surface of AuNPs can be changed via the reaction time between GO/MoS₂ and HAuCl₄. Figures 3(a₁) and 3(a₃) are SEM images of AuNPs@GO mesh@AgNPs with reaction times of 1 min and 5 min. This indicates that the GO mesh with a reaction time of 1 min has fewer and smaller holes than the 5-min sample (Fig. 2(b)). This decreases the binding sites and increases the AgNPs distance. This is because GO has not been subjected to sufficient tensile forces to rupture. The SERS spectra of 10⁻⁸ M CV on AuNPs@GO mesh@AgNPs with 1, 5, and 9 min of reaction times are shown in Fig. 3(a₂). he GO mesh was destroyed at 9 min of reaction and cannot control AgNPs spacing. The strongest Raman signal was obtained at 5 minutes, which is consistent with the SEM data. The density of AgNPs is also important to the self-assembly. The SEM image and Raman signal in Fig. 3(b₁), 3(b₂) and 3(b₃) show that if the concentration is too large, then it will cause the AgNPs to aggregate. In contrast, there will be an insufficient number of “hot spots” on the surface. These results revealed that the strongest SERS signal and the largest electric field enhancement platform are obtained when the concentration of spin-coated AgNPs is 3 mg/ml in the AuNPs@GO mesh array mixture (Fig. 2(c)).

 

Fig. 3. (a₂) Raman spectra of CV (10⁻⁷ M) on AuNPs@GO mesh@AgNPs with different reaction times of GO@MoS² and HAuC¹⁴; (a₁) and (a₃) SEM image of AuNPs@GO mesh@AgNPs with 9 min and 1 min of reaction time; (b₂) Raman spectra of CV (10⁻⁷ M) on AuNPs@GO mesh@AgNPs with different concentrations of AgNPs in the spin-coated process; (b₁) and (b₃) different morphologies of AuNPs@GO mesh@AgNPs.

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We performed FDTD simulations to calculate electric field distributions and investigate the enhancement of electromagnetic fields in the proposed structures [28–29]. Using the SEM structures in Figs. 2(b) and 2(c), we modeled the AuNPs@GO mesh@AgNPs substrates. The diameter of AuNPs and AgNPs were set to 50 nm and 45 nm. The diameter of GO gaps was 10 nm. According to the simulation results, we selected five electric field distributions at intervals of 20 nm and 15 nm in the horizontal and vertical planes to clearly show the electric field simulated by FDTD. For ease of reading, we adjusted the upper limit of the scale to visually represent the distribution of the electric field throughout the structure. Maximum value of the real electric field is not 12 but 27. The highest value of electric field strength is at the the junction of GO mesh, AuNPs and AgNPs, and EF is 2.8 ×10⁵. Calculated electric field distributions are shown in Figs. 4(a) and 4(b) for different heights. Although the distribution of electromagnetic fields has changed with different heights, the induced electromagnetic fields of AuNPs@GO mesh@AgNPs are much stronger than the incident field. It indicates that most areas of the substrate were covered by “hot spot”, ensuring that the substrate can perform SERS detection accurately and consistently. Meanwhile, an excellent electric field was exhibited at the junction of GO mesh, AuNPs and AgNPs. In the Raman test, due to the excellent adsorption capacity and excellent biocompatibility to various molecules of GO, the molecules were effectively aggregated on the GO mesh. The ability to detect analytes creates excellent conditions for single molecule detection.

 

Fig. 4. (a) and (b) electric field distributions of AuNPs@GO mesh@AgNPs with different heights.

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In particularly, the Enhancement factor (EF) given by the power of the electric field can be calculated ($EF = {|{E/{E_0}} |^4}$). Here, the max E obtained in our simulation is 23-fold higher than E₀, and the maximum EF is 2.8 ×10⁵. In addition, the EF can also be obtained by the Raman intensity of CV; the standard equation is $EF = \frac{{{C_{RS}} \times {I_{SERS}}}}{{{C_{SERS}} \times {I_{RS}}}}$. Here, ${C_{RS}}$ and ${C_{SERS}}$ are the number of molecules absorbed on the surface of foam Ni and AuNPs@GO mesh@AgNPs ; ${I_{RS}}$ and ${I_{SERS}}$ are the intensity of CV on the substrate. The EF of AuNPs@GO mesh@AgNPs is 4.2×10⁸ indicating the high enhancement activity. However, there is a certain inconsistency of theory and experiment, which has been caused by the CM of the GO mesh and the size of laser spot. One the one hand, the charge transfer between GO@MoS₂ and the molecules result in a chemical enhancement (CM), which can provide 10-100 times enhancement. One the other hand, the spot diameter of the incident laser is around 1µm which should cover several nanostructures, and the SERS performance of the substrate is attributed to the synergistic electromagnetic enhancement (EM) of several AuNPs@GO mesh@AgNPs. Therefore, the distribution of the electric field strength agrees well with the SERS activity shown in Fig. 2(e).

