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Cation−π Interaction Triggered-Fluorescence of Clay Fillers in Polymer Composites for Quantification of Three-Dimensional Macrodispersion Jinpan Zhong, Zhiqiang Li, Weijiang Guan, and Chao Lu* State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China S Supporting Information *

ABSTRACT: It is a considerable challenge to realize 3D fluorescence quantification of macrodispersion of clay fillers in a polymer matrix mainly owing to quenching of light emission in the solid state. Herein, a strong light emission is generated within the interlaminated clay as a result of a cation−π interaction between cationic surfactant and fluorescent polycyclic aromatic hydrocarbon when they are cointercalated into clay. Confocal laser scanning microscopy (CLSM) is applied for 3D imaging of macrodispersion of the fluorescence-labeled clay fillers in a silicone rubber matrix. More importantly, the quantification of macrodispersion of clay fillers in the overall polymer composite is established by a statistical model. The proposed method fills in an important gap in the standard for macrodispersion quantification of inorganic fillers in polymer composites. he degree of dispersion of clay fillers in a polymer matrix influences improvements of their mechanical, electrical, and thermal properties of composites.1−4 Therefore, the ability to accurately quantify dispersion of clay fillers within the polymer is essential not only to understand the structure− property relationships in fillers but also to achieve the critical processing conditions necessary for high-quality composite production.5−7 Currently, transmission electron microscopy (TEM) has been extensively applied for qualitative evaluation of dispersion of clay fillers within the polymer.8−10 However, TEM is unsuitable for quantitative description of the dispersion of inorganic fillers because of its intrinsic limitations: the domain size is usually less than ten micrometers, and the penetration depth of electron in TEM system is so limited that polymer matrix samples require thin enough (∼100 nm) for subsequent three-dimensional analysis.11−13 Therefore, there is an imperative need to establish an efficient quantitative methodology for 3D dispersion of inorganic fillers in a polymer matrix at macroscale. Nowadays, confocal laser scanning microscopy (CLSM) has become an attracting technique in biochemical research and material science.14,15 CLSM owns a powerful confocal system to allow the 3D sectioning and reconstruction of one sample in a nondestructive manner.16,17 In comparison to the thin penetration depth of electron in TEM system, the penetration depth of the laser beam in a transparent medium is able to attain a few tens of micrometers, facilitating the information acquisition of vertical dimension at macroscale.18,19 More importantly, scanning field size of CLSM could be up to hundreds of micrometers in 2D dimension. The natural advantages of CLSM in the 3D imaging make it promising to quantify the macrodispersion of inorganic fillers in the polymer

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© XXXX American Chemical Society

matrix. Unfortunately, the direct visualization of dispersion of inorganic fillers using 3D fluorescent imaging is hampered because the light emissions generally are quenched in solid state inorganic fillers.20,21 This bottleneck has recently been broken by synthesizing aggregation-induced emission (AIE)-active fluorophore to illuminate the inorganic fillers.22 However, the synthesis procedure for such AIE-active fluorophore is complicated and time-consuming. Cation−π interaction is a noncovalent molecular interaction between the face of an electron-rich π system and an adjacent cation.23−25 This ubiquitous interaction plays an important role in chemistry, material science, biology, and allied areas.26−28 In the current era of material technology, it is key to modulate the cation−π interaction in strengthening the functionality of materials. Recently, the improved fluorescence of aromatic compounds is achieved by a strong cation−π interaction between cations and aromatic compounds.29,30 In this work, cetyltrimethylammonium bromide (CTAB) and fluorescent polycyclic aromatic hydrocarbon (PAH) are cointercalated into clay. Interestingly, we found that the aggregation of PAH is inhibited as a result of a strong cation−π interaction between CTAB and PAH. The 3D imaging of macrodispersion of fluorescence-labeled clay fillers in a silicone rubber matrix was carried out by CLSM technique. The quantification of macrodispersion of clay fillers in the overall polymer composite is established by a statistical model by computing the deviation between ideal homodispersion and real dispersion (Figure 1). The proposed statistical model is composed of three steps: (1) Received: September 1, 2017 Accepted: October 27, 2017 Published: October 27, 2017 A

DOI: 10.1021/acs.analchem.7b03575 Anal. Chem. XXXX, XXX, XXX−XXX

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Analytical Chemistry

Figure 1. Schematic representation of 3D macrodispersion of fluorescent inorganic fillers triggered by cation−π interaction in polymer matrix quantified by statistical model.

