Impact of Nonideal Nanoparticles on XPS Quantitation: An Investi

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Impact of Nonideal Nanoparticles on XPS Quantitation: An Investigation Using Simulation and Modelling of Gold Nanoparticles Smruti R. Sahoo, Ramacharyulu VRK P, and Shyue-Chu Ke Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b02837 • Publication Date (Web): 15 Jan 2018 Downloaded from http://pubs.acs.org on January 15, 2018

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Analytical Chemistry

Impact of Nonideal Nanoparticles on XPS Quantitation: An Investigation Using Simulation and Modelling of Gold Nanoparticles Smruti R. Sahoo,a P.V.R.K. Ramacharyulu,a and Shyue-Chu Kea* a

Department of Physics, National Dong Hwa University, Hualien 97401, Taiwan.

*Corresponding Author E-mail: [email protected]; Fax: +886-3-8633690

ABSTRACT: Quantitative X-ray photoelectron spectroscopic (XPS) analysis combined with spectral modeling of photoelectrons can be valuable while investigating the surface chemistry of nanoparticles (NPs) with different morphologies. Herein, with the use of NIST Simulation of Electron Spectra for Surface Analysis (SESSA), a comparative analysis of experimental and simulated photoelectron peak intensities in gold nanoparticles (AuNPs) of different morphologies is presented. Three sets of supported AuNPs with different morphologies were selected from a series of as synthesized Au-TiO2 catalyst samples. Using the Transmission Electron Microscopy (TEM) analyzed morphological information of the AuNPs as input model parameters in SESSA, XPS spectra were generated from the respective input NP morphologies. A degree of greater mismatch between SESSA simulated and experimental XPS spectra was observed while using the TEM obtained average diameter of the nanoparticles. The degree of mismatch lowered when the true nonspherical shape of the nanoparticles as obtained from TEM images was taken into account for the simulation. This demonstrates the impact of surface morphology on the XPS peak intensities which needs to be incorporated to obtain precise quantified information from the supported nanoparticles. This work demonstrates the applicability of SESSA in combination with experimental XPS and TEM measurements for precise quantification of XPS spectra from complex, nonideal shaped nanoparticles. This study can be extended to include a broad range of nanoparticles with ideal or nonideal geometries, thus providing a simple method to utilize quantitative XPS analysis to a wide range of nanomaterials.

Nanoparticles (NPs) are found in a broad range of technological applications in various fields, such as medicine, agriculture, optoelectronic devices, aerospace and catalysis.1,2,3 The surface chemistry of NPs plays an important role in the application and processing of nanomaterials4, as it affects the interaction of the NPs with the surrounding environment.5 Compared to the flat surface nature of bulk materials, NPs have structures of surface planes, edges, and vertices due to curvature and faceting.6 Moreover, supported NPs have not been subjected to the same level of fundamental surface characterization as the flat surfaces. A technological challenge, therefore, is the characterization of the nanoparticle surface, which may be highly complex. Transmission electron microscopy (TEM), dynamic light scattering (DLS), UV/vis, and zeta potential measurements are some of the most widely used techniques to characterize nanoparticles. Apart from many of their advantages, they do not provide any quantitative information about the nanoparticle surface composition. Analysis with X-ray Diffraction (XRD) poses certain problems such as weak detection limits and variation of angle.7 Sample preparation for TEM studies requires thinning the samples using a Focussed Ion Beam (FIB) or a chemical etching method, which is time consuming and may create artefacts and damages upon electron bombardment.8 Atomic Force Microscopy (AFM) cannot provide information from the underlying second or third layer which are important in understanding the interaction between the nanoparticle surface and the support. In many catalysis research studies, very small NP contents are used which often escape from the sensitivity limits of many characterization tools. For instance, the ideal limit of Au con-

