Anal. Chem. 2010, 82, 2599–2607
Visualizing Materials Chemistry at Atomic Resolution Sergio I. Sanchez, Matthew W. Small, Shankar Sivaramakrishnan, Jian-guo Wen, Jian-Min Zuo, and Ralph G. Nuzzo University of Illinois Urbana-Champaign
Materials’ properties depend on the interplay of structure with the molecular, electronic, and atomic dynamics of a system. For example, adhesion is a complex physical property in which interfacial/molecular interactions are deeply convolved with both higher levels of structure and dynamics. Properties related to electronic structure also depend on features beyond composition alone, manifesting in, for example, unique impacts due to quantum confinement (e.g., quantum dots). In such cases, complex interactions connect structure, bonding, composition, and dynamics to functional properties. For this reason, progress in materials chemistry requires an expanded focus that goes beyond chemical reactions to systems of bonding and hierarchical interactions for specific, functional outcomes. To support this goal, the analytical methods used to characterize nanoscale materials are of great importance. In this Feature, we discuss recent advances in analytical electron microscopy (AEM) that promise to significantly transform conventional wisdom in materials chemistry and, in particular, nanochemistry. We describe advances in AEM, notably the development of high-resolution aberration-corrected (Cs-corrected) electron optics, which will broadly expand the understanding of structure-property and structure-activity relationships in nanoscience. Progress is illustrated with works combining atomic resolution imaging, diffraction, and high spatial and energy resolution imaging spectroscopy with theory-based modeling to quantitatively characterize the atomic and electronic structure of nanoscale materials with unprecedented precisionsthat is, to see all aspects of their structure at the atomic level. 10.1021/ac902089f 2010 American Chemical Society Published on Web 02/19/2010
ALEX JEREZ, IMAGING TECHNOLOGY GROUP AT THE BECKMAN INSTITUTE, UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN
Analytical electron microscopysempowered by advances in electron optics and detectorssis poised to radically transform our understanding of the complex phenomena arising from atomic and electronic structure in materials chemistry. (To listen to a podcast about this article, please go to the Analytical Chemistry multimedia page at pubs.acs.org/page/ancham/audio/index.html.)
THE ELECTRON MICROSCOPE LENS AND ABERRATION CORRECTION The primary purpose of a microscope is to magnify objects to observe details too small to discern with the naked eye. Limitations in the resolution of light microscopes ultimately motivated the development of the electron microscope. In the 1930s, Knoll and Ruska developed electromagnetic (EM) lenses using a collection of coiled wires encased within a round iron cap (Figure 1 a and b). Circulation of an electrical current through the wires produces fringing magnetic fields at a gap in the iron cap that can be used Analytical Chemistry, Vol. 82, No. 7, April 1, 2010
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Figure 1. (a) A schematic representation of magnetic field lines (red arrows) generated via electrical current flow (blue arrow) around a circular coil. (b) Cross-sectional diagram of an EM lens represented by its optical analog (c), for simplicity. (d) Spherical aberration for an uncorrected lens and aberration correction using a compensating lens system (depicted as a concave lens). (e) The differences in signal detection for imaging in aberration corrected TEM (left) and STEM mode (right).