scan rate of 10°/min. Steady-state polarized photoluminescence measurements of the organoclay were recorded with an Edinburgh Instruments FLS 920 fluorospectrophotometer. Transmission electron microscopy (TEM) photographs were obtained by a Tecnai G220 TEM (FEI Company, USA). Preparation of CTAB-BPEA-MMT. CTAB-BPEA-MMT was prepared from Na+-MMT by ion exchange method. Typically, a 1.0 g portion of Na+-MMT was mixed with 100 mL of deionized water. Then, different loading amounts of CTAB were added into the solution for preparing different samples. Note that BPEA is indissolvable in aqueous solution, and its solubility is enhanced in CTAB micelle solution. In this work, excess BPEA (3.7 mg) is added into the adduct of CTABMMT with different amounts of CTAB to make larger adsorption of BPEA. The ion exchange was carried out under stirring for 8 h at 60 °C. Then the reaction solution was centrifuged at 5000 rpm for 5 min, and the precipitate was washed with distilled water. The obtained CTAB-BPEA-MMT was dried under vacuum at 60 °C, and then finely powdered in an agate mortar. The granulated powder was then passed through a 600 mesh sieve to remove agglomerates larger than 20 μm. Preparation of DTAB-Perylene-MMT. DTAB-PeryleneMMT was also prepared from Na+-MMT by ion exchange method. A 1.0 g portion of Na+-MMT was mixed with 100 mL of deionized water. Then, 0.5 g of DTAB and 1.5 mg of perylene were added into the solution. The ion exchange was carried out under stirring for 8 h at 60 °C. Then the reaction solution was centrifuged at 5000 rpm for 5 min, and the precipitate was washed with distilled water. The obtained DTAB-Perylene-MMT was dried under vacuum at 60 °C, and then finely powdered in an agate mortar. The granulated powder was then passed through a 600 mesh sieve to remove agglomerates larger than 20 μm. Preparation of Silicone Rubber/CTAB-BPEA-MMT and Silicone Rubber/DTAB-Perylene-MMT. The silicone rubber/CTAB-BPEA-MMT composite, containing 30.0 g of silicone rubber, 9.0 g of precipitated silica, 0.5 g of di(tertbutylperoxyisopropyl)benzene and 0.9 g CTAB-BPEA-MMT, was prepared by blending in a double-roller mixer for 25 min at room temperature. The resulting composites were molded at

Pearson chi-square test of the deviation of centroids number in different data sets; (2) projection of the centroids onto different planes for density comparison; (3) projection of the centroids onto axes for evaluation of numerical characteristics. The proposed quantification method was successfully applied to distinguish the macrodispersion of inorganic fillers in two silicone films, which was impossibly identified by qualitative methods. This method was successfully applied to distinguish the macrodispersion of inorganic fillers in two silicone films.

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EXPERIMENTAL SECTION Chemicals and Materials. Sodium montmorillonite (Na+MMT) with maximum cation exchange capacity values of 145 mequiv per 100 g (from Nanocor, PGW grades) was used without further purification. Methyl vinyl silicone rubber was obtained from Guibao Co., Ltd. (Chengdu, China). Cetyltrimethylammonium bromide, dodecyltrimethylammonium bromide (DTAB), and di(tert-butylperoxyisopropyl)benzene were obtained from J&K Chemical Ltd. (Beijing, China). 9,10Bis(phenylethynyl)anthracene was obtained from Beijing HWRK Chem. Co., Ltd. (Beijing, China). Perylene was obtained from Alfa Aesar (Ward Hill, MA, USA). Precipitated silica was obtained from Evonik Industries AG (Hanau, Germany). Deionized water (18.2 MU cm, Milli-Q, Millipore, Barnstead, CA, USA) was used throughout the experiments. Apparatus. 3D fluorescence image series, 3D reconstruction images were recorded on a Leica TCS SP8 laser scanning confocal microscope (Leica, Germany). The data sets of centroids were obtained by a professional 3D analysis software (LAS X 3D Analysis) provided by Leica Microsystem. The fluorescence spectra were carried out using a Hitachi F-7000 fluorescence spectrophotometer (Tokyo, Japan) with the excitation wavelength of 315 nm. Both the excitation slit and the emission slit were maintained at 5.0 nm with a scanning rate of 1200 nm/min. The UV−Vis absorption spectra were acquired on a Shimadzu UV−3600 spectrophotometer (Tokyo, Japan). The powder X-ray diffraction (XRD) measurements were performed on a Bruker (Karlsruhe, Germany) D8 ADVANCE X-ray diffractometer equipped with graphitemonochromatized Cu Kα radiation (λ = 1.5406 Å). The 2θ angle of the diffractometer was stepped from 2° to 70° with a B

DOI: 10.1021/acs.analchem.7b03575 Anal. Chem. XXXX, XXX, XXX−XXX

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Figure 2. (A) Fluorescence spectra of Na+-MMT and CTAB-BPEA-MMT; the inset showed the photographs of Na+-MMT powder (left) and CTAB-BPEA-MMT powder (right) under UV irradiation at 365 nm. (B) Normalized fluorescence spectra of CTAB-BPEA-MMT with different loading amount of CTAB (0.1−0.8 g); the inset showed the corresponding fluorescence intensity (emission wavelength at 489 nm). (C) The fluorescence anisotropy diagrams of CTAB-BPEA-MMT with a loading amount of 0.5 g of CTAB. (D) The fluorescence anisotropy values of CTABBPEA-MMT with different loading amount of 0.1−0.8 g of CTAB (emission wavelength at 489 nm).

