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Three-Dimensional Morphology of Iron Oxide Nanoparticles with Reactive Concave Surfaces. A Compressed Sensing-Electron Tomography (CS-ET) Approach Zineb Saghi,*,†,||,^ Daniel J. Holland,*,‡,|| Rowan Leary,† Andrea Falqui,§ Giovanni Bertoni,§ Andrew J. Sederman,‡ Lynn F. Gladden,‡ and Paul A. Midgley† †
Department of Materials Science and Metallurgy, University of Cambridge, Pembroke Street, Cambridge CB2 3QZ, United Kingdom Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge CB2 3RA, United Kingdom § Istituto Italiano di Tecnologia (IIT), Via Morego 30, 16163 Genova, Italy ‡
bS Supporting Information ABSTRACT: In this paper, we apply electron tomography (ET) to the study of the three-dimensional (3D) morphology of iron oxide nanoparticles (NPs) with reactive concave surfaces. The ability to determine quantitatively the volume and shape of the NP concavity is essential for understanding the keylock mechanism responsible for the destabilization of gold nanocrystals within the iron oxide NP concavity. We show that quantitative ET is enhanced greatly by the application of compressed sensing (CS) techniques to the tomographic reconstruction. High-fidelity tomograms using a new CS-ET algorithm reveal with clarity the concavities of the particle and enable 3D nanometrology studies to be undertaken with confidence. In addition, the robust performance of the CS-ET algorithm with undersampled data should allow rapid progress with time-resolved 3D nanoscale studies, 3D atomic resolution imaging, and cryo-tomography of nanoscale cellular structures. KEYWORDS: Electron tomography, compressed sensing, 3D reconstruction, quantitative, iron oxide nanoparticles
I
ron oxide nanostructures have attracted growing interest in recent years for their unique performance as catalysts, drug delivery carriers, and contrast agents for magnetic resonance imaging.13 They have also been used as smart vehicles for in vitro protein and cell magnetic separation and as biocompatible active materials for localized hyperthermia treatments.4,5 For all reported applications, it is essential to tailor precisely the shape and size of the nanoparticles (NPs). For example, Cheng et al.6 synthesized porous hollow iron oxide nanospheres to allow the encapsulation of the anticancer drug cis-platin and the transport of the formed nanocapsules by magnetic manipulation. On the basis of a similar synthesis route, we produced either asymmetric iron oxide containers or iron oxide concave NPs by annealing goldiron oxide coreshell NPs (based on the process duration) followed by gold leaching.7 The initial single crystal structure of the iron oxide domains was completely retained after the gold leaching (see Figure S1 in the Supporting Information). The size and morphology of the concave NPs were controlled directly by the size of the initial gold nanocrystal around which the iron oxide shell was grown. Such concave NPs were also observed to have the ability to destabilize gold nanocrystals if their size matches that of the reactive concave region. As a result, a drastic size and morphological change of the r 2011 American Chemical Society
gold nanocrystals was observed within minutes, while the concave NPs remained unchanged. Although transmission electron microscopy (TEM), dynamic light scattering, and absorption spectroscopy could give some insight, no direct proof was available to highlight the role played by the 3D concave morphology, for example the 3D curvature and surface area, on the reactivity of the NPs. For 3D characterization at the nanoscale, electron tomography (ET) has proven itself as a powerful technique in the study of structures in the fields of biology8,9 and materials science.10,11 A series of projections (2D images) recorded by TEM, typically about a single tilt axis, can be used to determine the 3D morphology and composition of nanoscale structures. The tilt series is usually exported to a commercial tomography package where, after alignment by cross-correlation, the reconstruction is undertaken using a reconstruction algorithm such as weighted back-projection (WBP), simultaneous iterative reconstruction technique (SIRT), or algebraic reconstruction technique (ART). Even though SIRT and ART often provide Received: July 3, 2011 Revised: September 13, 2011 Published: September 27, 2011 4666