to focus electrons (Figure 1b). The strength of the fringing fields can be manipulated by adjusting the electrical current through the coil, thereby increasing or decreasing the focal strength of the lens. This allows for a convergence of the electrons in a manner similar to adjusting the height of a glass lens in a compound light microscope (Figure 1c).1 The electron wavelengths used in transmission electron microscopy (TEM) range from ∼0.4 to 4 pm. The highest magnification factor of a modern electron microscope is about a few million.1 Coupled with an array detector, these magnifications provide sufficient sampling to detect individual atoms. However, the resolution for these instruments is consistently ∼20× worse than their theoretical limits. This discrepancy is caused by aberrations in EM lenses arising from differences in field strength that increase with distance from the lens axis. As a result, the electrons traveling most distant from the axis are focused more strongly than those traveling proximally to the axis (Figure 1d). This effect is termed spherical aberration, and ultimately, this spread in the focal distances limits the resolution of electron microscopes.2 The amount of spread is proportional to the spherical aberration coefficient (Cs) and the square of the offaxis distance divided by the focal length of the lens. Cylindrical magnetic lenses have positive Cs values.2 Thus, correcting the aberration in magnetic lenses requires noncylindrical magnetic lenses with negative Cs values (Figure 1d). The thick magnetic hexapole lens, which is composed of three 2600
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magnetic dipoles, meets this requirement.3 Unfortunately, the hexapole lens, like any other multipole lens system, exerts large low-order aberrations (Cs is a 3rd order aberration), which if not removed, impose greater limitations on the resolution than the Cs alone.3,4 A successful Cs-corrector design for an electron microscope uses two symmetric magnetic hexapoles arranged (with the help of two additional round lenses) to eliminate each other’s lower-order aberrations.4-6 Krivanek and colleagues have developed an Cs-corrector design for scanning transmission electron microscopy (STEM)-dedicated instruments that uses a combination of quadrupoles and octupoles.7,8 The Cs-corrector can be positioned either before or after the sample. If placed after the sample (Figure 1e), high resolution electron microscopy (HREM) imaging with a resolution of 1 Å or better can be achieved with very thin (a few nanometers) samples. The contrast within an image comes mostly from modifications to the phase of the electron wave by the electrostatic potential of atoms.9 This contrast is sensitive to both light and heavy atom contributions and the nature of the electron scattering events. Positioning the Cs-corrector before the specimen plane (Figure 1e) results in a sub-nanometer electron probe for STEM imaging. In STEM, as the electron probe scans across the sample, it is scattered by atoms in the specimen. Electrons scattered into an annular detector placed after the specimen are collected and used to form a rastered image (Figure 1e). In this mode of imaging, called annular dark-field (ADF)-STEM,10 the electron
image contrast is approximately proportional to the square of the atomic number (Z) and thickness when a large inner cutoff angle is used for the ADF detector.11 This mode of imaging is known as incoherent or Z-contrast imaging. Additionally, the electrons passing through the hole in the ADF detector can be analyzed by electron energy loss spectroscopy (EELS)12,13 for high resolution chemical and electronic structure analysis.14 The combination of Cs-correction and improvements in electron energy loss spectrometers have led to the recent demonstration of atomic resolution chemical mapping15,16 and local electronic structure analysis.17 Z-CONTRAST IMAGING AT ATOMIC RESOLUTION Quantitative Z-contrast imaging is an exceptionally powerful method for characterizing the structure of nanoscale materials. In this technique, image contrast is obtained by the collection of electrons scattered at high angles (sin θ/λ > 2 Å-1; θ is the scattering angle) and is dominated by electron-phonon scattering.11 The high-angle scattering intensity has the characteristics of Rutherford scattering, with the scattering crosssection of an atom defined by:1 dσ(θ) ) dΩ
e4Z2 16(E0)2 sin4
θ 2
in which dσ(θ)/dΩ denotes the angular distribution of electrons elastically scattered from an atom, e is the electron charge, and E0 is the potential of the incident electron beam. The ADF detector (Figure 1e) converts the flux of high θ scattered electrons to photons. The intensity of this output signal is then used to construct the image. This Z-dependence is extremely useful for improving image contrast in studies of supported metal catalysts because of the difference in scattering power between a metal cluster and the low-Z support materials that are typically used in such systems (e.g., C, SiO2, or Al2O3).18 More importantly though, with proper detector calibration, the image intensity measurements can be quantified to yield the approximate number of atoms in a cluster.19-21 This useful analytical quality of Z-contrast imaging is further empowered by adding a Cs-corrector, which enables atomic resolution. The improvements can both resolve and speciate the atoms present in multimetallic clusters, as was shown in recent publications on the structure of bimetallic coreshell and alloy nanoclusters (Pt-Pd) and Pt-Co nanoparticless model systems of interest for applications in electrocatalysis.20,22 Alloying two or more metals can improve the catalytic properties of nanoscale clusters.23-25 Examples of promoted behavior include enhanced rates and selectivity, resistance to poisoning, and improved resiliency toward sintering.23,25,26 These features are both structurally and compositionally responsive and thus require accurate determinations of the nanoparticles’ morphological and elemental properties. Such characterizations remain a frontier challenge in research. In a recent work, Pt(core)-Pd(shell) and Pd(core)-Pt(shell) nanoparticles and a random Pt-Pd nanoalloy were synthesized and systematically compared.20 Figure 2a shows the Cs-corrected ADF-STEM image of a representative Pt(core)-Pd(shell) nano-