tents in Au-TiO2 catalysts is 0.5-1 wt%,9,10 which therefore demands the need of methods with higher sensitivities.11 In catalysis research, researchers have demonstrated the utility of X-ray Photoelectron Spectroscopy (XPS) in providing detailed quantitative information of NP surfaces.12,13,14,15 XPS utilizes the short inelastic mean free path of electrons to provide information from a depth similar to the sizes of NPs, which makes it sensitive to the NP surface.16 Furthermore, compared to other methods, XPS does not significantly damage NP surfaces. Qualitative use of XPS has been used extensively to examine the presence of expected elements or contaminants in materials. Although quantitative XPS yields a broad range of information such as measurement of mean particle sizes,17,18,19 heterojunction band discontinuities,20,21,22 ligand densities on nanomaterials23 and crystal surface orientation,24 it often needs sophisticated data analysis. Recently, quantitative XPS is used along with some spectral modelling softwares like Simulation of Electron Spectra for Surface Analysis (SESSA)25 to extract information, such as shell thickness of core-shell NPs,26 nonuniformities in core-shell NPs27 and presence of a self-assembled monolayer on a synthesized nanoparticle surface.28 SESSA uses partial intensity calculations along with a Monte-Carlo algorithm to generate the model spectra. Samples can be modelled as a flat surface, spheres, islands and multiple thin layers with specific shapes, sizes and thicknesses. The spectrometer settings such as the source energy, analyzer and sample orientation with surface normal can be input in SESSA to obtain simulated spectra for comparison with experimental spectra.25

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In this article, we demonstrate the use of XPS, in combination with TEM for the measurement of size, to examine the influence of nanoparticle morphology on the XPS peak intensities. Three sets of Au-TiO2 catalyst samples with varied AuNP morphologies were selected as model systems for this study. We compared and contrasted XPS peak intensities from three different NP morphologies with analogous results for planar Au thin films and examined the peak intensity ratios in all cases. We combined SESSA for modelling spherical as well as nonspherical NPs with the experimental XPS results to determine the Au 4f doublet peak intensity ratios and investigated how variations in particle morphology influenced the quantitative XPS analysis. This study shows how nanostructures influence XPS data and how simple models of sample structure and data analysis can be used to extract precise quantitative information from their corresponding XPS spectra.

EXPERIMENTAL All glassware was cleaned with deionized (DI) water, ethanol and acetone several times prior to first use, in order to avoid possible contaminants during the entire reaction process. Millipore DI water (resistivity: 18.2 MΩ) was used throughout the synthesis process. A series of Au-TiO2 catalysts were prepared using the deposition–precipitation (DP) method. Titania Degussa P25 (70% anatase and 30% rutile, purity > 99.5%) was used as the support. HAuCl4·3H2O (Sigma-Aldrich, Au content ≥ 99.9%) was used as the Au precursor. Typically, 2.0 g of TiO2 was added to 150 mL of aqueous solution, which contained HAuCl4 (3.34 × 10–4 M) at an adjusted pH value of 8-8.5. The mixture (pH value 8-8.5) was vigorously stirred at room temperature for 6 h in the absence of light to avoid the gold precursor decomposition. Next, the suspension was centrifuged and thoroughly washed with deionized water for at least ten times to remove the chlorine residual. The obtained material was divided equally and placed in several different crucibles. Finally, the materials placed in crucibles were calcined in air at different temperatures, ranging from 300oC to 600oC. The appearance of AuTiO2 samples in all cases changed from white to light and dark purple after calcination, indicating the deposition of metallic Au on TiO2. Three of the prepared catalyst samples were chosen for this study. They were marked as GC1, GC2, and GC3 and classified according to their varied morphology, which was a result of the variation in the calcination conditions. XPS measurements were performed on a Thermo Scientific Kα XPS system using a monochromatic AlKα X-ray source. The core level high-resolution spectra were collected with an analyzer pass energy of 50 eV. The energy calibration to determine the work function of the spectrometer was made by using the inbuilt Au, Ag, and Cu standard samples. The uncertainty for binding energy measurements was checked by the position of a bulk Au 4f7/2 line to 84.0 eV and a C1s line at 284.8 eV, and it was estimated to be ± 0.02 eV. Specimens for high-resolution Transmission Electron Microscopic (HRTEM) studies were prepared by the usual dropcast method. Very small amounts of the as calcined Au-TiO2 powder samples were diluted in ethanol and sonicated to form a uniform colloidal suspension. A few drops from the thin upper part of the colloidal suspension were collected and cast onto carbon coated copper TEM grids. The specimens were

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then allowed to dry in air at room temperature. A JEOL JEM 3010 Transmission Electron Microscope (TEM) was operated at an accelerating voltage of 300 kV. The HRTEM images were acquired at a beam current density of ~50-60 pAcm-2. A total of n ~ 70 particles were analyzed to determine the mean diameter and standard deviation in each set of samples.

