Growth of Cuprous Oxide Particles in Liquid-Phase Synthesis

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Growth of cuprous oxide particles in liquid-phase synthesis investigated by X-ray laser diffraction Tomotaka Oroguchi, Takashi Yoshidome, Takahiro Yamamoto, and Masayoshi Nakasako Nano Lett., Just Accepted Manuscript • DOI: 10.1021/acs.nanolett.8b02153 • Publication Date (Web): 10 Jul 2018 Downloaded from http://pubs.acs.org on July 13, 2018

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Growth of cuprous oxide particles in liquid-phase synthesis investigated by X-ray laser diffraction

AUTHOR NAMES Tomotaka Oroguchi a,b, Takashi Yoshidome c, Takahiro Yamamoto a,b and Masayoshi Nakasako a,b,*

AUTHOR ADDRESS a

Department of Physics, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi,

Kohoku-ku, Yokohama, 223-8522 Japan b

c

RIKEN SPring-8 Center, 1-1-1 Kohto, Sayo, Sayo-gun, Hyogo 679-5148 Japan

Department of Applied Physics, Graduate School of Engineering, Tohoku University, 6-6-05,

Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan.

KEYWORDS Cuprous oxide; liquid-phase synthesis; X-ray diffraction imaging; X-ray free electron laser

ACS Paragon Plus Environment

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ABSTRACT Cuprous oxide (Cu2O) particles obtained by surfactant-assisted liquid-phase synthesis have cuboid shapes, but the internal structures are difficult to be visualized by electron microscopy. Herein, we investigated the internal structures of numerous individual Cu2O particles with submicrometer dimensions by X-ray diffraction imaging (XDI) using X-ray free-electron laser (XFEL) pulses. The reconstructed two-dimensional electron density maps, which displayed inhomogeneous internal structures, were divided into five classes characterized by the positions and shapes of high and low electron density areas. Further analysis of the maps in each class by a manifold learning algorithm revealed that the internal structures of Cu2O particles varied in correlation with total electron density while retaining the characteristics within each class. Based on the analyses, we proposed a growth mechanism to yield the inhomogeneity in the internal structures of Cu2O particles in surfactant-mediated liquid-phase synthesis.

Metal nanoparticles and particles with sub-micrometer dimensions impart unique properties compared to the bulk solid1 and have been utilized in imaging2, sensing3, photonics4, and biomedical5 applications. As their functions are correlated with their shapes, sizes, and internal structures, quality control with respect to these factors is necessary for exploring the use of these particles6,7. Small particles are often produced by surfactant-assisted liquid-phase synthesis. The synthesis method has been widely used because of the simple procedure to mix solutions of reagents8. The sizes, shapes, and internal structures are necessary to be visualized to address not only the structural basis of the properties but also the mechanism of synthesis9. However, studies on the internal structures of particles larger than 50 nm are still in progress. Scanning electron microscopy (SEM) is a useful tool to measure the size of particles,


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but it provides little information on their internal structures. Furthermore, while both shape and internal structure can be visualized using transmission electron microscopy10, particles larger than about 50 nm are opaque due to absorption and multiple scattering of the electrons. On the other hand, due to the penetration power of X-rays with short wavelengths into specimens, XDI11,12 is suitable for visualizing the shape, size, and internal structure of larger particles as demonstrated in structure analyses of various particles by using synchrotron X-rays1315

. Moreover, the recent implementation of very intense and spatially coherent XFEL pulses

provided at a high repetition rate enables diffraction patterns to be rapidly collected from numerous individual particles16. In this way, a large number of electron density maps can be retrieved, which allows statistical analyses to be conducted with regard to the internal structures of the particles, as well as their shapes and sizes17,18. In this study, we investigated the internal structures of Cu2O particles by XFEL-XDI experiments (Fig. 1) and discuss their growth during surfactant assisted liquid-phase synthesis. Cu2O19-21, known as a p-type semiconductor,22 is utilized in gas sensing23 and medical applications24, and as an electrode material for lithium ion batteries25. In liquid-phase synthesis assisted by the surfactant sodium dodecyl sulfate (SDS), the majority of Cu2O particles grow with cuboid shapes19 (Fig. 1(a)). It was proposed that the nucleation of granular particles of 40 nm is initiated from seed particles of 10–15 nm and then the homogeneous association of the granules is progressively repeated to develop the surfaces into a cubic shape19 (Fig. 1(a)). This continues until the particles reach several hundred nanometers in size. However, without visualization of the internal structures, the internal growth process is unclear. XFEL-XDI. Cu2O particles were synthesized under aqueous conditions by mixing copper sulfate, SDS, sodium ascorbate, and sodium hydroxide solutions according to a procedure