particle. Individual atoms are clearly visible, but more impressive is the Z-dependent scattering intensity exhibited by the elementally distinct atoms in the particle. The peak intensities reveal the predominant atom type present at various regions in the cluster. The region with the strongest scattering intensity (localized at the center of the cluster) is caused primarily by Pt (Z ) 78), whereas the significantly weaker scattering (observed at the cluster’s extremities) is caused by a shell comprised primarily of Pd (Z ) 46). The features observed in Figure 2a are in agreement with the qualitative expectations of a core-shell motif based on the chemical methodologies used to synthesize these clusters.27 The dashed box traversing the cluster provides a quantitative sectional intensity map of the cluster. As expected, the intensity profile (inset, Figure 2b) shows the strong scattering signal in the region of the core and the sharp drop in intensity at the extremities of the particle. The Cs-corrected ADFSTEM micrographs permit the identification of the facet planes that truncate specific particles. To do so, an FT of the image is used to generate a power spectrum (Figure 2b). This in turn is used to identify the crystal’s habit (e.g., fcc) and orientation relative to the electron beam (i.e., its zone axis), as well as its truncating crystallographic planes (Figure 2a). Figure 2c shows exemplary data for a Pd(core)-Pt(shell) nanoparticle. Visual comparison indicates that this type of particle exhibits a complete inversion of the intensity profile (inset, Figure 2d) versus the cluster in Figure 2a. Analyzing this intensity profile quantitatively verifies the presence of a higher Z-scattering element (Pt) encapsulating a low-Z core (Pd). As before, the power spectrum of the cluster (Figure 2d) defines its zone axis and terminating planes. The data for an alloy cluster (Figure 2e) shows a more statistical placement of the Pt and Pd atoms within the particle. The intensity profile in this case (inset, Figure 2f) shows pronounced undulations across the particle, a feature that correlates well with the speckled pattern seen in the Cs-corrected ADF-STEM image of the cluster. Such features, previously observed in related systems,22 support a more distributed placement of the Pt and Pd atoms within the particle. Figure 2f shows the corresponding power spectrum for this cluster along with the zone axis and terminating edges. The atomic speciation afforded by Cs-corrected ADF-STEM imaging at atomic resolution is powerfully complemented by energy dispersive X-ray spectroscopy (EDX), a technique that performs elemental analysis using the X-ray emissions from sample-probe interactions.1,28 The X-rays produced provide a fingerprint that quantitatively defines the elemental composition of a cluster.29,30 Although it is not a method that provides singleatom resolution, EDX has an indispensible capacity for assessing composition.1,20,22,28 ELECTRON TOMOGRAPHY AND ELECTRON MICROSCOPY GUIDED THEORETICAL MODELING Electron images are inherently 2D projections; however, extracting information from these images to formulate 3D reconstructions is possible. Characterizing 3D structural features in this way is crucial to obtaining predictive understandings of structure-function relationships for many nanostructured materials. One obvious example is understanding the interconnectivity of porous media Analytical Chemistry, Vol. 82, No. 7, April 1, 2010
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Figure 2. (a, c, and e) Different assortments of bimetallic Pt-Pd nanoparticles accompanied by (b, d, and f) their respective power spectra and intensity profiles (insets).
and the spatial distribution of nanoparticles caused by the synergistic, catalytic effects of particle support interactions.31 Another example is the 3D reconstruction of nanocrystals to gain more insight into the role of crystal morphology as it relates to catalytic behavior.32 Different crystal facets on catalytic nanoclusters can promote reactions at different rates,33-35 and visualization of crystal facets requires 3D imaging. To date, the only technique capable of 3D reconstruction at nanometer resolution is electron tomography. To create the 3D reconstruction, a series of images at different orientations are collected and combined to form a 3D rendering.36 This can be achieved either using ADF-STEM with a high cutoff angle for the Z-contrast11 or using energy-filtered TEM based on inelastic scattering.37 The image contrast must be proportional to the mass and thickness. Furthermore, exploiting the reduced depth of focus in Cs-corrected microscopes allows optical sectioning of nanoclusters.32,38 This depth sectioning technique 2602
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reduces exposure to the electron beam, decreasing specimen damage in small nanoclusters compared to tilt tomography.39 The extension of electron tomography to atomic resolution is currently under development.40 The number of orientations required for 3D reconstruction increases as the image resolution improves. However, image information recorded at orientations in which atomic columns are not well separated is limited by the efficiency in information transfer by the lens.41 Discrete projections have been proposed for tomography of crystalline materials and demonstrated for Au nanocrystals.42 Alternatively, image simulation can be used to infer the approximate 3D structure of experimentally observed metal clusters. Figure 3a shows an example of a monometallic Pt nanoparticle with single-crystalline features. This micrograph demonstrates the high-quality images attainable with Cs-corrected ADF-STEM and the wealth of data supplied by the image that can be exploited to construct a 3D representation. Compara-
Figure 3. Monometallic (a) Pt and (b) Pd nanoparticles. (c) Atom quantification for different sized Pd particles. Model icosahedron (d) before and (e) after application of a Gaussian blur. (f) Projected potential and Zmult intensities of the boxed regions in (d) and (e).
