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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 3 — Feb. 11, 2013
  • pp: 2987–3000
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Fast statistical measurement of aspect ratio distribution of gold nanorod ensembles by optical extinction spectroscopy

Ninghan Xu, Benfeng Bai, Qiaofeng Tan, and Guofan Jin  »View Author Affiliations


Optics Express, Vol. 21, Issue 3, pp. 2987-3000 (2013)
http://dx.doi.org/10.1364/OE.21.002987


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Abstract

Fast and accurate geometric characterization and metrology of noble metal nanoparticles such as gold nanorod (NR) ensembles is highly demanded in practical production, trade, and application of nanoparticles. Traditional imaging methods such as transmission electron microscopy (TEM) need to measure a sufficiently large number of nanoparticles individually in order to characterize a nanoparticle ensemble statistically, which are time-consuming and costly, though accurate enough. In this work, we present the use of optical extinction spectroscopy (OES) to fast measure the aspect ratio distribution (which is a critical geometric parameter) of gold NR ensembles statistically. By comparing with the TEM results experimentally, it is shown that the mean aspect ratio obtained by the OES method coincides with that of the TEM method well if the other NR structural parameters are reasonably pre-determined, while the OES method is much faster and of more statistical significance. Furthermore, the influences of these NR structural parameters on the measurement results are thoroughly analyzed and the possible measures to improve the accuracy of solving the ill-posed inverse scattering problem are discussed. By using the OES method, it is also possible to determine the mass-volume concentration of NRs, which is helpful for improving the solution of the inverse scattering problem while is unable to be obtained by the TEM method.

© 2013 OSA

1. Introduction

So far, the most commonly used dimensional metrological methods for metal NPs are microscopic imaging methods such as transmission electron microscopy (TEM), scanning electron microscopy, and scanning probe microscopy. These methods, though with high precision, can only measure individual NPs or a small number of NPs locally. Hence, these methods are slow and costly when they are used to measure large amount of NPs (or the so-called NP ensembles). In addition, when the NPs are prepared for microscopic measurement, the NPs may aggregate strongly after the NP colloid is coated on a substrate and the solvent is evaporated, which is disadvantageous for accurate characterization of the NP geometry. Furthermore, for NPs with non-uniform geometries (i.e., the so-called polydisperse NP ensembles), it is often needed to characterize the distribution function of some geometric parameters of the NPs statistically, which is obviously hard to do with the microscopic methods due to the required large amount of sampling NPs. To achieve this goal, some methods based on scatterometry (i.e., the technique of retrieving the geometrical parameters of NPs from their scattering spectra) have been proposed, such as optical extinction spectroscopy (OES), small-angle x-ray scattering method, and dynamic light scattering (DLS) method. Among these methods, the DLS method is probably the most widely used one because of its versatility of measuring various materials of NPs. However, since the DLS method measures the hydrodynamic size of NPs in a liquid environment by detecting and analyzing the Brownian motion of the NPs, it can only give the equivalent spherical diameter of the measured NPs no matter what practical shape the NPs may have. Therefore, it cannot measure the shape of the NPs. For the characterization of non-spherical NPs, some other scatterometric methods such as the OES method have to be developed.

2. Theoretical method

The objective of the OES method is to retrieve the geometric parameters of the gold NRs from the measured extinction spectra. It is in essence an inverse scattering problem, which is solved by using the methods below.

2.1. Calculation of the extinction cross section of a single NR

Fig. 1 Geometric model of the NR. Several NRs with the same width D and aspect ratio AR but different end-cap factor e are demonstrated.

The extinction cross section Cext is defined as the ratio of the radiant power being extinct by a particle to the radiant power incident on the particle in the process of scattering [8

8. C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles (Wiley, 1983).

]. To rigorously calculate Cext of a single NR or an ensemble of randomly oriented discrete gold NRs in a monodisperse system, some numerical methods such as the T-matrix method [10

10. M. I. Mishchenko, L. D. Travis, and A. A. Lacis, Scattering, Absorption, and Emission of Light by Small Particles (Cambridge University Press, 2002).

] can be used. The T-matrix method, a rigorous semi-analytical method, is used in our simulation because it is much faster for modeling randomly oriented NR ensembles than the other methods [10

10. M. I. Mishchenko, L. D. Travis, and A. A. Lacis, Scattering, Absorption, and Emission of Light by Small Particles (Cambridge University Press, 2002).

, 11

11. B. Khlebtsov, V. Khanadeev, T. Pylaev, and N. Khlebtsov, “A new t-matrix solvable model for nanorods: Tem-based ensemble simulations supported by experiments,” J. Phys. Chem. C 115, 6317–6323 (2011). [CrossRef]

].

2.2. Calculation of the absorbance of NR ensembles

Obviously, Eq. (4) is valid only for a monodisperse system. If the NR ensemble is polydisperse, the measured absorbance spectrum is the superposition of the absorbance spectra of the composing NRs with different sizes and shapes [22

22. S. Eustis and M. A. El-Sayed, “Determination of the aspect ratio statistical distribution of gold nanorods in solution from a theoretical fit of the observed inhomogeneously broadened longitudinal plasmon resonance absorption spectrum,” J. Appl. Phys. 100, 044324 (2006). [CrossRef]

]. Therefore, for a polydisperse NR ensemble, the total absorbance A is calculated by integrating the contribution from the composing NRs according to the PDF p(D,AR,e) as [10

10. M. I. Mishchenko, L. D. Travis, and A. A. Lacis, Scattering, Absorption, and Emission of Light by Small Particles (Cambridge University Press, 2002).