160 °C for 15 min and then cooled at room temperature to give thin films with a thickness of 0.1 mm. The silicone rubber/ DTAB-Perylene-MMT composite was prepared in the same way. Leica EM UC6 ultramicrotome was used to ultrathinsection the composite films with a diamond knife at −120 °C. The obtained ultrathin sections were then placed on 200-mesh copper grids.

cationic exchange capacity (CECmax). These results demonstrated the successful intercalation of CTAB into the MMT. More importantly, the hydrophilic MMT was transformed into hydrophobic CTAB-MMT by organic modification. The expanded interlayer of CTAB-MMT provided an accommodation for the indissolubly hydrophobic PAHs.36−38 When BPEA was added into CTAB-modified MMT, the fluorescence intensity of the as-prepared CTAB-BPEA-MMT increased with the increasing loading amounts of CTAB until CECmax; however, the fluorescence intensity kept constant when the loading amounts were higher than CECmax (inset of Figure 2B). The normalized fluorescence spectra of CTABBPEA-MMT indicated that the emission at the green-yellow region was blue-shifted with increasing loading amounts of CTAB (Figure 2B). In addition, the absorption of CTABBPEA-MMT at the visible region was depressed (Figure S1). These optical changes of CTAB-BPEA-MMT were mainly ascribed to the strong cation−π interaction between CTAB and BPEA.39,40 At a low loading amount of quaternary ammonium, the cation−π interaction was relatively weak, and thus most of the BPEA molecules were in the coplanar conformation. However, the higher positive charge density could twist phenylethynyl to the perpendicular conformation with an increase in the loading amounts of quaternary ammonium.41−43 Therefore, a gradual blue shift occurred in the fluorescence spectra, while the absorption at the visible region was depressed in the absorption spectra. In comparison, the fluorescence property of BPEA dissolved in CTAB with different weight percents (wt %) in aqueous solution was discussed. CTAB could easily form micelles in aqueous solution when the wt % of CTAB is above 0.034% (its critical micelle concentration is 0.93 mmol/L at 25 °C).44 The fluorescence intensity of the solution increased with an increasing wt % of CTAB because the hydrophobic core of CTAB micelle provided an accommodation for BPEA.45 However, the fluorescence of

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RESULTS AND DISCUSSION Fluorescence Marker of Organoclay. Organoclay is one of the most common inorganic fillers in a polymer matrix.31−34 In addition, it is appropriate for quantification of macrodispersion by conventional CLSM, whose horizontal and vertical resolution is 200 and 500 nm, respectively.35 CTAB was used to modify montmorillonite (MMT) for the preparation of organoclay. Next, 9,10-bis(phenylethynyl)anthracene (BPEA) was selected to make organoclay glow by physical adsorption. Figure 2A showed that the as-prepared CTAB-BPEA-MMT solid powder could generate glaring green fluorescence under the irradiation of ultraviolet light (365 nm); however, no fluorescence was observed for the solid powder of MMT (inset of Figure 2A). The differences between the solid state UV/vis absorption of CTAB-BPEA-MMT and MMT further confirmed the successful attachment of BPEA into the organoclay (Figure S1). The interactions between CTAB and BPEA were investigated in detail. Different amounts of CTAB (0.1−0.8 g) were employed for preparation of the organically modified MMT. The XRD data were used to reveal the changes of interlayer spacing of MMT (Figure S2). The diffraction peak of the natural MMT was located in the range from 2 to 10°, which was attributed to the d-spacing (d001). In general, the (001) peak of CTAB-BPEA-MMT increased with an increasing loading amount of CTAB and reached a maximum at maximum C

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Figure 3. (A) 3D reconstruction image (580 μm × 580 μm × 30 μm) of silicone rubber/organoclay (3 wt %) film taken with a 405 nm laser; right and bottom inset showed the side view of YZ-plane and XZ-plane, respectively. (B) Counting and volume calculation of organoclay particles made up of small voxels accomplished by 3D analysis software. (C) Number percentage of all the organoclay particles in (A) classified by volume.