tively, Figure 3b shows a monometallic Pd nanoparticle displaying high levels of disorder, as suggested by the poor resolution of atomic columns and the lack of precise, atomic definition at the terminating edges. Such data, however, can still be used for structural identification using theory-based methods to quantitatively treat the experimental data. In the present case, the image intensities from free, individual atoms (Figures 3 a and b) provide a means of calibrating the detector response and offer the possibility of approximating the number of atoms contained within a particle. The number of Pd atoms in a small cluster for a series of images was calculated with averaged single-atom scattering intensity.20 The graph in Figure 3c shows the dependence of particle diameter on the atom count simultaneously plotted with growth curves for different model crystal structures. Comparison of the experimental data against these theoretical structures reveals the idealized structure that best predicts the relative scaling of the intensity of the experimental image (and thus atom count) as a function of the particle diameter. The trends seen in Figure 3c indicate that the Pd clusters (of which Figure 3b is representative) were most favorably modeled by either an icosahedron or a cuboctahedron. An analysis of a series of micrographs qualitatively determined that the Pd clusters exhibited multiply-twinned structures, strongly suggesting that the particles adopt the theoretically predicted, highly-twinned structure of an icosahedron.43,44 A model of an icosahedron oriented along a 2-fold axis further validated this hypothesis of crystal structure identity. When the detected intensity is assumed to scale precisely with the number of atoms in an atomic column, an image referred to as the “projected potential” can be created (Figure 3d). Application of a Gaussian blur to Figure 3d yields Figure 3e, which when juxtaposed with Figure 3b, shows the qualitative similarity of the experimental and theoretical images. The most prominent feature
of both images is the row of atoms extending across the equator of the particle. Inclusion of multiple scattering effects using the multislice45,46 simulation program ZMULT47 introduces a rolling background (Figure 3f) into an integrated intensity profile (boxed regions, Figure 3d and e) but retains the overall structural trends. Although the experimental image lacks the structural definition of the theoretical model at its terminating edges (suggesting the terminations embed other forms of complexity in their bonding), the use of Cs-corrected ADF-STEM imaging in conjunction with modeling clearly provides good insights into a crystal geometry that describes this disordered system. QUANTIFYING SURFACE STRUCTURE IN NANOPARTICLES USING COHERENT DIFFRACTION As noted above, because surface atoms in a nanocluster occupy lower coordination sites, they can exhibit complicated bonding motifs that differ from those of bulk atoms.48 The chemical activity that occurs at a surface is determined by the unique nature of its bonding environment.33,35,49 Consequently, atoms’ respective positions on the surface and their reconstructionswhether they are on facets, steps, or at the kinks of stepssdefine the properties of those atoms. Though a great deal of theoretical and experimental knowledge about the relaxation of extended 2D surfaces has been accumulated, far less is known about the surface structures of nanoparticles. Molecular dynamics (MD) simulations have predicted that surface bond lengths contract by a few percent relative to those within a nanoparticle’s core.50 The dominant contraction mode gleaned from these studies is radial, with the surface atoms being pulled towards the nanoparticle’s center.50 Experimentally, surface contraction has been observed for Au nanoparticles using powder XRD.51 X-ray absorption spectroscopy (XAS), which measures average bond distances, has also revealed Analytical Chemistry, Vol. 82, No. 7, April 1, 2010
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Figure 4. (a) Experimental diffraction patterns for the (-11-1), (00-2), and (1-1-3) diffraction peaks. (b-d) Simulated patterns for the experimental peaks in (a). (e, f) Vectors representing surface atom displacements for the grey atoms shown in their respective insets. The magnitudes of the displacements are rendered using colors.