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described previously19. A suspension of Cu2O particles was poured on Si3N4 membranes (Norcada, Canada). After adsorption of the particles onto the membranes (approximately 5 min), the reaction buffer and non-adsorbed particles were washed out by soaking the membranes into distilled water and blotting off excess amount of water by wick papers26. As the SEM observation shown in Fig. 1(a), a number of Cu2O nanoparticles were randomly dispersed, and adsorbed on the Si3N4 membrane by one of six facets. Diffraction patterns were collected using the diffraction apparatus16 at BL327 of SACLA28. We used focused XFEL pulses with a 10-fs duration, which were provided at a repetition rate of 30 Hz. They had almost complete spatial coherence29 and an ultimately strong intensity (more than 1010 X-ray photons/4 µm2/pulse)30 at a photon energy of 5.5 keV (X-ray wavelength of 0.225 nm). Although a focused XFEL pulse destroys a specimen particle at the atomic level, diffraction occurs from the particle before its destruction, as demonstrated by a crystal structure analysis of a protein complex by using XFEL pulses at BL3 of SACLA31. In fact, within the duration, atoms composing the protein complex in the irradiation area of crystals were located at the nearly same positions with that before the incidence of a single XFEL pulse. Therefore, a large number of diffraction patterns of intact Cu2O particles can be collected, pulse-by-pulse, by supplying fresh particles into the irradiation area3 (Fig. 1(b)). Diffraction patterns of Cu2O particles were collected by scanning the Si4N3 membranes, at a speed of 25 µm / 33 ms against the focused XFEL pulses16. The diffraction patterns, each of which was 20 MB ((4 bytes per pixel) × (512 × 1024 )× 10 CCD panels), were recorded using two multi-port charge-coupled device (MPCCD) detectors32, and were processed by a data processing software18,33. We collected 19019 diffraction patterns, which displayed the signal-to-noise ratio better than 3 at a resolution of 50 nm. For the subsequent structure analysis, we selected 1637 single-


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particle diffraction patterns, and discarded patterns from aggregates, which were distinctly different from the flare patterns of single particles. Fig. 1(c) shows typical XFEL diffraction patterns from individual Cu2O particles. The patterns were composed of crossed flares, which can be approximated as Fraunhofer diffractions by rectangular apertures18,34 with sizes of 100– 1000 nm. These diffraction patterns indicated that the cuboid shaped particles were adsorbed on the Si3N4 membrane by one of the facets as observed by SEM (Fig. 1(a)). The period of the fringe patterns along the flare correlate to the size of the particles. They were also accompanied by complicated interference patterns in the off-flare regions, which were caused by the internal structures (Fig. 1(c) and Supplementary Fig. S1). Classification of electron density maps. The electron density of a particle, projected along the direction of the incident X-ray, was reconstructed from each diffraction pattern alone by using the phase retrieval (PR) algorithm35. The most probable projection electron density maps were selected by a protocol proposed previously36,37. The projection maps (resolution of 25 nm) revealed inhomogeneous internal structures (Fig. 1(c) and Fig. S2), with a pronounced presence of high-density regions, in which the density was more than twice that of the facets. The internal structures of the projection maps varied independently of size, and there were some common characteristics regarding the positions and shapes of the major density peaks. The previously proposed mechanism19, which assumes the homogeneous growth of Cu2O particle, is insufficient to explain why high and low density regions appears with the variety in the positions, shapes and sizes. Therefore, with the aim of elucidating the causes of this variation, we classified the projection maps (Fig. 2(a)). Prior to the classification, the variation of the density distribution was emphasized by subtracting the averaged values. After the normalization of the size to 18 × 18 pixels, the maps were rotationally aligned so that one of four edges fitted to


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an edge of a reference map. After the alignment, the density values of pixels were recalculated by their proportional distribution of the previous pixels. These two processes were carried out computationally. Then, we selected the rotation, which gave the optimal overlap of the highest density region of the map to that of the reference, among four possible rotations. Finally we manually classified them by inspecting both the shapes and positions of the high and low density regions. Most of the population was classed as α, β, or γ, and the rest were classed as δ or ε. The density maps in each class were further divided into four subclasses (sub1–sub4), by inspecting the variation in the size of the major density peaks and the appearance of minor peaks (Fig. 2(a)). As an example of careful classification, we distinguished subclass 4 in class α and subclass 4 class ε. The two maps were similar at a glance, but displayed the substantial difference with respect to the shapes and positions of their low density regions. Each class-α map had a single circular density peak near a corner, and additional density peaks situated near the edges or corners. Class-β maps had a predominant bell-shaped density peak, and an additional high-density stripe parallel to the bell. These classes could be interpreted as different views of the same-shaped particles. The bell-shaped peaks in class-β maps correspond to the circular densities in class-α maps, and the additional stripes to the minor peaks, forming particles designated as α/β (Fig. 2(a)). Class-γ and class-δ maps had triangular- and Lshaped high-density regions, respectively, radiating from a corner. Class-ε maps were characterized by wide stripes along the edges and additional high density regions appeared as spots or stripes near the middle of two edges. The class-γ map could be interpreted as a particle with a γ or ε facet, and the class-δ map as that with a δ or ε facet (Fig. 2(a)).