]:
A(λ,D,AR,e)=lNvln10DminDmaxARminARmaxeminemaxp(D,AR,e)Cext(λ,D,AR,e)dDdARde.
(5)

2.3. Solution of the inverse scattering problem

The discretization of Eq. (6) results in the following system of linear algebraic equations:
A=CP,
(7)
where A and P are M × 1 and N × 1 vectors, respectively, and C is a M × N matrix. The vector A contains the measured extinction values at different λm and the matrix C consists of the calculated extinction cross sections Cext for NRs with each pair of λm and ARn. The vector P is the PDF to be solved. Their specific expressions are shown below
A=[A(λ1)A(λ2)A(λm)A(λM)]T,m=1,2,,M,
(8)
P=ΔAR[p(AR1)p(AR2)p(ARn)p(ARN)]T,n=1,2,,N,
(9)
Cmn=lNvln10[Cext(λm,D¯,ARn,e¯)],
(10)
ΔAR=ARmaxARminN,
(11)
m and n are integers, and the superscript T means the transpose of the vectors. Here we consider M > N so that Eq. (7) is an overdetermined system with N unknowns. p(ARn) has two physical constraints: the non-negativity constraint p(ARn) ≥ 0 and the standard normalization condition ∑np(ARn) = 1.

Since the discretized matrix Cmn is usually ill-conditioned, the inverse problem formulated in terms of Eq. (6) is ill-posed [32

32. P. C. Hansen, “Regularization tools: A matlab package for analysis and solution of discrete ill-posed problems,” NUMER ALGORITHMS 6, 1–35 (1994). [CrossRef]

]. In order to find a unique and accurate solution of the inverse problem, one of the commonly used numerical techniques is Tikhonov regularization [33

33. J. Mroczka and D. Szczuczynski, “Improved regularized solution of the inverse problem in turbidimetric measurements,” Appl. Opt. 49, 4591–4603 (2010). [CrossRef] [PubMed]

]. Here we just briefly summarize the process of Tikhonov regularization in our problem and more details can be found in Refs. [32

32. P. C. Hansen, “Regularization tools: A matlab package for analysis and solution of discrete ill-posed problems,” NUMER ALGORITHMS 6, 1–35 (1994). [CrossRef]

35

35. J. Mroczka and D. Szczuczynski, “Simulation research on improved regularized solution of the inverse problem in spectral extinction measurements,” Appl. Opt. 51, 1715–1723 (2012). [CrossRef] [PubMed]

].

The regularized least-squared solution PRLS of Eq. (7) is given as [32

32. P. C. Hansen, “Regularization tools: A matlab package for analysis and solution of discrete ill-posed problems,” NUMER ALGORITHMS 6, 1–35 (1994). [CrossRef]

]:
PRLS=min{ACP22+γ2L(PP*)22},
(12)
where ‖ · ‖2 is the Euclidean norm, γ is the regularization factor, P* is an assumed a priori assumed solution (taken as P* = 0 here), L is typically either an identity matrix (as we take here) or a discrete approximation of the derivative operator [32

32. P. C. Hansen, “Regularization tools: A matlab package for analysis and solution of discrete ill-posed problems,” NUMER ALGORITHMS 6, 1–35 (1994). [CrossRef]

]. Eq. (12) can be written in another equivalent form as follows [33

33. J. Mroczka and D. Szczuczynski, “Improved regularized solution of the inverse problem in turbidimetric measurements,” Appl. Opt. 49, 4591–4603 (2010). [CrossRef] [PubMed]

]:
PRLS=min{12PTQP+qTP},
(13)
where Q = 2(CTC +γ2LTL) is a symmetrical matrix of size N × N, and q = −2CTA is a N-dimensional column vector. We use the active set method [34

34. P. Gill, W. Murray, and M. Wright, Numerical Linear Algebra and Optimization (Addison Wesley, 1991).

] to find the solution of Eq. (13). A mean square error (mse) defined below is used to evaluate the quality of the solution:
mse=1Mm[A(λm)Acal(λm)A(λm)]2,m=1,2,,M,
(14)
where Acal = CPRLS is the fitted optimal solution of the absorbance. We set the criteria that if mse is smaller than 1 × 10−3, the solution PRLS is acceptable.

3. Experiment and results

We applied the OES method to measure the ARD p(AR) of gold NR ensemble samples and compared the results with those directly obtained by the TEM method. 30 samples of gold NR ensembles were measured and analyzed, in which each sample contains approximately 1010 NRs per millilitre. Here, without loss of generality, the results of three samples with different D, AR and e are demonstrated. The three samples designated as NR-40-700, NR-20-700, and NR-10-750 were obtained from NanoSeedz Ltd., which have the nominal width D of 40 nm, 20 nm, and 10 nm and the expected L-LSPR wavelengthes of 700 nm, 700 nm, and 750 nm, respectively.

In the OES measurement, a UV-VIS spectrophotometer (PekinElmer LAMBDA 950) was used to measure the extinction spectra of the samples. For each sample, the measurement was repeated six times in one hour and the average value is used. The measurement range of wavelength λ was 400 nm – 1000 nm and the step was taken as 1 nm. The most time-consuming process of the OES method is to prepare the extinction spectra database [corresponding to the matrix C in Eq. (7)] of the gold NRs with different values of the width, the aspect ratio and the end-cap factor D, AR, and e. Fortunately, the database just need to be calculated once. By using a dual-core 2.13GHz Intel Xeon CPU with 80Gb RAM, it takes about 12 seconds to calculate a single extinction spectrum of NRs in the wavelength range of the 400 nm – 1000 nm, with a 1 nm spectral resolution, and a relative calculation accuracy of better than 1%. Based on the measurement data, the optimization process described above was implemented to retrieve the ARD p(AR) of the samples, where AR was discretized in the range of 1 to 5, with a step of 0.1. The inverse algorithm was run on a 3.00GHz Intel Core2 Duo CPU with 4Gb RAM and the average time consumption is about 0.25 seconds for a single measured spectra.