RESULTS AND DISCUSSION

TEM Results Figure 1 shows the HRTEM images of the AuNPs for the series of three Au-TiO2 catalysts GC1, GC2 and GC3 as prepared by the deposition-precipitation procedure. As is observed from the HRTEM images, there is little variation in the sizes (GC1: 9.98 ± 1.14 nm; GC2: 10.01 ± 1.13 nm; GC3: 10.02 ± 1.10 nm) of AuNPs in the three series of catalyst samples. However, the nanoparticles possess different surface morphologies. The shape of the AuNPs in the GC1 catalyst samples is nearly spherical in nature. Though the supported AuNPs in GC2 and GC3 samples retain their round shapes during calcination, they are non-spherical in nature with different degrees of flattening on the TiO2 surface. These nanoparticles expose their flat surfaces to TiO2 possibly to increase adhesion with greater surface areas.29 Surface morphologies of the supported AuNPs depend upon several parameters viz. the concentration of gold and TiO2 in the precipitates and the calcination conditions. In our case, only calcination temperature is expected to affect the particle morphology, as the other conditions were kept fixed. We used this information, namely size and nonideal shapes of the supported nanoparticles for XPS simulation by SESSA.

XPS Analysis

Figure 1. HRTEM images of AuNPs from three different AuTiO2 catalyst samples obtained by casting a few drops of AuTiO2 colloidal solution on a carbon coated copper grid and dried in air at room temperature. (Scale Bar: 10nm)

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Figure 2. Fitted high-resolution Au 4f XPS spectra from the as synthesized Au-TiO2 catalyst samples GC1, GC2 and GC3 and bulk Au B1 and B2 respectively. The Au 4f7/2 peak positions are referenced to the C 1s peak of adventitious carbon at 284.8 eV. X-ray photoelectron spectroscopy (XPS) was employed to study the core level electronic structure of the supported AuNPs. It was found that the C1s charge corrected Au 4f7/2 binding energy of Au-TiO2 catalysts was ~0.4 – 0.5 eV lower than that of pure bulk Au. Such negative shifts of the Au 4f7/2 binding energy positions are explained on the basis of metalsupport interaction,30,31 particle size effects32 and low coordinated gold atoms at the surface.33,34 The C1s peak position at 284.8 eV was determined from the pure bulk Au samples with Au 4f7/2 lines at Binding Energy (BE) = 84.0 eV. To account for such negative shifts, other peak positions namely the Ti 2p3/2 at BE: 458.85 eV (Ti4+), O1s at 530.01 eV (O2-) and Ti 3p at 37.5 eV lines in Au-TiO2 catalysts were selected as reference lines. These reference values are typically taken from literature data.35,33,36 The Au 4f7/2 peak position in the GC1 catalyst changed to 83.79 eV when the Ti 2p3/2 line in the catalyst was selected as reference. It further changed to 83.71 eV and 83.62 eV when the O1s and Ti3p lines were chosen as references, respectively. The Au4f7/2 lines in the other catalysts also showed such variations in peak positions when the reference peak was changed from C1s to Ti 2p3/2, O1s or Ti 3p. The Au 4f7/2 binding energy position varied in the range from 83.53 eV to 83.80 eV in all catalysts with change in reference peak position. So the choice of a reliable elemental

