1. Introduction

Surface-enhanced Raman scattering (SERS) has been widely used to detect trace analyte as an ultrasensitive analytical tool, since its discovery in 1974 [1,2]. On the one hand, SERS can reach single-molecule sensitivity, with optimized SERS substrates [3,4]. On the other hand, it is of importance to detect the quantity of the analyte, especially in some certain fields, such as in vivo monitoring of medicines in sustained-release, in situ detection of contaminants for quality control, and in situ quantitative sensing [3,5]. There are three major problems for the practical applications of SERS quantification, including the non-uniformity of electromagnetic enhancement (EM) induced by non-uniformity of metal-nanostructure substrates [6,7], the orientation fluctuation of molecules due to chemical interactions resulting in the Raman spectral instability [8,9], and the uncertainty of the numbers of analytical molecules [3,10]. Some optimized SERS substrates have been developed to resolve the uniformity of electromagnetic enhancement, for example orderly distributed nano metal structures [11–13], internal standard species implemented to normalize the Raman intensity of the analyte molecule [3,11,14,15]. In order to reduce the Raman spectral instability, some shell-core nanostructures were used by isolating the molecule from the metal surface [9,16]. For the uncertainty of the numbers of analyte molecule, it is more difficult to calculate precisely, actually researchers almost estimated based on the surface rough situation of the SERS substrates, absorption area and volume of detection analyte molecule, under the homogenous-adsorption assumption [3,17].

Carbon nanotube (CNT)/Ag nanoparticles (CNT/AgNPs) composites have shown good characteristics in SERS applications [17]. In this paper, we report CNT/AgNPs as SERS substrate for analyte quantification. The CNT is used as a naturally internal standard for the normalization of the Raman intensity of analyte molecule, and the different enhancement of detection molecule with the same concentration at different CNT/AgNPs substrates can be calibrated. We demonstrated a quantitative detection of rhodamine 6G (R6G) with concentrations from 10⁻⁹ to 10⁻⁷ M and N₂ in natural air.

2. Preparation and method

Carbon nanotube was purchased from Chengdu Organic Chemistry Co. Ltd (Chengdu, China). Rhodamine 6G (R6G) was purchased from Aladdin Company (Shanghai, China). Silver nitrate and other chemical solvents were from Sinopharm Chemical Reagent Co. Ltd (Shanghai, China). All reagents were of analytical grade.

Based on our previous preparation method [18], we prepared CNT/AgNPs hybrids. There are three steps. Firstly, the CNT powders were dispersed in ultra-pure paper to obtain CNT suspension. Then by reduction of AgNO₃ with sodium citrate in the presence of CNT, the CNT/AgNPs solution was synthesized. Finally, in order to remove other impurities, the solution was processed orderly by centrifugation at 4000 r/min for one hour, washing with ultra-pure water, and repeated with three times. The CNT/AgNPs samples were prepared onto SiO₂/Si substrates (area: 5 × 5 mm², thickness of SiO₂: ~300 nm, thickness of Si: ~525 μm), with a simple dip method of CNT/AgNPs solution. The analyte interacts with the substrate via a van der Waals interaction.

All Raman signals were collected using a Raman spectrometer (Horiba Jobin Yvon LabRAM HR Evolution), with the incident light of 532 nm, power of 5 mW at room temperature. An integration time of 2 s was used in the R6G measurements to reduce the heating effect induced by laser. Because of low concentration of N₂, the integration time of 200 s was used in gas detection.

3. Experiments and discussion

3.1 Self-calibration method

Figure 1 shows the scheme of the CNT/AgNPs for SERS quantification. AgNPs generate localized electromagnetic field enhancement. While AgNPs also produce a random distribution of hot spots. There are variations in the detected Raman signals across the same sample, due to both the disproportionate distribution of hot spots and fluctuations in optical setup.