The Raman substrate was characterized for reproducibility, uniformity, and detection limits [30–32]. Figure 5(a) shows the detection limit of AuNPs@GO mesh@AgNPs. The Raman signal of CV is still observable at 10⁻¹⁵ M. We evaluated the linear correlation between signal at 910 and 1173 cm⁻¹ and concentration. The linear correlation coefficients are 0.983 and 0.979 at 910 and 1173 cm⁻¹, respectively, for CV concentrations of 10⁻¹⁰−10⁻¹⁵ M (Fig. 5(b)). This confirms the good performance of AuNPs@GO mesh@AgNPs as a SERS substrate.

 

Fig. 5. (a) The detection limit and Raman spectra of CV from 10⁻¹⁰ to 10⁻¹⁵ M on the AuNPs@GO mesh@AgNPs substrate; (b) linear fitting for the intensity changes of peak 910 cm⁻¹ and 1173 cm⁻¹; (c) The 30 different points of CV on AuNPs@GO mesh@AgNPs substrate and RSD of the peak at 910 cm⁻¹; (d) Raman mapping of this substrate; (e) SERS spectra of CV detected by AuNPs@GO mesh@AgNPs for 30 days.

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We also defined the SERS reproducibility by the relative standard deviation (RSD). We randomly collected 30 CV signals on the surface of the AuNPs@GO mesh@AgNPs and calculated the RSD at 1650 cm⁻¹ to be 5.2% (Fig. 5(c)). Raman mapping further evaluated the substrate uniformity (Fig. 5(d)); here, yellow represents a high peak and red represents a low peak demonstrating the good uniformity of the Raman signals. In order to make the results more accurate, we used a statistical method to test the Raman spectra of 10 different positions of 10 samples. The Raman spectra of CV at 910cm⁻¹ was shown in Fig. 9. The AgNPs are easily oxidized, and it is particularly important to detect the Raman enhancement properties of the substrate over time. To evaluate the stability of the structure, the Raman spectrum of CV was tested every three days for one months (Fig. 5(e)). The intensity of the Raman peak showed little attenuation, which indicated that the AuNPs@GO mesh@AgNPs can maintain its Raman activity for an extended period.

SERS-based DNA detection is an important emerging topic. It is rapid, easy-to-use, and cost-effective versus PCR and fluorescence-based microarrays [33–34]. The label-free DNA can be measured with the AgNP-based SERS substrate [35]. However, the poor interaction of AgNPs and DNA makes it virtually impossible to obtain precise information. AuNPs@GO mesh with bio-compatibility and adsorption capacity can improve these shortcomings of AgNPs. Here, DNA was spin-coated with AgNPs onto the surface of the AuNPs@GO mesh after one night of incubation. The SERS spectra with different base sequences (adenine: A, cytosine: C, and thymine: T) are presented in Fig. 6(a). The peaks at 716, 1325, 1442, and 1589 cm⁻¹ are the Raman peaks of adenine. The peaks at 754, 1079, 1252, 1370, and 1663 cm⁻¹ are the Raman peaks of thymine. The peaks at 785, 1002, 1341, 1401, 1563, and 1776cm⁻¹ are the Raman peaks of cytosine. In addition, the intensity of the three base is A > T>C similar to other reported.

 

Fig. 6. (a) The Raman spectrum of DNA with different base (adenine, thymine, cytosine); (b) Raman spectral changes after addition of complementary and non-complementary DNA.

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DNA mutations are an important indicator of disease [36]. However, such assays are difficult with Raman due to the low resolution, poor repeatability, and sample complexity. Here, the SERS spectra of probe DNA (base information is described in detail in Experimental Methods) are shown in Fig. 6(b). Peaks at 700 cm⁻¹ appeared upon adding complementary DNA; the intensity of the original peaks changed. These results can be explained by the process of hybridization in which additional molecules were captured on the surface of the substrate leading to the appearance of vibrational bands from new nucleotides; there was also a Raman shift and intensity changes from the previously probed DNA chain. Furthermore, the Raman peak also shifted versus the Raman peaks measured upon addition of complementary DNA and non-complementary DNA. Molecular rearrangements are the main cause of this phenomenon. These results suggest that the AuNPs@GO mesh@AgNPs can distinguish DNA by SERS with many potential medical applications.