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Classes α, β, and γ had size distributions with maxima at around 300 nm, while those of classes δ and ε were around 400 nm (Fig. 2(b)). In classes α, β, and γ, the size distribution of each subclass resembled that of the whole (Figs. S3(a)-(c)), indicating that the variation in the internal structures was size-independent. There was a strong correlation between the size distributions of classes α and β, which supported the idea that they were different views of the same class particles (α/β) (Fig. 2(c)). In contrast, the structural relationships among classes γ, δ, and ε were not clarified, due to the absence of correlations among the size distributions (Figs. S3(d)-(f)). Diffusion map analysis. For further quantitative analysis regarding the variation of the internal structures, maps in each class were compared with respect to their mutual similarity, by using the diffusion map (DM)38,39 algorithm for manifold learning40 (Figs. 3 and S4). Prior to applying the DM method, the transition probability from i-th to j-th maps, Pi → j , is calculated as

Pi → j =

(

exp −Tij σ

∑

(

)

exp −Tij σ

)

,

j

Tij is a score to measure the similarity between the i-th and j-th maps, and is defined as

∑ ρ ( x, y ) − ρ ( x, y ) i

Tij =

j

x ,y

∑ ρ ( x, y ) + ρ ( x, y ) i

,

j

x ,y

where ρi ( x , y ) is the density value at pixel ( x , y ) 37. This score reflects the similarity of maps more sensitively than the correlation coefficient as reported37 and good for measuring the transition probability between maps within each class. σ is the standard deviation controlling the transition probability and is determined by inspecting the distribution of the Tij values (Fig.


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S4)40. The transition probability is normalized as

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∑P

i→ j

= 1 . The eigenvalues and eigenvectors

i

were calculated from the transition probability matrix P (Fig. S4)40. The distribution of the maps was visualized as a manifold in the space spanned by the eigenvectors with the first, second, and third largest eigenvalues. In each class, the maps were distributed within a boomerang-shaped corridor (manifold) in a space spanned by the first, second, and third eigenvectors (Fig. 3 and Fig. S5), except a few outliers, which displayed extremely large values of eigenvector values. The maps in each class were distributed on the corridor in the order of the subclasses, which were simply characterized by the normalized total electron densities of the maps. Therefore, the distribution of the maps on the corridor implied that the Cu2O particles varied the total electron density with the internal structures retaining the structural characteristics, such as the shapes and positions of high and low density regions. Growth mechanism. Based on the structural analyses and classification, we propose a growth step, which would explain the class-dependent and inhomogeneous development of the internal structures (Fig. 4), in the hypothetical mechanism (Fig. 1(a)). As high-density regions vary size-independently (Fig. 2(b)) and develop class-dependently (Fig. 3), small building blocks would be heterogeneously formed by the association of granules, and initiate irreversible and anisotropic development of the high-density regions (Fig. 4). The population of the classes (Fig. 2(b)) indicates a major growth pathway building from a facet (classes α and β), and a minor pathway from a corner or an edge (classes γ, ε, and δ) (Fig. 2(a)). Although little information on the fine structure of the low density region is available, the possible composition of the lowdensity regions inside the cuboid can be speculated. One of possible components is aggregates of granules associated with SDS molecules, because SDS micelles surrounding the granules


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contribute to the low density (Fig. 4). An irregular distribution of Cu2O in loosely packed structures, and/or holes and inclinations between Cu2O particles could also contribute to low electron density regions. In spite of the class-dependent variation in the internal structures, sharp facets were commonly observed in the projection maps. This indicates that the formation of the facets would simultaneously occur with the growth of the building blocks, until the enclosure of the six facets. Once the enclosure is size-independently and stochastically complete, the escape of granules, surfactant molecules and Cu2O particles from the interior of the particles is prohibited. Thus, in the interior of the particles, the dissociation/association of surfactant molecules with granules would be in equilibrium, which would further suppress the growth of the high-density regions, because the removal of surfactant molecules from the surface of the blocks and granules is necessary for their association. This scheme explains partly why the size distributions of subclasses resemble each other. The growth speeds of the facets and the high-density regions would be a predominant factor to determine the sizes of particles and their internal structures. Future prospect. From diffraction patterns obtained by the XFEL-XDI experiment, the present study addressed the growth of Cu2O particles by analyzing the variety in the internal structures of a large number of particles. Structural analyses at higher resolution are necessary to investigate the proposed growth mechanism of Cu2O particles. In addition, due to the intensity of the XFEL pulses, the study was limited to particles with dimensions larger than 100 nm. When XFEL pulses with intensities one order stronger than those of this study are available, the scheme presented here would be applicable to the structural analyses of particles smaller than 100 nm. Regarding the DM analysis, the following two problems will be improved. Firstly it was difficult to interpret the distribution of maps of all classes as a manifold. The use of other