similar forms of contraction.52,53 In the above cases, however, the measurements were incapable of distinguishing surface bond length contractions from contractions occurring within a cluster’s interior. Coherent nanoarea electron diffraction is an alternative tool for studying surface relaxations of nanoclusters and offers multiple advantages. First, the electron probe is small enough to isolate a single nanoparticle; the electron probes currently being developed for coherent electron diffraction reach sizes as small as 10 nm yet still provide adequate electron intensity to obtain sufficient S/N in recorded diffraction patterns.54 Second, because of the large electron scattering cross-section of noble metals, recording diffraction patterns from nanoparticles as small as 1.3 nm is possible.55 The quality of diffraction patterns can be further improved using energy filters that selectively permit electrons that have lost little or no energy from inelastic scattering to form the 2604
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diffraction pattern. Finally, information recorded in the diffraction pattern is only limited by scattering. Recent studies have reconstructed coherent diffraction patterns to form images that provide resolution well beyond the limits of direct imaging.56,57 Huang et al. outlined the principle of using coherent electron diffraction to determine the surface strain in nanoclusters by modeling of experimental diffraction data.58 That study measured the surface contraction of Au nanoclusters from an HREM image and its diffraction pattern. The orientation of the nanocrystal was identified by creating 3D models of the nanocluster based on comparison of the experimental HREM micrograph image and diffraction pattern to simulated analogs. Figure 4 shows models of three diffraction peaks ([-11-1], [002], and [1-1-3]) compared with the experimentally obtained diffraction peaks for a 3.5 nm Au nanocrystal (Figure 4a). The models are an unrelaxed nanocrystal (Figure 4b), a coordination
Figure 5. (a, b) Au nanoclusters on (-110) TiO2. (c, d) EELS spectra for the regions (arrows) scanned in (b).
dependent radial contraction model (Figure 4c), and a nanoparticle relaxed by an MD simulation using the embedded atom method potential for the interaction between Au atoms (Figure 4d). The diffraction patterns were simulated using a kinematic approximation. Comparing Figure 4 a and b shows that the asymmetry in the first order fringes around the central Bragg peak in experimental diffraction patterns cannot be accounted for by the atomic scattering of an unrelaxed Au nanocrystal. Agreement between theory and experiment was improved using a radial contraction model (Figure 4c), but the best agreement was with the MD relaxed nanoparticle (Figure 4d). MD simulations of surface contraction are more rigorous because they account for nonnearest-neighbor interatomic forces. Nonetheless, the radial contraction model allowed a parameter-based fit to the experimental data. The fit showed that the degree of asymmetry surrounding the central Bragg peak was very sensitive to the relative displacements of surface atoms at different facets and edges. Specifically, the atoms in the {100} and {111} planes had out-of-plane displacements of 0.13 Å and 0.05 Å, respectively; conversely, the edge atoms showed a displacement of 0.2 Å. Immediately obvious is that lower coordination environments make the greatest contribution to surface contraction. Furthermore, the magnitude of surface contraction quantitatively agrees
with the results of the MD simulation. Figure 4e and f show the magnitude and direction of surface relaxation experienced by atoms in the Au nanocrystal obtained from the MD simulation. EELS CHARACTERIZATION OF INTERFACIAL CHARGE TRANSFER IN SUPPORTED CLUSTERS In heterogeneous catalytic systems, perturbations of electronic structure originating at cluster-support interfaces can significantly affect the energetics of catalytic reactions.59 This effect may contribute to the rate enhancements seen in the catalytic oxidation of CO to CO2 by TiO2-supported Au nanoparticles.60 The ability to measure charge-transfer events in systems such as this could help explain the catalytic behavior observed at cluster-support junctions (e.g., the Au/TiO2 boundary) and provide insights toward the design of new catalysts. Some common techniques used to measure electronic structure are XAS, XPS, and EELS. In EELS, an electron energy filter composed of magnetic sector(s) collects and sorts the electrons according to their energy. The resultant spectrum of the signal intensity versus electron energy loss can yield information about plasmon excitations, chemical identification based on core-electron excitations, and the electronic structure based on near edge fine structure of the core-loss edge energies.12,13,28 The information obtained by EELS complements that from XAS and XPS, Analytical Chemistry, Vol. 82, No. 7, April 1, 2010
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which have the advantage of larger sampling size, but EELS also can probe an individual cluster’s local electronic structure at nearatomic resolution. In the context of the interfacial electronic structure of catalysts, the ensemble average of the atoms present in each cluster (i.e., surface, core, and interfacial atoms) dominates the XAS and XPS signals. This cumulative signal makes it challenging to unambiguously deconvolute the signal of the interfacial atoms from the overall data. EELS, when combined with Cs-corrected ADF-STEM, can overcome this obstacle by allowing the study of individual nanoparticles with near-atomic precision. The ultimate EELS spatial resolution in aberrationcorrected STEM instruments with probe sizes