In the TEM experiment, a transmission electron microscope (Hitachi H-7650B) was used to get the images of the NRs. For each of the three samples, ten TEM images were taken. Therefore, we analyzed altogether 788, 896, and 804 NRs in samples NR-400-700, NR-20-700, and NR-10-750, respectively to get the mean width , the mean end-cap factor ē and the ARD p(AR).

The TEM images of the three samples as well as their extinction spectra measured by the OES method are shown in Fig. 2. In Figs. 2(a)–(c), it is clearly seen that the gold NR ensembles are polydisperse. In Fig. 2(d), the measured L-LSPR extinction peaks of the three samples are very close to their nominal values. NR-40-700 (red line) and NR-20-700 (black line) have the same resonance wavelength but different linewidths, i.e., the full width at half maximum (FWHM) of the resonance peak, because of their different and dispersancy.

Fig. 2 TEM images of the three gold NR ensemble samples: (a) NR-40-700, (b) NR-20-700, (c) NR-10-750. (d) Experimentally measured extinction spectra (dots) of the samples as well as the corresponding numerically reproduced extinction spectra (lines) according to the retrieved ARD functions p(AR) based on the OES results.

The AR distributions p(AR) of the samples were retrieved from the OES results, with the procedure presented in Section 2. In Fig. 3, the red columns and curves are the retrieved results obtained by the OES method while the black ones are the results obtained by TEM. We use a sum of Gaussian functions to fit the discrete results:
p(AR)=iwiσi,AR2πexp[(ARAR¯i)22σi,AR2],
(15)
where AR¯i and σi,AR are the mean value and standard deviation of the ith Gaussian function p(AR), respectively. The constant wi was chosen such that p(AR) satisfies the standard normalization condition ∑np(ARn) = 1.

Fig. 3 Comparison of the measured AR distribution functions of three gold NR ensemble samples obtained by the OES method (red) and those obtained by the TEM method (black). In each subfigure, both the discrete AR distribution and a Gaussian fit of it are given. The values in parentheses ( AR¯, σAR) give the mean AR and the standard deviation of the PDF obtained by the two methods.

Furthermore, with these retrieved AR distribution functions p(AR), the extinction spectra of the three samples were numerically reproduced by Eq. (6), as shown in Fig. 2(d), which coincide with the measured extinction spectra quite well. Therefore, both Fig. 3 and Fig. 2(d) show that the retrieved results by the OES method are reliable in our characterization.

By comparing the OES results with those obtained by the TEM method in detail, it is seen that the mean AR values derived by the two methods coincide with each other well, with their relative difference as 0.70%, 0.26% and 1.10% for samples NR-40-700, NR-20-700, and NR-10-750, respectively. The relative difference here is calculated by (AR¯OESAR¯TEM)/AR¯OES. It is worth noting that in Figs. 3(b) and 3(c), the OES results show significant ARD between 1 and 1.5, while the TEM results have few NPs in this range. The main reason is that in the counting process of the TEM method, we ignored most byproducts (such as spheres, cubes etc.) in the samples, as shown in the TEM images of Figs. 2(b) and 2(c). However, these byproducts also contribute to the extinction spectra and thus can be detected by the OES method. The standard deviation of the OES results (σOES,AR = 0.144, 0.221, and 0.306) are smaller than those of the TEM results (σTEM,AR = 0.267, 0.345, and 0.364), with their relative difference as 46.1%, 35.9%, and 15.9% for the three samples. The possible reason is that many factors such as the deviation between the real shape of the gold NRs and our calculation shape model and the correction method of the dielectric function could influence the extinction spectra and thus also influence the retrieved ARD. Therefore, we proceed to discuss the influences by these parameters.

4. Discussion: influences by the other structural parameters

In the AR retrieval process of the OES method, the mean width values OES that we adopted are 46.0 nm, 20.0 nm, and 22.0 nm for samples NR-40-700, NR-20-700,and NR-10-750, respectively, but not the nominal values, so as to obtain the best-fit results. By TEM imaging, the measured mean width and the standard deviation (TEM ± σTEM,D) of the three NR ensemble samples are (47.0±4.1 nm), (19.5±3.0 nm), and (18.7±1.9 nm), which are also different from the nominal values but close to our OES results. This, on the other hand, shows that our OES measurement results are reliable. For the end-cap eccentricity, the best-fit values of ēOES that we used in the OES method are 0.9, 0.6, and 0.3 for the three samples, while the corresponding TEM measurement results ēTEM are 0.8, 0.6, and 0.4, respectively. The two sets of end-cap eccentricity values also coincide with each other relatively well (where the small difference may be owing to the in-sufficient sampling in the TEM method).