reference peak remained an obvious problem. However, it always remained lower than the bulk value (BE = 84.00 eV) by any means of correction. Moreover, for any means of reference, the Au 4f7/2 line from the GC1 Au-TiO2 catalyst always remained ~0.06 - 0.17 eV lower than both the GC2 and GC3 catalysts. This might be related to the “more round” spherical nature of the AuNPs in the GC1 catalyst when compared to the GC2 or GC3 catalysts.37 A spherical particle exposes a larger surface area, thereby a larger amount of surface atoms, compared to either the partially hemispherical or the quasi- spherical nanoparticles in the GC3 and GC2 catalysts. The coordination number imperfection associated with the surface atoms decreases the surface valence band width. This results in a higher localization of the Au 5d electrons, which in turn decreases the Au 4f BE position.37,34 The Au 4f core level photoemission spectra were background subtracted and fitted with mixed Gaussian-Lorentzian peaks as described below. In all Au4f spectra, Shirley type background subtraction was used before peak fitting. The spectra were fitted by a non-linear and least square procedure. Mixed Gaussian-Lorentzian (GL) lines were used for fitting in order to include both the surface and the bulk components.38,39 To take account of all photoelectrons emitted into the given solid angle, binding Energy, FWHM and peak areas of the Au 4f7/2 and Au 4f5/2 peaks were allowed to adjust to receive a minimum χ2 value. Initially, we constrained the GL ratio to equal values, then varied them freely in the range from 20/80 to 1/99 (10/90 on average) for different samples. We performed 10 fittings for each spectrum by keeping the spectrum end points at fixed values of binding energies. The peak fitting procedures were first applied to two flat planar Au thin films with different thicknesses (50 nm and 30 nm respectively) sputter deposited onto clean Silicon substrates. Both the XPS and SESSA simulation results from the two flat planar Au thin films were compared for accuracy or deviation in calculated peak ratios. The same peak fitting parameters were then extended to fit both XPS and SESSA simulated spectra from the three supported AuNPs with different morphologies.

SESSA Simulation on Au Thin Films In order to check the match or mismatch between XPS and SESSA simulated results, the spectra from two gold thin films with different thicknesses (50 nm and 30 nm) sputter deposited on silicon substrates were compared. The experimental XPS spectra from the two Au thin films, B1 (50 nm) and B2 (30 nm) are shown in Fig. 2. The experimental data were background subtracted and fitted according to the procedure described earlier. The Au 4f7/2 peak occurs at the binding energy position 83.98 eV. The ratio of the peak areas in samples B1 and B2 was calculated to be 1.30 ± 0.02 and 1.29 ± 0.03 respectively. The Au 4f spin-orbit splitting in the bulk samples displayed small variations, 3.68 ± 0.02 eV, which is comparable to the reference value for bulk Au at 3.67 eV.40 Using the planar layer model in SESSA (Figure S1a), we generated simulated spectra for the samples, assuming a bulk silicon substrate and a gold layer of thickness 50 nm (Sample B1) or 30 nm (Sample B2) on top of the substrate. The simulated spectra along with the experimental spectra are shown in Fig. 3 for comparison.

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Figure 4. Values of Au 4f7/2 to Au 4f5/2 peak intensity ratios obtained after peak fitting both SESSA simulated and experimental XPS data.

Figure 3. SESSA generated XPS spectra overlapped on experimental XPS spectra from Au thin films of different thicknesses (B1: 50 nm and B2: 30 nm) sputter deposited onto Silicon substrates. The Planar layer model was used to generate the simulated spectra.

variations, we resorted to SESSA modelling and generation of XPS spectra for the three sets of NPs with different morphologies. In all our catalyst samples GC1-GC3, the peak intensity ratios of the Au 4f7/2 and Au 4f5/2 excitations are found to deviate from the standard statistical ratio of 4:3, which has been reported by many researchers.41,42,43,44,45,46 The peak intensity ratio for the GC3 catalyst remained closer to the standard statistical ratio (1.35 ± 0.02 vs 4:3). So a standard t-test was done to validate its statistical significance: the p-value = 0.044, which is less than 0.05, indicating that the difference is significant. Au 4f7/2 to Au 4f5/2 peak intensity ratios have been observed to vary widely from 1.09 ± 0.04 to 1.63 ± 0.08.38 This is reasoned to be due to changes in the band structure of the supported AuNPs, the electronic state of the AuNPs and their interaction with the support material.38 A decrease in the coordination number of the atoms in AuNPs reduces the strength of the Au-Au d-d interaction which thus increases the number of d-hole counts. An increased number of empty 5d states hybridized into 6sp conduction bands explains the variation in Au 4f7/2 and Au 4f5/2 peak intensity ratios as47

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An overall good match with the experimental spectra is observed. As is observed from Figure 4, the difference between the SESSA and experimental XPS peak intensity ratios from the two bulk Au samples displayed values of 0.07 and 0.05 for samples B1 and B2, respectively. A smaller difference implies a better match between the experimental and simulated values of the peak intensity ratios. This demonstrates the applicability of SESSA to flat planar substrate/layered samples, which agrees well with the experimental results.