method is 0.02 μm3. More importantly, the obtained voxels also stored individual space coordinate information for their accurately spatial position. In our experiment, the silicone rubber/CTAB-BPEA-MMT film was fabricated by a conventional mill-mixing method. The volume of an individual oragnoclay particle in the silicone rubber matrix could be obtained by summing the volumes of the voxels in the CLSM system (Figure 3B). Subsequently, the spatial positions and volume data of organoclay particles were acquired by a professional 3D analysis software (Figure 3C). The volume data of a sample indicated that the larger organoclay particle occupied about 0.2% of the whole scanned volume. Additionally, the total volume of all the organoclay particles occupied around 3% of the whole scanned space. Therefore, the observed organoclay particles could be regarded as dots in comparison to the silicone rubber matrix. The centroid data of these dots were provided by the same 3D analysis software and subsequently applied for 3D quantification of macrodispersion. In this Article, the quantification method was performed according to the following three steps: (1) evaluation of the deviation of numbers of centroids in different data sets by Pearson chi-square test; (2) projection of the centroids onto planar coordinate systems (XY-plane, YZ-plane, and XZ-plane) for 2D quantification; (3) projection of the centroids onto one dimension (1D) coordinate system (X-axis, Y-axis, and Z-axis) for numerical characteristics calculation. In order to quantify the overall macrodispersion of organoclay in the silicone film, 24 data sets of centroids were collected from 24 different locations for each sample by CLSM (Figures S7 and S8). For the first step in evaluation of these data sets by the Pearson chi-square test, the macrodispersion of organoclay in the silicone film is homogeneous is posed as the null hypothesis (H0).52−54 For an ideally homogeneous macrodispersion, the existence of a single centroid in each data set has the same probability (Pi) (i.e., Pi = 1/24 in our experiment).55 As a result, the number of centroids in each data set is also same. However, for a real sample, the numbers of centroids in different data sets deviated in a certain range. Therefore, we calculated the chi-

the solution is much lower than CTAB-BPEA-MMT powder because the confined interlayer of MMT could promote the formation of cation−π interaction between CTAB and BPEA (Figure S3).46,47 In addition, fluorescence anisotropy was measured to evaluate the arrangement of the BPEA molecules in the CTAB-BPEA-MMT. The fluorescence anisotropy increased with an increase in the loading amounts of CTAB and kept constant at a loading amount of CEC (Figures 2C and D and S4). The increasingly loading amounts of CTAB led to a higher packing density of organic matter in the MMT platelets. The strong cation−π interaction in a confined space between CTAB and BPEA restricted the rotation speed of BPEA, leading to a higher anisotropy.48,49 Therefore, the strong cation−π interaction between BPEA and CTAB make BPEA as a fluorescent indicator for dispersion of the CTAB-BPEA-MMT fillers in a polymer matrix (Figure 3A). In contrast, the TEM image of the same sample was done for validating the present method. The results showed that only smaller montmorillonite particles were observed (Figure S5). This phenomenon was ascribed to the fact that the microdispersion state of fillers obtained by TEM was usually dependent on the cut cross-section.50 It was concluded that the proposed method can offer the advantages of a wide-view dispersion image with simplicity, sensitivity and high-contrast. In addition, to validate the universality of the proposed method, the cyan-colored organoclay (DTABPerylene-MMT) was successfully synthesized by cointercalation of DTAB and perylene into MMT (Figure S6). In conclusion, organoclay with different colors could be synthesized by cointercalation of different cationic surfactants and PAHs into MMT. Generally, the section scanning and the next 3D reconstruction constitute the two basic steps of CLSM 3Dimaging. The section scanning method collected the images of XY-planes at a series of Z depths for recording the spatial information on a sample. The scanned space could be divided into a large number of voxels (200 nm × 200 nm × 500 nm) for storing fluorescence intensity.51 Therefore, the lower limit of volume of an organoclay filler detected by the present D

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Analytical Chemistry squared test statistic (χ2) to assess the deviation between the observed and theoretical numbers of centroids by eqs 1 and 2:

f

fXY (x , y) =

n

χ2 =

∑

2

(mi − mPi) mPi

i=1

∑ mi i=1

e

(1)

(2)

(4)

The 1D marginal functions f X(x) and f Y(y) are obtained by further marginal integration of the density function f XY(x,y) of (X,Y) for x and y, respectively.

In eqs 1 and 2, mi is the number of centroids in each data set, n is the number of data sets, and m is the sum of all the centroids in all data sets, mPi is the theoretical frequency. Next, the degrees of freedom of {Pi, i = 1, 2, ..., n} should be n − 1, which was determined by the number of data sets and n restriction of probability (∑i Pi = 1).56 Subsequently, χ2 was compared with critical value from the chi-squared distribution with n − 1 degrees of freedom for selecting confidence level. Finally, the null hypothesis could be rejected if the test statistic χ2 exceeded the critical value at the selected confidence level, and thus the alternative hypothesis could be accepted.57−59 In the present system, the macrodispersion of two silicone films (A and B) was quantified by the above established Pearson chi-square test. The computational process was accomplished by MATLAB (script available in ESI) and the calculated χ2 values of A and B were 26.4 and 319.1, respectively. Theoretically, the smaller χ2 value manifests a better homogeneity. Of course, the χ2 value is zero if mi was equal to mPi, indicating the absolute homogeneity. Empirically, 95% is the most common confidence level for rejection or acceptation of H0. The critical value of 95% confidence level is 35.2 according to the standard chi-square distribution table with 23 degrees of freedom. For sample A, the χ2 value is smaller than the critical value and therefore H0 could not be rejected. The null hypothesis was passively accepted that the macrodispersion of sample A was relatively homogeneous. For sample B, H0 was rejected because χ2 value is much larger than critical value and hence the sample B is inhomogeneous. In order to conduct the second and third steps of the proposed quantification method, the rationality of transferring 3D quantification to 2D and 1D quantification was demonstrated by mathematical reasoning. Assuming that a random vector (X,Y,Z) has the joint density function as follows: ⎧1 ⎪ , (x , y , z ) ∈ G f (x , y , z) = ⎨ VG ⎪ ⎩ 0, otherwise