However, from another point of view, these calculations show that the selection of the width and the end-cap factor ē is important in the retrieval process, although and ē are considered to affect the LSPR response of the gold NR ensembles weakly at the beginning [14

14. S. W. Prescott and P. Mulvaney, “Gold nanorod extinction spectra,” J. Appl. Phys. 99, 123504 (2006). [CrossRef]

]. In the following, we detailedly analyze the influences of and ē on the retrieval results. We analyze the value of mean width ranged from 5 nm to 50nm with a step 5 nm and the mean end-cap factor ē ranged from 0 to 1 with a step of 0.1. Without loss of generality, we choose sample NR-20-700 in the following analysis.

4.1. Influence by the selection of mean width D̄

Here, the NRs have a fixed end-cap eccentricity ē = 0.6 and the mean width is varied. In the retrieval calculation, ten different values of were selected to solve the inverse problem. The obtained mean square error mse values are summarized in Fig. 4(b). It shows that four of them (for 10 ≤ ≤ 25 nm) are acceptable with mse ≤ 1 × 10−3 while the others (for ≥ 30 nm or ≤ 5 nm) are unacceptable.

Fig. 4 (a) Comparison of the retrieved ARD p(AR) obtained by the OES method using different assumed mean width and the p(AR) directly measured by the TEM method. (b) Dependence of the mean square error mse on the assumed mean width .

Figure 4(a) compares the retrieved aspect ratio distribution p(AR) obtained by the OES method using five different assumed mean width with the p(AR) directly measured by the TEM method. It is seen that when increases from 10 nm to 30 nm, the retrieved p(AR) is left shifted and the FWHM decreases. By linear fitting, we find that the shift bears a linear relation with respect to , as shown in Fig. 5(a),
AR¯=3.010.0176D¯(R2=0.9971)
(16)
where R2 means the coefficient of determination of the linear fit. According to Eq. (16), we know that a change of 10 nm in leads to around 7% change in the retrieved AR¯.

Fig. 5 (a) Dependence of the retrieved mean aspect ratio AR¯ and the standard deviation σ on the assumed mean width for sample NR-20-700 with assumed ē = 0.6. (b)Dependence of the number of NRs per unit volume Nv and the mass-volume concentration Cg of NRs on the assumed mean width .

In addition, the standard deviation σAR of the retrieved p(AR) also decreases with the increase of , as shown in Fig. 5(a). These are owing to the ill-posedness of the inverse problem. Therefore, if we want to get accurate solution of p(AR) without knowing the value of , some other a priori information about the gold NR ensembles should be determined beforehand.

In Fig. 5 (b), we calculated the dependence of the number of NRs per unit volume Nv and the mass-volume concentration Cg of NRs on the mean width . Here, Nv can be obtained by the optimization progress described in Section 2 and the mass-volume concentration Cg is derived as Cg = ρNvVn · PRLS, where Vn is a row vector consisting of the volume of each nanorod of ARn and ρ is the density of bulk gold. Since Nv has an evident dependence on the mean width , as shown in Fig. 5(b), it can be used as a priori information for the retrieval process. In contrast, the value of Cg (= 22.45 ± 0.07 μg/ml) changes only a little with respect to the change of when ≤ 30 nm. Thus it is not suitable to act as a priori information for the determination of the mean width.

4.2. Influence by the mean end-cap eccentricity ē

To study the influence by the mean end-cap factor ē, we fix the mean width value = 20 nm and vary ē. In the calculation, 11 different values of ē were adopted to solve the inverse problem and the obtained mse values are summarized in Fig. 6(b). It is seen that eight of them (for 0.3 ≤ ē ≤ 1) are acceptable and the others are unacceptable. Figure 6(a) shows the comparison of the retrieved ARD p(AR) obtained by the OES method using eight assumed mean end-cap eccentricities ē and the measured p(AR) obtained by the TEM method. With the increase of ē from 0.3 to 1, the retrieved p(AR) has a right shift while the FWHM only changes a little. The shift also bears a linear relation with respect to ē, following the fitted equation:
AR¯=2.404+0.43e¯(R2=0.9941).
(17)

Fig. 6 (a) Comparison of the retrieved ARD p(AR) obtained by the OES method using different assumed mean end-cap eccentricity ē and the measured p(AR) obtained by the TEM method. (b) Dependence of the mse on the assumed ē.

Similarly as the influence by , the fitted AR¯ is affected by the change of ē evidently. According to Eq. (17), a change of ē by 0.1 may lead to the change of AR¯ by 1.6%. It means that the retrieved PDF is dependent not only on the value of mean width , but also on the value of ē. However, different from the influence by , in this case the standard deviation σ and the number of NRs per unit volume Nv only depend on ē slightly, as shown in Fig. 7. Although Nv is also dependent on the end-cap factor ē, the relativity is much smaller, compared with the influence of . Thus it is difficult to use Nv as a priori information to determine the mean end-cap factor ē. Meanwhile the value of the mass-volume concentration can also be obtained (as Cg = 22.39 ± 0.26 μg/ml), which changes only a little with respect to ē and the value coincides well with Cg = 22.45 ± 0.07 μg/ml obtained in subsection 4.1. Therefore, we can conclude that the mass-volume concentration Cg of the gold NR ensembles can be determined accurately by the OES method, without knowing the other structrual parameters (ē and ) beforehand.

Fig. 7 (a) Dependence of the retrieved mean aspect ratio AR¯ and the standard deviation σ on the assumed mean end-cap eccentricity ē for sample NR-20-700 with assumed = 20 nm. (b) Dependence of the number of NRs per unit volume Nv and the mass-volume concentration Cg of NRs on the assumed mean end-cap eccentricity ē.