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measure such as proposed recently may improve this problem41. Secondly the number of maps in each class was still small. By increasing the number, the distribution of all maps could be interpreted as a manifold, and then more fine visualization of growth pathway may be possible as demonstrated in the manifold analysis of a target with a large number of samples42.

ASSOCIATED CONTENT Supporting Information The supporting information is available free of charge on the ACS publication website. Examples of diffraction patterns obtained by an XFEL-XDI experiment, Representative electron density maps retrieved from diffraction patterns, Size distribution of subclasses in γ, δ and ε, details of the DM analysis, and the manifold of projection maps in classes β, δ and ε (PDF)

AUTHOR INFORMATION Corresponding Author *

E-mail: [email protected]. Phone: +81-45-566-1713.

Notes The authors declare no competing financial interest

ACKNOWLEDGEMENTS This study was supported by a grant for XFEL key technology, and the X-ray Free Electron Laser Priority Strategy Program, Grant-in-Aid for Scientific Research on Innovative Areas (Nos.


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jp23120525, jp25120725, jp15H01647, jp24113723, jp26104535) from the Ministry of Education, Culture, Sports, Science and Technology. This study was also supported by Grants from the Japan Society for the Promotion of Science (Nos. jp24654140, jp1920402, and jp16H02218). The experiments were performed at SACLA with the approval of Japan Synchrotron Research Institute (proposal Nos. 2016A8048, 2016B8064, 2017A8015, and 2017B8003). The PR calculations and multivariate analyses were performed using the mini-K supercomputer system at the SACLA facility.

The authors thank to the members of SACLA

engineering team for their help in the tuning of beamline optics. The authors also thank to Dr. Sekiguchi of Keio University for his contribution in the early stage of this work.


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Schwander, P.; Fung, R.; Philips, G. N.; Ourmazd, A. New J. Phys. 2010, 12, 035007.


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FIGURE CAPTIONS Figure 1 The surfactant-assisted synthesis of Cu2O particles and XFEl-XDI experiment. (a) Hypothetical mechanism of the growth of Cu2O particles in surfactant-assisted liquid-phase synthesis; and a SEM image of Cu2O particles absorbed on a Si3N4 membrane. (b) Schematic illustration of XFEL-XDI experiments at SACLA. A specimen disk of adsorbed Cu2O particles is scanned by XFEL pulses provided at a repetition of 30 Hz. (c) Typical diffraction patterns from Cu2O particles of different size and internal structure. The magnitude of the scattering vector is defined as S = 2sinθ λ , where 2θ is the scattering angle, and λ is the X-ray wavelength. The insets are the projection electron density maps of Cu2O particles retrieved from the diffraction patterns. The scale bar indicates 500 nm.

Figure 2 Classification of internal structures. (a) Five classes, α, β, γ, δ, and ε, of the maps after a manual classification. The number of maps in each class is shown in parenthesis. The maps are further divided into subclasses (sub1–sub4) with respect to the size of the high-density regions. The relation between classes α and β, and the possible relation among classes γ, δ, and ε, are shown. (b) Size distribution in the five classes (top), subclasses in class α (center), and class β (bottom). (c) Correlation between the size distributions of classes α and β. The correlation coefficient regarding the frequency of the sizes was 0.99. For the major subclasses 3 of both classes, the correlation coefficient was 0.91. In other minor classes, the coefficients were in the range of 0.77-0.81.


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Figure 3 DM analysis of internal structures. Distributions of projection maps in classes α (a) and γ (b) in pairs of planes spanned by the eigenvectors with the first, second, and third largest eigenvalues in the DM analysis. The distributions in the other classes are shown in Supplementary Figure S3. The maps are represented as dots, the colors of which are distinguished according to the subclass scheme in Fig. 2(a). Representative density maps of the four subclasses are shown.

Figure 4 Schematic illustration of the growth of Cu2O particles based on the variation in the internal structures (see the main text).


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Figure 1


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Figure 2


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Figure 3


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Figure 4


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Growth of Cuprous Oxide Particles in Liquid-Phase Synthesis

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