 

Fig. 1 Normalization procedure for analyte detection using Raman spectroscopy. The spectral data show the Raman peaks of the analyte and CNT. The intensity of analyte and CNT is used to calculate the ratio k (I_Analyte/I_CNT), where N_Analyte and N_CNT is the number of analyte molecule and CNT, α_Analyte and α_CNT is the Raman scattering polarizability of analyte molecules and CNT, I₀(ω₀) is the incident light intensity of the laser at frequency ω₀.

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During the SERS measurement, we thought all of the parameters except N_Analyte (number of analyte molecules) remain constant. The Raman signals of the analyte (I_Analyte) and CNT (I_CNT) are simultaneously collected, and I_Analyte is proportional to I_CNT due to EM. The instrumental itself drift and the variation of I_Analyte due to the random distribution of electromagnetic hot spots can be calibrated, by calculating the relative parameter k (the ratio of I_Analyte and I_CNT). We chose the 2D Raman peak of CNT as an internal standard due to three factors. Firstly, the 2D peak of CNT at ~2673 cm⁻¹ is far from the typical window of commonly used dye molecules. R6G has some main Raman feature between 1300 to 1650 cm⁻¹, and it is probably affected by the background of the CNT vibrations at ~1350 cm⁻¹ (D mode) and ~1600 cm⁻¹ (G mode). Secondly, the D mode is also induced by defects. In addition, the intensity of 2D peak of CNT is comparatively strong. Note that: the chemical enhancement (CM) is not considered in this study because the electromagnetic enhancement is typical dominant one in SERS.

3.2 Detection of R6G

Figure 2(a) illustrates five Raman spectra from 10⁻⁷ M R6G, and there are differences in the Raman intensity between each spectrum despite being collected from the same sample. Relying on a limited number of Raman spectra to extract quantitative information from the system, it is insufficient for the calculation. Instead, we perform a Raman mapping procedure on an area of 100 μm by 100 μm. We calculate the normalization ratio k for each Raman spectrum within the mapping area, and then plot the probability density function. The probability density function of the ratio k (I_@613/I_@2D) is shown in Fig. 2(b), in which a lognormal distribution is shown to be a good fit. To confirm the appropriateness of the lognormal distribution fit, we plot the theoretical vs. empirical probabilities (Fig. 2(c)), which shows a good fit of the lognormal distribution for the probability distribution of the normalization ratio k. The corresponding probability density function of the ratio k when R6G with concentration of 10⁻⁸ M and 10⁻⁹ M is shown in Figs. 2(d) and 2(e), respectively. They all have a goodness of fit with a lognormal distribution.

 

Fig. 2 (a). Representative 5 results of SERS quantification measurements of R6G (concentration of 10⁻⁷ M) using CNT/AgNPs as SERS substrates, with the C-C-C bond vibration mode (~613 cm⁻¹), out-of-plane bending motion of C-H bond mode (~773 cm⁻¹), and 2D peak of CNT (~2673 cm⁻¹). (b) Probability density function of the ratio k (data at ~613 cm⁻¹ used), showing the good lognormal distribution with a lognormal median at k = 2.114. (c) Probability-probability plot visually demonstrating the goodness of fit with a lognormal distribution (red line, theoretical fit; black dots, our experimental data). The corresponding probability density function of the ratio k (data at ~613 cm⁻¹ used) when R6G with concentration of (d) 10⁻⁸ M and (e) 10⁻⁹ M using the same Raman mapping measurement.

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Figure 3(a) demonstrates the Raman mapping intensity of R6G (with concentration of 10⁻⁷ M) absorbed on CNT/AgNPs. The Raman signals of R6G and the CNT fluctuated simultaneously. The non-uniform distribution of Raman intensity indicates that the enhancement factor across the sample is different. But, if we normalize the peak intensity of R6G to that of CNT, the signal becomes more stable. Figures 3(c) and 3(d) show the relative standard deviation (RSD) of the peak intensity at ~613 cm⁻¹ and the calculated ratio k, respectively. The RSD (at ~613 cm⁻¹) was reduced from 53% to 27%. We also calculated the RSD (at ~773 cm-1), reduced from 54% to 28%.