4.3. Influence by the polydispersity of the width D and end-cap eccentricity e

So far, we have been always considering the fixed values of and ē. In this subsection, we analyze the influence by the polydispersity of the width D and end-cap eccentricity e, i.e, σD and σe that are defined as the standard deviations of the PDFs of D and e, respectively. The retrieved p(AR) was obtained by integrating the retrieval results with respect to each discrete pair of D and e.

Figure 8 shows the retrieved ARD p(AR) obtained by the OES method, by taking different σD and σe. The mse is always smaller than 1 × 10−3 so that the retrieved results are acceptable. It is seen that the changes of the mean aspect ratio AR¯ and standard deviations σAR of p(AR) are around 1% when σD and σe are significantly increased from 0 to 5.4 and to 0.143m, respectively. This shows that the influences by the polydispersity σD and σe are pretty small and can be ignored, compared with the influences by the mean width and the mean end cap ē.

Fig. 8 Comparison of the retrieved p(AR) obtained by the OES method by assuming (a) different polydispersities of the width D and a fixed e = 0.6, and (b) different polydispersities of the end-cap eccentricity e and a fixed D = 20 nm.

4.4. Influence by the surface electron scattering constant As

To study the influence by the surface electron scattering constant As, we fix = 20 nm and ē = 0.6. In the calculation, six different values of As were used to solve the inverse problem and the obtained mse values are summarized in Fig. 9(b). It is seen that when As increases from 0.3 to 1.3, the values for mse also increases. The range of the acceptable values (0.3 ≤ As ≤ 0.6) are consistent well with the measurement values determine by Ref. [11

11. B. Khlebtsov, V. Khanadeev, T. Pylaev, and N. Khlebtsov, “A new t-matrix solvable model for nanorods: Tem-based ensemble simulations supported by experiments,” J. Phys. Chem. C 115, 6317–6323 (2011). [CrossRef]

, 30

30. C. Novo, D. Gomez, J. Perez-Juste, Z. Zhang, H. Petrova, M. Reismann, P. Mulvaney, and G. V. Hartland, “Contributions from radiation damping and surface scattering to the linewidth of the longitudinal plasmon band of gold nanorods: a single particle study,” Phys. Chem. Chem. Phys. 8, 3540–3546 (2006). [CrossRef] [PubMed]

].

Fig. 9 (a) Comparison of the retrieved ARD p(AR) by the OES method using different surface electron scattering constant As. (b) Dependence of the mse on the As.

Figure 9(a) shows the retrieved ARD p(AR) by the OES method, by taking different As. It is seen that when As is significantly increased from 0.3 to 1.3, the mean aspect ratio AR¯ only changes negligibly (smaller than 1%) while the standard deviation σAR decreases by 20%. The decrease of σAR can be explained by Eq. (1): the increase of As increases the damping constant γ, which leads to the broadening of the L-LSPR [30

30. C. Novo, D. Gomez, J. Perez-Juste, Z. Zhang, H. Petrova, M. Reismann, P. Mulvaney, and G. V. Hartland, “Contributions from radiation damping and surface scattering to the linewidth of the longitudinal plasmon band of gold nanorods: a single particle study,” Phys. Chem. Chem. Phys. 8, 3540–3546 (2006). [CrossRef] [PubMed]

]. Thus the collection of these spectra would be broadened and the σAR of the retrieved ARD would decrease. It is worth noting that only the gold NRs narrower than ∼20 nm may have broader resonance due to the surface scattering [30

30. C. Novo, D. Gomez, J. Perez-Juste, Z. Zhang, H. Petrova, M. Reismann, P. Mulvaney, and G. V. Hartland, “Contributions from radiation damping and surface scattering to the linewidth of the longitudinal plasmon band of gold nanorods: a single particle study,” Phys. Chem. Chem. Phys. 8, 3540–3546 (2006). [CrossRef] [PubMed]

, 36

36. B. N. Khlebtsov and N. G. Khlebtsov, “Multipole plasmons in metal nanorods: Scaling properties and dependence on particle size, shape, orientation, and dielectric environment,” J. Phys. Chem. C 111, 11516–11527 (2007). [CrossRef]

]. However, most of our calculations were performed for NRs with width and lengths exceeding 20 nm, so that the influences by As are pretty small (compared with the influences by and ē) and can also be ignored.

5. Conclusions

We have studied the use of the OES method to measure the ARD of polydisperse gold NR ensembles statistically. To solve the inverse scattering problem, the extinction of the polydisperse NR ensemble is modeled rigorously by the T-matrix method and the AR parameter retrieval is performed by an optimization process with data fitting to the measured extinction spectra. We have shown that, for different NR samples that we have prepared, the retrieved PDF results coincide well with those obtained by the TEM method. The comparison results indicate that the OES method is fast, cost effective, and accurate enough if the mean width and end-cap shape ē of the NR ensembles are reasonably assumed or pre-determined. Furthermore, the Cg of NRs can also be measured by the OES method, which is useful for improving the solution of the inverse problem while cannot be obtained by the imaging methods.

Detailed analyses of the influences of NR parameters on the retrieval results have shown that the measured mean aspect ratio AR¯ depends on the assumed mean width and mean end-cap factor ē linearly. A change of 10 nm in may lead to around 7% change in the retrieved AR¯ and a change of ē by 0.1 may lead to the change of AR¯ by 1.6%. The influences by the polydispersity σD and σe, however, are pretty small and can be ignored. For gold NRs with the width larger than ∼20 nm, the influence by the surface electron scattering constant As is also very small and can be ignored. Based on the analyses, we suggest that the measurement accuracy can be further improved if some a priori information of the NRs can be obtained beforehand. A good guess of the mean width can be obtained by measuring the number of NRs per unit volume Nv, which can be achieved by the OES method itself. To get a good guess of the end-cap shape ē, some auxiliary measurements (such as scattering cross section measurements, and polarization- or incident-angle-dependent scattering measurements of NRs) could be taken, which are the tasks of our further work.