f (x , y , z)dz

⎧ 1 , (x , y) ∈ GXY ⎪ ( b − a )( d − c) =⎨ ⎪ otherwise ⎩ 0,

n

m=

∫

d

f X (x ) =

∫ c

⎧ 1 , x ∈ (a , b) ⎪ fXY (x , y)dy = ⎨ (b − a) ⎪ otherwise ⎩ 0, (5)

b

fY (y) =

∫ a

⎧ 1 , y ∈ (c , d ) ⎪ fXY (x , y)dx = ⎨ (d − c) ⎪ otherwise ⎩ 0, (6)

Similarly, the density functions f XZ(x,z), f YZ(y,z), and f Z(z) are achieved in the same way ⎧ 1 , (x , z) ∈ GXZ ⎪ fXZ (x , z) = ⎨ (b − a)(f − e) ⎪ otherwise ⎩ 0,

(7)

⎧ 1 , (y , z) ∈ GYZ ⎪ ( d − c )( f − e) ⎨ fYZ (y , z) = ⎪ otherwise ⎩ 0,

(8)

⎧ 1 , y ∈ (e , f ) ⎪ fZ (z) = ⎨ (f − e) ⎪ otherwise ⎩ 0,

(9)

Obviously, we could get the following conclusion based on eqs 3−9: f (x , y , z) = fX (x)fY (y)fZ (z) fXY (x , y) = fX (x)fY (y)

(3)

fXZ (x , z) = fX (x)fZ (z)

The region of cuboid G could be written as

fYZ (y , z) = fY (y)fZ (z)

G = {(x , y , z)|a < x < b , c < y < d , e < z < f }

From the above discussion, it is obvious that the vector (X,Y,Z) obeys the uniform distribution in the cuboid G and the random variables X, Y, Z are independent and obey the uniform distribution in their own regions, respectively. In addition, random vectors, (X,Y), (Y,Z), and (X,Z) obey the uniform distribution in their own regions, respectively. In conclusion, the 3D quantification of macrodispersion could be evaluated by projection of the centroids onto 2D and 1D coordinate system. The centroids were projected onto 2D coordinate systems (XY-plane, YZ-plane, and XZ-plane) for direct comparison of macrodispersion between A and B. The number density of

The volume (VG) could be written as VG = (b − a)(d − c)(f − e)

Hence, the vector (X,Y,Z) obeys the uniform distribution in the cuboid G. The probability of vector (X,Y,Z) at any position within the cuboid G is same. The density function of 2D random vector and 1D random variable is obtained by marginal integration of the joint density function. The density function f XY(x,y) of (X,Y) is obtained by marginal integration of Z on the interval (e,f) as follows: E

DOI: 10.1021/acs.analchem.7b03575 Anal. Chem. XXXX, XXX, XXX−XXX

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X-axis value were classified into MVx and CVx, respectively. The similar classification was suitable for MVy, CVy, MVz, and CVz. Subsequently, MV, SD and CV values of MVx, CVx, MVy, CVy, MVz, and CVz were calculated in Table S7. The MV of MVx from sample A and sample B were 287 and 300, respectively. As depicted in Figure 3A, the space size of each data set was 580 μm × 580 μm × 30 μm, and thus the data range of X-axis values should be [0, 580]. When the macrodispersion of centroids was homogeneous, the probability of existence of a single centroid at any position in [0, 580] was same. When the data range was equally divided into several parts, the number of centroids in each part was anticipated to be the same. Figure 5 showed that

centroids at any position of the 2D coordinate system (XYplane, YZ-plane, and XZ-plane) was anticipated to be the same if the macrodispersion was homogeneous. If not, the sample was considered to be inhomogeneous when the number densities of centroids varied greatly at different positions of the projection graphs. For sample A, the number density of centroids at any position in XY-plane was nearly constant and therefore the macrodispersion of A was relatively homogeneous. For sample B, the number densities of centroids in the indicated regions were much higher than other regions and thus the distribution of centroids was inhomogeneous in XY-plane (Figure 4). Therefore, the 3D macrodispersion of sample B was

Figure 4. Projection of the centroids onto XY-plane. (A and B) Six data sets of samples A and B. The indicated regions of sample B showed higher number density of centroids than other regions.