 

Fig. 3 (a) Raman mapping results of R6G with concentration of 10⁻⁷ M; (b) three random Raman mapping areas for R6G with concentration of 10⁻⁹ M; the corresponding calculated RSD of (c) Raman intensity at ~613 cm⁻¹, and (d) the normalized ratio k, when R6G with concentration of 10⁻⁷ M; (e) the averaged Raman signals of R6G with concentration of 10⁻⁷, 10⁻⁸, and 10⁻⁹ M; (f) the averaged Raman signals of R6G (10⁻⁹ M) in three random Raman mapping areas.

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Figure 3(e) shows the averaged spectra of R6G with concentration of 10⁻⁹ to 10⁻⁷ M. It can be seen that the averaged intensity of R6G increased with the increase of concentration. The calculated normalization ratio k (at ~613 cm⁻¹) is 0.755, 1.116, 2.114 for 10⁻⁹, 10⁻⁸ and 10⁻⁷ M, respectively. While the peak intensity of CNT fluctuated slightly, which could be different focus status on different CNT/AgNPs and probably different distribution of hot spots. Meanwhile, the corresponding RSD values of the ratio k (R6G of 10⁻⁸ and 10⁻⁹ M) are also calculated, reduced to half of that of the Raman intensity.

Figure 3(b) demonstrates three random mapping areas used in our Raman mapping for R6G with concentration of 10⁻⁹ M, in order to investigate the self -calibration method applied in random big areas. The averaged SERS spectra of R6G in three random areas are shown in Fig. 3(f). The corresponding normalized ratio k is 0.736, 0.759 and 0.759, respectively. Meanwhile, the whole averaged spectra in all areas are calculated and given with pink line (k = 0.755).

3.3 Detection of N₂

Figure 4(a) demonstrates the representative 5 results of SERS quantification using five CNT/AgNPs samples. Due to the natural same concentration of N₂ in air and its comparative uniformity absorbed on the SERS substrates, the major difference of samples is the enhancement factor induced by different distribution of hot spots. So, we investigate our self-calibration method by detecting N₂. The peak position of N₂ is ~2328 cm⁻¹. The Raman intensity of N₂ changes from 192 to 240 counts, and the corresponding calculated ratio k fluctuates from 0.012 to 0.020, almost 25% change. We noticed that the intensity of 2D mode (of single-wall CNT) is much larger compared with that of N₂. So, we also prepared multi-walls CNTs/AgNPs composite as SERS substrate with the same method. The result is shown in Fig. 4(b), the Raman intensity at ~2328 cm⁻¹ changes from 224 to 303 counts, and there are also ~15% change for the same N₂ concentration. However, the calculated ratio k fluctuates from 0.11 to 0.13, almost ~8% change, which implies the self-calibration method with CNTs as an internal standard can largely reduce the fluctuation of Raman intensity induced by different enhancement factor of SERS substrates and the internal optical setup.

 

Fig. 4 Representative 5 results of SERS quantification measurements of N₂ with (a) CNT/AgNPs and (b) multi-walls carbon nanotubes (CNTs)/AgNPs as SERS substrates.

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Although the CNT can be as an internal standard to investigate the SERS quantification, but the RSD values of the normalized ratio k are still a little big, we discuss as follows.

4. Conclusion

In conclusion, we have developed a CNT/AgNPs SERS substrate for quantification analysis. Carbon nanotube plays an internal standard. We also show the feasibility of quantification of R6G molecules with concentration ranging from 10⁻⁹ to 10⁻⁷ M. Moreover, natural air detection with our CNT/AgNPs further indicates that such self-calibration method with 2D peak of carbon nanotube can be functional to reliable SERS quantification. However certain challenges still remain, the chemical interaction of molecules and the substrates, the distribution of hot spots and the competitive adsorption of molecules and internal standard species should be further taken consideration.