Acknowledgments

We acknowledge the support by the Ministry of Science and Technology of China (Project No. 2011BAK15B03) and the Natural Science Foundation of China (Project No. 61161130005).

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C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles (Wiley, 1983).

9.

S. Link and M. A. El-Sayed, “Simulation of the optical absorption spectra of gold nanorods as a function of their aspect ratio and the effect of the medium dielectric constant,” J. Phys. Chem. B 109, 10531C10532 (2005). [CrossRef]

10.

M. I. Mishchenko, L. D. Travis, and A. A. Lacis, Scattering, Absorption, and Emission of Light by Small Particles (Cambridge University Press, 2002).

11.

B. Khlebtsov, V. Khanadeev, T. Pylaev, and N. Khlebtsov, “A new t-matrix solvable model for nanorods: Tem-based ensemble simulations supported by experiments,” J. Phys. Chem. C 115, 6317–6323 (2011). [CrossRef]

12.

B. T. Draine and P. J. Flatau, “Discrete-dipole approximation for scattering calculations,” J. Opt. Soc. Am. A 11, 1491–1499 (1994). [CrossRef]

13.

V. L. Y. Loke and M. P. Mengüç, “Surface waves and atomic force microscope probe-particle near-field coupling: discrete dipole approximation with surface interaction,” J. Opt. Soc. Am. A 27, 2293–2303 (2010). [CrossRef]

14.

S. W. Prescott and P. Mulvaney, “Gold nanorod extinction spectra,” J. Appl. Phys. 99, 123504 (2006). [CrossRef]

15.

W. Yanpeng and N. Peter, “Finite-difference time-domain modeling of the optical properties of nanoparticles near dielectric substrates,” J. Phys. Chem. C 114, 7302–7307 (2010). [CrossRef]

16.

U. Hohenester and J. Krenn, “Surface plasmon resonances of single and coupled metallic nanoparticles: A boundary integral method approach,” Phys. Rev. B 72, 195429 (2005). [CrossRef]

17.

V. Myroshnychenko, J. Rodriguez-Fernandez, I. Pastoriza-Santos, A. M. Funston, C. Novo, P. Mulvaney, L. M. Liz-Marzan, and F. J. Garcia de Abajo, “Modelling the optical response of gold nanoparticles,” Chem. Soc. Rev. 37, 1792–1805 (2008). [CrossRef] [PubMed]

18.

M. Karamehmedović, R. Schuh, V. Schmidt, T. Wriedt, C. Matyssek, W. Hergert, A. Stalmashonak, G. Seifert, and O. Stranik, “Comparison of numerical methods in near-field computation for metallic nanoparticles,” Opt. Express 19, 8939–8953 (2011). [CrossRef]

19.

T. Wriedt and U. Comberg, “Comparison of computational scattering methods,” J. Quant. Spectrosc. Radiat. Transfer 60, 411 – 423 (1998). [CrossRef]

20.

T. Wriedt, “Light scattering theories and computer codes,” J. Quant. Spectrosc. Radiat. Transfer 110, 833 – 843 (2009). Light Scattering: Mie and More Commemorating 100 years of Mie’s 1908 publication. [CrossRef]

21.

M. Karamehmedović, R. Schuh, V. Schmidt, T. Wriedt, C. Matyssek, W. Hergert, A. Stalmashonak, G. Seifert, and O. Stranik, “Comparison of numerical methods in near-field computation for metallic nanoparticles,” Opt. Express 19, 8939–8953 (2011). [CrossRef]

22.

S. Eustis and M. A. El-Sayed, “Determination of the aspect ratio statistical distribution of gold nanorods in solution from a theoretical fit of the observed inhomogeneously broadened longitudinal plasmon resonance absorption spectrum,” J. Appl. Phys. 100, 044324 (2006). [CrossRef]

23.

R. Gans, “Über die form ultramikroskopischer goldteilchen,” Annalen der Physik 342, 881–900 (1912). [CrossRef]

24.

B. N. Khlebtsov, V. A. Khanadeev, and N. G. Khlebtsov, “Observation of extra-high depolarized light scattering spectra from gold nanorods,” The J. Phys. Chem. C 112, 12760–12768 (2008). [CrossRef]

25.

M. I. Mishchenko, “Light scattering by randomly oriented axially symmetric particles,” J. Opt. Soc. Am. A 8, 871–882 (1991). [CrossRef]

26.

F. Kuik, J. F. Dehaan, and J. W. Hovenier, “Benchmark results for single scattering by spheroids,” J. Quant. Spectrosc. Radiat. Transfer 47, 477–489 (1992). [CrossRef]

27.

I. R. Ciric and F. R. Cooray, “Benchmark solutions for electromagnetic scattering by systems of randomly oriented spheroids,” J. Quant. Spectrosc. Radiat. Transfer 63, 131–148 (1999). [CrossRef]

28.

P. B. Johnson and R. W. Christy, “Optical-constants of noble-metals,” Phys. Rev. B 6, 4370–4379 (1972). [CrossRef]

29.

E. A. Coronado and G. C. Schatz, “Surface plasmon broadening for arbitrary shape nanoparticles: A geometrical probability approach,” J. Chem. Phys. 119, 3926–3934 (2003). [CrossRef]

30.