Figure 5. Projection of the centroids onto X-axis. The X-axis was divided into seven equal parts and the number of centroids in each part was counted. The dotted line indicated the ideally average number. (A and B) Six data sets of samples A and B.

considered to be inhomogeneous. The similar results could be obtained for A and B by projecting centroids onto YZ-plane and XZ-plane (Figures S9 and S10). In the third step, the centroids were projected onto 1D coordinate systems (X-axis, Y-axis, and Z-axis) for dispersion degree assessment. The coefficient of variation (CV) is a dimensionless parameter for a standardized measurement of dispersion degree of frequency distribution.60,61 Herein, CV values of centroids projected on 1D coordinate systems were calculated for quantification of macrodispersion by eq 10 σ CV = μ (10)

the data range [0, 580] was equally divided into seven parts and the number of centroids in each part was counted in a histogram. Apparently, the number of centroids in each part fluctuated around the ideally average value (one-seventh of the total number of centroids in a data set). The smaller fluctuation degree indicated the distribution of centroids on X-axis was more homogeneous. Therefore, the smaller fluctuation degree of sample A than sample B indicated the macrodispersion of A was better. More importantly, when the distribution of centroids on X-axis was homogeneous, the ideal MV of X-axis value in each data set was anticipated to be 290. Consequently, the MV of MVx was anticipated to be 290. The MV of MVx from sample A is closer to the ideal MV than sample B, indicating the macrodispersion of sample A was closer to homogeneous dispersion. In addition, the SD and CV were important parameters to reflect the fluctuation of a series of

In which, σ and μ are the standard deviation (SD) and mean value (MV) of the data, respectively. The MV, SD, and CV of the X-axis, Y-axis, and Z-axis values of the centroids in 24 data sets were first calculated for further quantification of macrodispersion (Tables S1−6). The obtained 24 MV and 24 CV of F

DOI: 10.1021/acs.analchem.7b03575 Anal. Chem. XXXX, XXX, XXX−XXX

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data. The smaller SD and CV of MVx from sample A indicated the 24 MV from sample A were fluctuated in a narrower range, meaning that the marcodispersion of A was better than B. Afterward, the statistical analysis of CV of X-axis value was conducted for quantifying the macrodispersion. Basically, high CV of X-axis value indicated the dispersion degree of the X-axis value was large, meaning the distribution of centroids on X-axis was inhomogeneous. However, the fluctuation degree of the 24 CV from a sample was a more important indicator for evaluating the whole macrodispersion of a sample. The difference between MV of the CVx from sample A and sample B was insignificant. Nonetheless, the smaller SV and CV of the CVx from sample A demonstrated the 24 CV from A was fluctuated in a narrower range, demonstrating the macrodispersion of A was better than B. The similar conclusion could be obtained by statistically analyzing Y-axis and Z-axis values (Table S7 and Figures S11 and S12). In summary, the proposed method was successfully applied to quantitatively distinguish the similar dispersion of inorganic fillers in two films, which was difficultly identified by a qualitative method.

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CONCLUSIONS

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ASSOCIATED CONTENT

Article

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel./Fax: +86 10 64411957. ORCID

Chao Lu: 0000-0002-7841-7477 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by National Basic Research Program of China (973 Program, 2014CB932103), the National Natural Science Foundation of China (21375006, 21656001, 21521005, and 21575010), and Innovation and Promotion Project of Beijing University of Chemical Technology.