SESSA Simulation on AuNPs The experimental peak ratios of the AuNPs from GC1, GC2 and GC3 catalysts showed variable peak ratios from 1.13 ± 0.02 (GC1) to 1.35 ± 0.02 (GC3). The GC1 and GC2 values are also different from the bulk samples. To account for such

Figure 5. Modelling the nanoparticle morphology in SESSA for XPS simulation. To model a flattened spherical NP, both diameter and height need to be specified as input parameters in the SESSA simulation.

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Figure 7. A comparison of the values of the differences in peak intensity ratios between experimental XPS and simulated results for a given SESSA model. The deviation between experiment and theory is larger when photoelectron signals are assumed to originate from a completely spherical AuNP or a flat planar Au layer.

Figure 6. SESSA generated XPS spectra from three different nanoparticle morphologies. TEM obtained information on the diameter and height of the nanoparticles was used as input parameters to create NP models in SESSA. (1) where h5/2 and h3/2 are the hole densities in the Au5d bands. Spherical AuNPs in the GC1 catalyst showed the lowest peak intensity ratios, which is in good agreement with the SESSA analysis as well. AuNPs from the GC2 and GC3 samples deserve special attention, as they are nonspherical with variable degrees of flattening on the TiO2 surface, thus having nonideal geometries that are different from ideal spherical or layered models incorporated in SESSA modelling. One advantage of the present version of SESSA v2.0 is that it too can incorporate nonspherical objects; in particular, a sphere with a flattened surface. Such kinds of flattened nanospheres can be modelled in SESSA by introducing two parameters, namely diameter and height of the particles. Such a model structure is illustrated in Fig. 5. The height of the particle is measured with reference to the flattened surface, the distance from the center of the flattened surface with an extended normal to the perimeter of the nanoparticle. By specifying the average diameter and height of the nanoparticles in SESSA, we generated

the respective XPS spectra (Fig. 6) for the three supported AuNPs with different morphologies. The generated spectra were then background subtracted and peak fitted according to the earlier procedure. A look into the difference of values in Fig. 4 reveals that there is a good match between SESSA and XPS results in all these three cases. Therefore, the different values of peak ratios resulted from the different shapes of the NPs. This demonstrates that XPS peak intensities are more sensitive to the surface of nanoparticles than to bulk samples. Fig. 7 shows a comparison of the values of the difference between simulated and experimental XPS results, when surface morphology is not incorporated. Nanoparticles from GC2 and GC3 catalysts are nonspherical in nature with variable degrees of flattening onto TiO2 surfaces. On the other hand, NPs from GC1 samples are nearly spherical. All the nanoparticles possess a diameter of 10 nm. So a comparison of peak intensities can be made between the ideal and nonideal geometries of the supported AuNPs. Assumption of a completely spherical shape yielded peak intensity ratio difference values of 0.13 and 0.11 in GC2 and GC3 nonideal AuNPs; which are larger than 0.07 and 0.05, the differences obtained when using a nonspherical geometry. Similarly, assuming a flat planar surface, difference values of 0.19 (GC1), 0.12 (GC2) and 0.10 (GC3) were obtained; which were also much larger than the actual difference values. This demonstrates the importance of sample size as well as shape affecting photoelectron peak intensities. A precise quantification therefore requires both the nonideal shape and size of the nanoparticles to be specified.

Elemental Composition from SESSA Analysis Both XPS and SESSA analyzed Au, Ti and Oxygen elemental compositions in all the three Au-TiO2 catalyst samples, GC1- GC3 are shown in Fig. 8 for comparison. The elemental compositions are determined in the standard way by integrating the peak areas and correcting the areas by the respective Scofield photoionization cross sections of the core level photoelectrons.48 The experimental calculations were based upon averaging data from three replicates and four batches meas-

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elemental concentration, the difference in Au concentration from all catalyst samples relative to a spherical particle geometry was analyzed. As shown in the inset table in Fig. 8, the difference amounts to ~14% and ~27% for the GC2 and GC3 catalyst samples, respectively; and there is an excellent match between the experimental XPS data and the SESSA calculations when the nonspherical particle morphology is taken into consideration.