C. Novo, D. Gomez, J. Perez-Juste, Z. Zhang, H. Petrova, M. Reismann, P. Mulvaney, and G. V. Hartland, “Contributions from radiation damping and surface scattering to the linewidth of the longitudinal plasmon band of gold nanorods: a single particle study,” Phys. Chem. Chem. Phys. 8, 3540–3546 (2006). [CrossRef] [PubMed]

31.

N. G. Khlebtsov, V. A. Bogatyrev, L. A. Dykman, and A. G. Melnikov, “Spectral extinction of colloidal gold and its biospecific conjugates,” J. Colloid Interface Sci. 180, 436 – 445 (1996). [CrossRef]

32.

P. C. Hansen, “Regularization tools: A matlab package for analysis and solution of discrete ill-posed problems,” NUMER ALGORITHMS 6, 1–35 (1994). [CrossRef]

33.

J. Mroczka and D. Szczuczynski, “Improved regularized solution of the inverse problem in turbidimetric measurements,” Appl. Opt. 49, 4591–4603 (2010). [CrossRef] [PubMed]

34.

P. Gill, W. Murray, and M. Wright, Numerical Linear Algebra and Optimization (Addison Wesley, 1991).

35.

J. Mroczka and D. Szczuczynski, “Simulation research on improved regularized solution of the inverse problem in spectral extinction measurements,” Appl. Opt. 51, 1715–1723 (2012). [CrossRef] [PubMed]

36.

B. N. Khlebtsov and N. G. Khlebtsov, “Multipole plasmons in metal nanorods: Scaling properties and dependence on particle size, shape, orientation, and dielectric environment,” J. Phys. Chem. C 111, 11516–11527 (2007). [CrossRef]

OCIS Codes
(240.6680) Optics at surfaces : Surface plasmons
(290.3200) Scattering : Inverse scattering
(290.5850) Scattering : Scattering, particles
(160.4236) Materials : Nanomaterials

ToC Category:
Optics at Surfaces

History
Original Manuscript: November 27, 2012
Revised Manuscript: January 18, 2013
Manuscript Accepted: January 21, 2013
Published: January 31, 2013

Virtual Issues
Vol. 8, Iss. 3 Virtual Journal for Biomedical Optics

Citation
Ninghan Xu, Benfeng Bai, Qiaofeng Tan, and Guofan Jin, "Fast statistical measurement of aspect ratio distribution of gold nanorod ensembles by optical extinction spectroscopy," Opt. Express 21, 2987-3000 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-3-2987