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REFERENCES

(1) Balazs, A. C.; Emrick, T.; Russell, T. P. Science 2006, 314, 1107− 1110. (2) Pavlidou, S.; Papaspyrides, C. D. Prog. Polym. Sci. 2008, 33, 1119−1198. (3) Paul, D. R.; Robeson, L. M. Polymer 2008, 49, 3187−3204. (4) Bright, C. J.; Nallon, E. C.; Polcha, M. P.; Schnee, V. P. Anal. Chem. 2015, 87, 12270−12275. (5) Mackay, M. E.; Tuteja, A.; Duxbury, P. M.; Hawker, C. J.; Van Horn, B.; Guan, Z.; Chen, G.; Krishnan, R. S. Science 2006, 311, 1740−1743. (6) Vaia, R. A.; Maguire, J. F. Chem. Mater. 2007, 19, 2736−2751. (7) Yang, F.; Zhang, X. X.; Song, L.; Cui, H.; Myers, J. N.; Bai, T.; Zhou, Y.; Chen, Z.; Gu, N. ACS Appl. Mater. Interfaces 2015, 7, 9410− 9419. (8) Scott, M. C.; Chen, C.-C.; Mecklenburg, M.; Zhu, C.; Xu, R.; Ercius, P.; Dahmen, U.; Regan, B. C.; Miao, J. W. Nature 2012, 483, 444−448. (9) Van Aert, S.; Batenburg, K. J.; Rossell, M. D.; Erni, R.; Van Tendeloo, G. Nature 2011, 470, 374−377. (10) Yu, Y.; Xin, H. L.; Hovden, R.; Wang, D.; Rus, E. D.; Mundy, J. A.; Muller, D. A.; Abruna, H. D. Nano Lett. 2012, 12, 4417−4423. (11) Midgley, P. A.; Dunin-Borkowski, R. E. Nat. Mater. 2009, 8, 271−280. (12) Van Tendeloo, G.; Bals, S.; Van Aert, S.; Verbeeck, J.; Van Dyck, D. Adv. Mater. 2012, 24, 5655−5675. (13) Kovalchuk, A.; Huang, K.; Xiang, C.; Marti, A. A.; Tour, J. M. ACS Appl. Mater. Interfaces 2015, 7, 26063−26068. (14) Yang, B.; Treweek, J. B.; Kulkarni, R. P.; Deverman, B. E.; Chen, C.-K.; Lubeck, E.; Shah, S.; Cai, L.; Gradinaru, V. Cell 2014, 158, 945− 958. (15) Ameloot, R.; Vermoortele, F.; Hofkens, J.; De Schryver, F. C.; De Vos, D. E.; Roeffaers, M. B. J. Angew. Chem., Int. Ed. 2013, 52, 401−405. (16) Conchello, J.-A.; Lichtman, J. W. Nat. Methods 2005, 2, 920− 931. (17) Fernandez, R.; Das, P.; Mirabet, V.; Moscardi, E.; Traas, J.; Verdeil, J.-L.; Malandain, G.; Godin, C. Nat. Methods 2010, 7, 547− 553. (18) Carlsson, K.; Danielsson, P. E.; Lenz, R.; Liljeborg, A.; Majlof, L.; Aslund, N. Opt. Lett. 1985, 10, 53−55. (19) Carlsson, K.; Aslund, N. Appl. Opt. 1987, 26, 3232−3238. (20) Turro, N. J. Modern Molecular Photochemistry; University Science Books: Sausalito, CA, 1991. (21) Saigusa, H.; Lim, E. C. Acc. Chem. Res. 1996, 29, 171−178. (22) Guan, W.; Wang, S.; Lu, C.; Tang, B. Z. Nat. Commun. 2016, 7, 11811−11817. (23) Ma, J. C.; Dougherty, D. A. Chem. Rev. 1997, 97, 1303−1324. (24) Mahadevi, A. S.; Sastry, G. N. Chem. Rev. 2013, 113, 2100− 2138. (25) Dougherty, D. A. Acc. Chem. Res. 2013, 46, 885−893.

In summary, using PAH-modified organoclay as the inorganic fillers in a silicone rubber matrix, we have demonstrated that CLSM could be used to collect the information on geometry and spatial macrodispersion of organoclay in a polymer matrix. Such information is evaluated by the Pearson chi-square test, planar distribution evaluation, and numerical characteristics calculation, allowing quantification of 3D macrodispersion of organoclay in the range of the hundreds of micrometers. Our quantification method is able to become a standard of macrodispersion quantification for enterprises involved in organic−inorganic composite. The generality of the established quantification method could be expanded to quantify the dispersion of positively charged fillers (e.g., layered double hydroxides) and even neutral fillers (e.g., silica particles) by making these fillers glow through tuning appropriate fluorescent substances.

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.7b03575. Absorption spectra of Na+-MMT and CTAB-BPEAMMT; powder XRD patterns of Na+-MMT and CTABBPEA-MMT; fluorescence property of BPEA dissolved in CTAB aqueous solution; fluorescence anisotropy of CTAB-BPEA-MMT; TEM images of CTAB-BPEAMMT fillers in silicone rubber; fluorescence spectra of DTAB-Perylene-MMT and 3D reconstruction images of silicone rubber/DTAB-Perylene-MMT film; 3D reconstruction images of sample A and sample B; projection of the centroids onto YZ-plane and XZ-plane; projection of the centroids onto Y-axis and Z-axis; 24 data sets of Xaxis, Y-axis, and Z-axis values of centroids from sample A and sample B; numerical characteristics calculation of Xaxis, Y-axis, and Z-axis values; MTALAB script for macrodispersion quantification (PDF) G