Single molecule detection, has strict requirements on the enhancement and stability of Raman substrates [37]. There are few studies on single-molecule detection using different bases to the best of our knowledge. Here, we demonstrate base pair discrimination via a statistical-based two-layer SERS method down to the single molecule level. Here, we used two closely spaced modes at 716 cm⁻¹ in adenine (A) and 782 cm⁻¹ in cytosine (C) as the effective statistic indicators. Due to their comparable cross-section and energy, these peaks are the ideal indicators. Therefore, we used a mixture of A and C at 10⁻¹³ M. The SERS spectra were collected after immersion of AgNPs in solutions with different bases: A: 10⁻¹³ M; C: 10⁻¹³ M; A and C: 10⁻¹³M. Figure 7(a) shows these Raman data. From top to bottom, the spectra are demonstrated for pure A, pure C, mixed bases, and negative control. To express the Poisson distribution for the single-molecule case, we randomly evaluated 100 different points, and experimented with ten substrates. The histogram of different kinds of spectral bases is plotted in Fig. 7(b). The average number of points of base A is 42, the average number of points of base C is 49, and the average number of points of both is 9. The standard deviation is shown in the histogram. This is a typical Poisson distribution and demonstrated that the Raman signal is mainly from single-molecule contributions; the AuNPs@GO mesh@AgNPs can be used for single molecule detection.

 

Fig. 7. (a) The Raman peaks of adenine, cytosine, a mixed case with adenine and cytosine, and negative control. (b) Histogram of the event distribution.

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4. Conclusion

We fabricated a 3D nanostructure of AuNPs@GO mesh@AgNPs via self-assembly. The excellent SERS activity of the bimetallic structure formed by AuNPs and AgNPs and the biological compatibility of GO mesh were combined in the substrate. The GO mesh provides landing sites for AgNPs and reduces AgNPs aggregation to form the stronger EM enhancement. The detection limit (CV: 10⁻¹⁵ M), stability, and reproducibility were all validated here. Label-free DNA can be discriminated by bases thanks to the Raman enhancement of the AuNPs@GO mesh@AgNPs including single molecule detection of adenine and cytosine. Thus, this technique is an efficient method to form substrates with high Raman enhancement properties. This opens up SERS applications in the field of biomolecule detection.

Appendix

In order to show the plasmon resonance of AuNPs@GO mesh@AgNPs, the optical absorption spectroscopy of AuNPs@GO mesh@AgNPs, AuNPs, AgNPs and GO was shown in Fig. 8. The LSPR peak of AuNPs@GO mesh@AgNPs is located at 488 nm, and a crest of absorbance around 423 nm-550 nm implies that there is a broad absorption region for the structure, well matching with the exciting light ( = 532 nm) in Raman test. In particularly, AuNPs and AgNPs spectrum present absorption peaks centered at 522 and 444 nm. Due to strong interaction with GO and foam Ni, the red-shift to 447 nm of AgNps spectrum and the blue-shift to 518 nm of AuNPs spectrum are observed.

 

Fig. 8. UV-VIS absorption spectrum in different kinds of substrates.

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The reproducibility and stability of AuNPs@GO mesh@AgNPs was measured by using ten samples in our previous experiment. In order to make the results more accurate, we used a statistical method to test the Raman spectra of 10 different positions of 10 samples. The Raman spectra of CV at 910cm⁻¹ was shown in Fig. 9. There is no significant change in the intensity of the Raman peak, showing excellent reproducibility and stability of AuNPs@GO mesh@AgNPs.

 

Fig. 9. Raman spectra of 10 different positions of 10 samples.

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In order to better demonstrate the performance of the substrate, we have added a comparison with several reports in the literature as shown in Table 1, such as RSC Adv., 2015,5, 46552-46557. The SERS performance of this substrate is still outstanding, by contrast with the EF of other substrates.

Table 1. Detection limits and enhancement factors of relevant substrate

View Table

In order to better demonstrate the performance of the substrate, we have added EDS measures in the supporting information as shown in Fig. 10.

 

Fig. 10. EDS measures of AuNPs@GO mesh@AgNPs.