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References

  1. N. G. Khlebtsov and L. A. Dykman, “Optical properties and biomedical applications of plasmonic nanoparticles,” J. Quant. Spectrosc. Radiat. Transfer111, 1–35 (2010). [CrossRef]
  2. S. A. Maier, Plasmonics: Fundamentals and Applications (Springer, 2007).
  3. X. Huang, S. Neretina, and M. A. El-Sayed, “Gold nanorods: From synthesis and properties to biological and biomedical applications,” Adv. Mater.21, 4880–4910 (2009). [CrossRef]
  4. L. B. Scaffardi, N. Pellegri, O. de Sanctis, and J. O. Tocho, “Sizing gold nanoparticles by optical extinction spectroscopy,” Nanotech.16, 158–163 (2005). [CrossRef]
  5. W. Haiss, N. T. K. Thanh, J. Aveyard, and D. G. Fernig, “Determination of size and concentration of gold nanoparticles from uv-vis spectra,” Anal. Chem.79, 4215–4221 (2007). [CrossRef] [PubMed]
  6. N. G. Khlebtsov, “Determination of size and concentration of gold nanoparticles from extinction spectra,” Anal. Chem.80, 6620–6625 (2008). [CrossRef] [PubMed]
  7. O. Peña, L. Rodríguez-Fernández, V. Rodríguez-Iglesias, G. Kellermann, A. Crespo-Sosa, J. C. Cheang-Wong, H. G. Silva-Pereyra, J. Arenas-Alatorre, and A. Oliver, “Determination of the size distribution of metallic nanoparticles by optical extinction spectroscopy,” Appl. Opt.48, 566–572 (2009). [CrossRef] [PubMed]
  8. C. F. Bohren and D. R. Huffman, Absorption and Scattering of Light by Small Particles (Wiley, 1983).
  9. S. Link and M. A. El-Sayed, “Simulation of the optical absorption spectra of gold nanorods as a function of their aspect ratio and the effect of the medium dielectric constant,” J. Phys. Chem. B109, 10531C10532 (2005). [CrossRef]
  10. M. I. Mishchenko, L. D. Travis, and A. A. Lacis, Scattering, Absorption, and Emission of Light by Small Particles (Cambridge University Press, 2002).
  11. B. Khlebtsov, V. Khanadeev, T. Pylaev, and N. Khlebtsov, “A new t-matrix solvable model for nanorods: Tem-based ensemble simulations supported by experiments,” J. Phys. Chem. C115, 6317–6323 (2011). [CrossRef]
  12. B. T. Draine and P. J. Flatau, “Discrete-dipole approximation for scattering calculations,” J. Opt. Soc. Am. A11, 1491–1499 (1994). [CrossRef]
  13. V. L. Y. Loke and M. P. Mengüç, “Surface waves and atomic force microscope probe-particle near-field coupling: discrete dipole approximation with surface interaction,” J. Opt. Soc. Am. A27, 2293–2303 (2010). [CrossRef]
  14. S. W. Prescott and P. Mulvaney, “Gold nanorod extinction spectra,” J. Appl. Phys.99, 123504 (2006). [CrossRef]
  15. W. Yanpeng and N. Peter, “Finite-difference time-domain modeling of the optical properties of nanoparticles near dielectric substrates,” J. Phys. Chem. C114, 7302–7307 (2010). [CrossRef]
  16. U. Hohenester and J. Krenn, “Surface plasmon resonances of single and coupled metallic nanoparticles: A boundary integral method approach,” Phys. Rev. B72, 195429 (2005). [CrossRef]
  17. V. Myroshnychenko, J. Rodriguez-Fernandez, I. Pastoriza-Santos, A. M. Funston, C. Novo, P. Mulvaney, L. M. Liz-Marzan, and F. J. Garcia de Abajo, “Modelling the optical response of gold nanoparticles,” Chem. Soc. Rev.37, 1792–1805 (2008). [CrossRef] [PubMed]
  18. M. Karamehmedović, R. Schuh, V. Schmidt, T. Wriedt, C. Matyssek, W. Hergert, A. Stalmashonak, G. Seifert, and O. Stranik, “Comparison of numerical methods in near-field computation for metallic nanoparticles,” Opt. Express19, 8939–8953 (2011). [CrossRef]
  19. T. Wriedt and U. Comberg, “Comparison of computational scattering methods,” J. Quant. Spectrosc. Radiat. Transfer60, 411 – 423 (1998). [CrossRef]
  20. T. Wriedt, “Light scattering theories and computer codes,” J. Quant. Spectrosc. Radiat. Transfer110, 833 – 843 (2009). Light Scattering: Mie and More Commemorating 100 years of Mie’s 1908 publication. [CrossRef]
  21. M. Karamehmedović, R. Schuh, V. Schmidt, T. Wriedt, C. Matyssek, W. Hergert, A. Stalmashonak, G. Seifert, and O. Stranik, “Comparison of numerical methods in near-field computation for metallic nanoparticles,” Opt. Express19, 8939–8953 (2011). [CrossRef]
  22. S. Eustis and M. A. El-Sayed, “Determination of the aspect ratio statistical distribution of gold nanorods in solution from a theoretical fit of the observed inhomogeneously broadened longitudinal plasmon resonance absorption spectrum,” J. Appl. Phys.100, 044324 (2006). [CrossRef]
  23. R. Gans, “Über die form ultramikroskopischer goldteilchen,” Annalen der Physik342, 881–900 (1912). [CrossRef]
  24. B. N. Khlebtsov, V. A. Khanadeev, and N. G. Khlebtsov, “Observation of extra-high depolarized light scattering spectra from gold nanorods,” The J. Phys. Chem. C112, 12760–12768 (2008). [CrossRef]
  25. M. I. Mishchenko, “Light scattering by randomly oriented axially symmetric particles,” J. Opt. Soc. Am. A8, 871–882 (1991). [CrossRef]
  26. F. Kuik, J. F. Dehaan, and J. W. Hovenier, “Benchmark results for single scattering by spheroids,” J. Quant. Spectrosc. Radiat. Transfer47, 477–489 (1992). [CrossRef]
  27. I. R. Ciric and F. R. Cooray, “Benchmark solutions for electromagnetic scattering by systems of randomly oriented spheroids,” J. Quant. Spectrosc. Radiat. Transfer63, 131–148 (1999). [CrossRef]
  28. P. B. Johnson and R. W. Christy, “Optical-constants of noble-metals,” Phys. Rev. B6, 4370–4379 (1972). [CrossRef]
  29. E. A. Coronado and G. C. Schatz, “Surface plasmon broadening for arbitrary shape nanoparticles: A geometrical probability approach,” J. Chem. Phys.119, 3926–3934 (2003). [CrossRef]
  30. C. Novo, D. Gomez, J. Perez-Juste, Z. Zhang, H. Petrova, M. Reismann, P. Mulvaney, and G. V. Hartland, “Contributions from radiation damping and surface scattering to the linewidth of the longitudinal plasmon band of gold nanorods: a single particle study,” Phys. Chem. Chem. Phys.8, 3540–3546 (2006). [CrossRef] [PubMed]
  31. N. G. Khlebtsov, V. A. Bogatyrev, L. A. Dykman, and A. G. Melnikov, “Spectral extinction of colloidal gold and its biospecific conjugates,” J. Colloid Interface Sci.180, 436 – 445 (1996). [CrossRef]
  32. P. C. Hansen, “Regularization tools: A matlab package for analysis and solution of discrete ill-posed problems,” NUMER ALGORITHMS6, 1–35 (1994). [CrossRef]
  33. J. Mroczka and D. Szczuczynski, “Improved regularized solution of the inverse problem in turbidimetric measurements,” Appl. Opt.49, 4591–4603 (2010). [CrossRef] [PubMed]
  34. P. Gill, W. Murray, and M. Wright, Numerical Linear Algebra and Optimization (Addison Wesley, 1991).
  35. J. Mroczka and D. Szczuczynski, “Simulation research on improved regularized solution of the inverse problem in spectral extinction measurements,” Appl. Opt.51, 1715–1723 (2012). [CrossRef] [PubMed]
  36. B. N. Khlebtsov and N. G. Khlebtsov, “Multipole plasmons in metal nanorods: Scaling properties and dependence on particle size, shape, orientation, and dielectric environment,” J. Phys. Chem. C111, 11516–11527 (2007). [CrossRef]

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