DOI: 10.1021/acs.analchem.7b03575 Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry (26) Dougherty, D. A. Science 1996, 271, 163−168. (27) Kuwahara, Y.; Nishizawa, K.; Nakajima, T.; Kamegawa, T.; Mori, K.; Yamashita, H. J. Am. Chem. Soc. 2011, 133, 12462−12465. (28) Keiluweit, M.; Kleber, M. Environ. Sci. Technol. 2009, 43, 3421− 3429. (29) Chen, W.; Elfeky, S. A.; Nonne, Y.; Male, L.; Ahmed, K.; Amiable, C.; Axe, P.; Yamada, S.; James, T. D.; Bull, S. D.; Fossey, J. S. Chem. Commun. 2011, 47, 253−255. (30) Qu, X. L.; Liu, P.; Zhu, D. Q. Environ. Sci. Technol. 2008, 42, 1109−1116. (31) Paul, D. R.; Mark, J. E. Prog. Polym. Sci. 2010, 35, 893−901. (32) Kiliaris, P.; Papaspyrides, C. D. Prog. Polym. Sci. 2010, 35, 902− 958. (33) Zha, C.; Wang, W.; Lu, Y.; Zhang, L. ACS Appl. Mater. Interfaces 2014, 6, 18769−18779. (34) Liu, R.; Chen, Y.; Cao, J. ACS Appl. Mater. Interfaces 2016, 8, 161−168. (35) Paddock, S. W. Mol. Biotechnol. 2000, 16, 127−149. (36) Yaron-Marcovich, D.; Chen, Y.; Nir, S.; Prost, R. Environ. Sci. Technol. 2005, 39, 1231−1238. (37) He, H.; Frost, R. L.; Bostrom, T.; Yuan, P.; Duong, L.; Yang, D.; Xi, Y.; Kloprogge, J. T. Appl. Clay Sci. 2006, 31, 262−271. (38) Zhao, Q.; Burns, S. E. Environ. Sci. Technol. 2013, 47, 2769− 2776. (39) Rashkin, M. J.; Hughes, R. M.; Calloway, N. T.; Waters, M. L. J. Am. Chem. Soc. 2004, 126, 13320−13325. (40) Lu, Q.; Oh, D. X.; Lee, Y.; Jho, Y.; Hwang, D. S.; Zeng, H. Angew. Chem., Int. Ed. 2013, 52, 3944−3948. (41) Terashima, T.; Nakashima, T.; Kawai, T. Org. Lett. 2007, 9, 4195−4198. (42) Zhu, A.; White, J. O.; Drickamer, H. G. J. Phys. Chem. A 2002, 106, 9209−9212. (43) Demeter, A. J. Phys. Chem. A 2014, 118, 9985−9993. (44) Cifuentes, A.; Bernal, J. L.; Diez-Masa, J. C. Anal. Chem. 1997, 69, 4271−4274. (45) Long, J. A.; Rankin, B. M.; Ben-Amotz, D. J. Am. Chem. Soc. 2015, 137, 10809−10815. (46) Zhao, Q.; Burns, S. E. Environ. Sci. Technol. 2012, 46, 3999− 4007. (47) Zhou, Q.; Zhu, R.; Parker, S. C.; Zhu, J.; He, H.; Molinari, M. RSC Adv. 2015, 5, 47022−47030. (48) Vasudevan, D.; Arey, T. A.; Dickstein, D. R.; Newman, M. H.; Zhang, T. Y.; Kinnear, H. M.; Bader, M. M. Environ. Sci. Technol. 2013, 47, 14119−14127. (49) Hou, X.-J.; Li, H.; He, P.; Li, S.; Liu, Q. J. Phys. Chem. A 2015, 119, 11199−11207. (50) Sinha Ray, S.; Okamoto, M. Prog. Polym. Sci. 2003, 28, 1539− 1641. (51) Yio, M. H. N.; Mac, M. J.; Wong, H. S.; Buenfeld, N. R. J. Microsc. 2015, 258, 151−169. (52) Greenwood, P. E.; Nikulin, M. S. A Guide to Chi-Squared Testing; Wiley: New York, 1996. (53) Wasserman, L. All of Statistics: A Concise Course in Statistical Inference; Springer-Verlag Inc., New York, 2004. (54) Plackett, R. L. Int. Stat. Rev. 1983, 51, 59−72. (55) Field, A. Discovering Statistics Using IBM SPSS Statistics; SAGE Publications Ltd.: London, 2013. (56) Gelman, A. Int. Stat. Rev. 2003, 71, 369−382. (57) Anderson, D. R.; Burnham, K. P.; Thompson, W. L. J. Wildl. Manage. 2000, 64, 912−923. (58) Frick, R. W. Mem. Cognit 1995, 23, 132−138. (59) Lehmann, E. L. Testing Statistical Hypothesis; Springer-Verlag: New York, 1991. (60) Winer, B. J.; Brown, D. R.; Michels, K. M. Statistical Principles in Experimental Design; McGraw-Hill: New York, 1991. (61) Reed, G. F.; Lynn, F.; Meade, B. D. Clin. Diagn. Lab. Immunol. 2002, 9, 1235−1239.

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DOI: 10.1021/acs.analchem.7b03575 Anal. Chem. XXXX, XXX, XXX−XXX