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Equations and methods of FDTD

In order to better understand the simulation results of FDTD, we organized the complete equations and analysis methods.

Each field component is solved on a discrete spatial and temporal grid cell named Yee Cell proposed in 1966, where an electric component is located on the edges of the box and the magnetic component is positioned on the faces. Moreover, FDTD is a time domain technique with E(t) and H(t). Results collected from the FDTD solver are automatically interpolated to the origin of each grid point. We also want to know the field as a function of wavelength, E(λ), or equivalently frequency, E(λ)

FDTD method is used to solve Maxwell's equations in nonmagnetic materials:

(1)$$\frac{{\partial \vec{D}}}{{\partial t}} = \nabla \times \overrightarrow H $$

(2)$$\overrightarrow D (\omega ) = {\varepsilon _0}{\varepsilon _r}\overrightarrow E (\omega )$$

(3)$$\frac{{\partial \overrightarrow H }}{{\partial t}} = - \frac{1}{{{\mu _0}}}\nabla \times \overrightarrow E $$

where H, E, and D are the magnetic, electric, and displacement fields, respectively. ε and µ0 are the complex relative dielectric constant and magnetic permeability coefficient, respectively.

Furthermore, in three dimensions, Maxwell equations have six EM field components. With the assumption that the structure is infinite in the z dimension and that the fields are independent of z, the Maxwell's equations are split into two independent groups of equations that can be solved in the x–y plane only, which results in the transverse electric (TE) and transverse magnetic (TM) equations. Then, we can use the components of E_x, E_y, and H_z to solve TE equations and those of H_x, H_y, and E_z to solve TM equations.

(4)$$\frac{{\partial {E_z}}}{{\partial y}} - \frac{{\partial {E_y}}}{{\partial z}} = - {\mu _0}\frac{{\partial {H_x}}}{{\partial t}}$$

(5)$$\frac{{\partial {E_x}}}{{\partial z}} - \frac{{\partial {E_z}}}{{\partial x}} = - {\mu _0}\frac{{\partial {H_y}}}{{\partial t}}$$

(6)$$\frac{{\partial {E_y}}}{{\partial x}} - \frac{{\partial {E_x}}}{{\partial y}} = - {\mu _0}\frac{{\partial {H_z}}}{{\partial t}}$$

(7)$$\frac{{\partial {H_z}}}{{\partial y}} - \frac{{\partial {H_y}}}{{\partial z}} = \varepsilon \frac{{\partial {E_x}}}{{\partial t}}$$

(8)$$\frac{{\partial {H_x}}}{{\partial z}} - \frac{{\partial {H_z}}}{{\partial x}} = \varepsilon \frac{{\partial {E_y}}}{{\partial t}}$$

(9)$$\frac{{\partial {H_y}}}{{\partial x}} - \frac{{\partial {H_x}}}{{\partial y}} = \varepsilon \frac{{\partial {E_z}}}{{\partial t}}$$

Considering that the function $f(x,\;y,\;z,\;t)$ denotes the electric or magnetic field in the coordinate system, it can be discretized via the central difference approximation in both space and time [45]

(10)$$\frac{{\partial f(x,\;y,\;z,\;t)}}{{\partial x}}|{_{x = i\Delta x}} \approx \frac{{{f^n}(i + 0.5,\;j,\;k) - {f^n}(i - 0.5,\;j,\;k)}}{{\Delta x}}$$

(11)$$\frac{{\partial f(x,\;y,\;z,\;t)}}{{\partial y}}|{_{y = j\Delta y}} \approx \frac{{{f^n}(i,\;j + 0.5,\;k) - {f^n}(i,\;j - 0.5,\;k)}}{{\Delta y}}$$

(12)$$\frac{{\partial f(x,\;y,\;z,\;t)}}{{\partial z}}|{_{z = k\Delta z}} \approx \frac{{{f^n}(i,\;j,\;k + 0.5) - {f^n}(i,\;j,\;k - 0.5)}}{{\Delta z}}$$

(13)$$\frac{{\partial f(x,\;y,\;z,\;t)}}{{\partial t}}|{_{t = n\Delta t}} \approx \frac{{{f^{n + 0.5}}(i,\;j,\;k) - {f^{n - 0.5}}(i,\;j,\;k)}}{{\Delta t}}$$

Funding

National Natural Science Foundation of China (11474187, 11774208, 11874244); Natural Science Foundation of Shandong Province (ZR2016AM19).

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