CONCLUSION

Figure 8. Comparison of elemental compositions from experimental XPS and SESSA simulated results for respective geometries in the three catalysts. The inset table shows the differences between experimental XPS and simulated SESSA calculated values of Au concentration. This also shows the difference from experimental values when photoelectron signals are assumed to originate from an ideal sphere.

ured from each sample (GC1, GC2 or GC3) type. In all cases the p-values remained less than 0.05 (p < 0.05; Table S1). SESSA simulations of the AuNPs were initially based on the schematic model as presented in Figure S1b, which is similar to the GC1 spherical AuNPs with an average diameter of 10 nm. The elemental gold concentration based on such a model calculation was 0.78 ± 0.05 atomic percent, which remained ~4.35% lower than the XPS determined value for GC1 (0.81 ± 0.22 atomic %). SESSA simulation based on the TEM analyzed nonspherical AuNPs in GC2 and GC3 catalysts provided gold composition values of 0.70 ± 0.05 and 0.60 ± 0.04 atomic % respectively, which differed by ~ 4.77 and ~4.73% than the experimental values (0.67 ± 0.23 and 0.58 ± 0.24 atomic % in the GC2 and GC3 catalysts, respectively.) To improve accuracy, we resorted to other features available in the present version of SESSA. Unlike various supported metal oxide catalyst models,49,50 SESSA v2.0 is limited to a few model morphologies. One of the features available in the present version of SESSA is simulating a layered spherical morphology as shown in Figure S1d. We used this model to simulate encapsulation of NPs from the support material.51,52,53 We introduced a thin Ti as well as TiOx (1 < x < 2) layer over the AuNPs in all catalyst samples. This affected the XPS intensities appreciably. A Ti layer of thickness as small as 0.3 nm resulted in drastic reduction in Au composition (0.45 atomic % in GC1 catalysts and 0.38 and 0.35 atomic % in GC2 and GC3 catalyst samples). A further reduction in Au composition was observed with an increase in the thickness of the Ti encapsulating layer, thereby restricting the use of this model. Overall, the SESSA simulations showed a good match to the experimental data when the appropriate model morphologies, obtained from TEM images, were incorporated. To further investigate the influence of particle shapes on values of

AuNPs with three different surface morphologies were synthesized on TiO2 supports by the depositionprecipitation method to investigate their impact on the photoelectron peak intensities. The Au 4f7/2 to Au 4f5/2 peak ratios from these AuNPs were observed to be different from each other. On the other hand, peak ratios from planar bulk Au samples did not show any appreciable changes. Photoelectron spectral modelling software SESSA v2.0 was used to generate model XPS spectra by taking AuNPs as well as bulk sample morphology into account. Greater deviation from the experimental photoelectron peak intensity ratios resulted when nonideal NP geometries were not taken into consideration. In order to obtain the best match between experimental and simulated peak intensities, inclusion of nonideal geometries from the supported AuNPs was found to be essential. SESSA was used to quantify elemental compositions present in all the three catalyst samples. A good match was observed between SESSA and XPS determined elemental compositions when the true morphologies of the AuNPs as obtained from TEM images were incorporated. Simulation results based on ideal spherical AuNPs and not accounting the nonspherical geometries of the AuNPs resulted in significant differences in the concentration of AuNPs. For the nonideal AuNPs in the catalyst samples investigated in this study, by not incorporating nonsphericity into the simulation, the calculated Au concentration values differed by ~14% and ~27% from the ideal spherical nanoparticles. In summary, considering the particle morphology in the SESSA calculations led to a better match of the calculated Au 4f7/2 to Au 4f5/2 peak intensity ratio with the corresponding experimental value; and an excellent agreement was obtained for the Au concentration of all samples as well. All of these factors indicate that using the particle morphology in the SESSA calculations leads to a better match with the experimental XPS results.

ASSOCIATED CONTENT Supporting Information The SESSA spectral modelling procedure for TiO2 supported spherical, nonspherical and layered spherical AuNP morphologies as well as flat, planar-layer morphologies from Au thin films sputter deposited onto silicon substrates is demonstrated. Nanoparticle size distribution in the catalysts are shown in Fig. S2. High resolution core level Ti 2p3/2, Ti 3p, C1s and O1s XPS spectra are also shown. The p-values from all the catalysts are tabulated in Table S1.

AUTHOR INFORMATION Corresponding Author

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Analytical Chemistry * E-mail: [email protected]

ACKNOWLEDGMENT This work was funded by the Ministry of Science and Technology (MOST grant No. 105-2112-M-259-007), Taiwan.

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