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Nano Letters tomograms of higher fidelity than those obtained using WBP (and especially when the input projections are noisy), the reconstruction still suffers from artifacts (e.g., “fan” or “star” artifacts, blurring, and elongation) that arise from the fact that the data set is limited in terms of both the number of angular projections that can be acquired and a maximum tilt angle (typically ca. 70) above which projections cannot be recorded.12 A manual and time-consuming segmentation process is then often necessary to overcome such artifacts and identify key components in the reconstruction, leading to potentially biased interpretation of the data. Many groups are trying to develop more advanced algorithms to tackle these problems, often by incorporating prior knowledge into their reconstruction process. One example, DART, based on discrete tomography,13 has recently been introduced successfully by Batenburg et al.14,15 to reconstruct objects from few projections, starting with a SIRT reconstruction and imposing the known number of gray levels (densities) for the reconstruction. The problem of reconstructing a signal or image from incompletely sampled data has also attracted significant interest from the field of information theory and approximation theory, particularly with the development of compressed sensing (CS).16,17 CS takes advantage of the “sparsity” of an object and is known to provide, under certain circumstances, accurate signal recovery from a very small number of measurements using a nonlinear reconstruction algorithm. CS has recently caused excitement in a number of fields,1820 and especially in magnetic resonance imaging (MRI)21 because far fewer measurements are needed to achieve an accurate reconstruction, thus significantly reducing the total scan duration. Furthermore, CS has been shown to be robust when handling low signal-to-noise ratio (SNR) data sets.18,22 These results suggest that CS could be a powerful means of reducing the artifacts that arise from ET reconstructions. In the present work, we introduce a compressed sensing-based reconstruction technique for ET applications. We apply CS-ET to characterize iron oxide NPs and demonstrate that the technique can substantially reduce the missing wedge and star artifacts that are so problematical with more conventional reconstruction approaches such as WBP or SIRT. To our knowledge, this is the first successful application of CS to ET experiments for the quantitative analysis of 3D nanoscale structures, and we believe that the technique will prove to be of great benefit for a variety of 3D imaging and analysis applications in materials science and biology. Principles of CS-ET. In ET we acquire a limited number of projections around a single tilt axis. The number of projections and the angle over which these can be acquired are restricted by the durability of the sample and the geometry of the microscope. The limited number of projections means that the reconstruction problem is underdetermined and it is not possible to reconstruct uniquely a full tomogram from the measured data. CS can provide a method for overcoming this limitation by incorporating some simple prior knowledge into the reconstruction. CS is derived from the principle that many images are compressible, an idea that is also exploited in the well-known JPEG and JPEG-2000 image compression algorithms.23 In this context, compressible means that the important details in the image can be recovered from significantly less information than would be required to represent the entire image, without affecting the quality significantly. To describe ET in the con-
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text of CS, we represent the 3D tomographic image of size a b c pixels in the x, y, and z directions, respectively, as a vector X with n = a b c coefficients. The value of each coefficient of the vector X is the intensity in a given pixel in the tomogram. There are then two key criteria which enable application of CS: (1) The final image must be sparsely represented in some basis. This statement is equivalent to saying the tomogram is compressible and means that it must be possible to transform the vector describing the tomogram in such a way that only a small number of coefficients have values greater than zero. For example, a sparse image in the pixel representation has few pixels that have nonzero values, thus the vector X contains only k nonzero values where k , n. CS can be adapted to almost any type of image through the use of appropriate sparsifying transformations. There are many possible sparsifying transformations;24 two of the most common are total variation (TV) (for piecewise constant or cartoon-like images) and wavelets (for natural images such as photographs).21,25 In the context of ET, DART could be regarded as a special case of CS where the transformation used characterizes the image using a set of discrete values, as with the TV transform.26 For ET reconstructions of simple objects such as the iron oxide nanoparticles, the pixel representation is itself sparse. For more complex systems such as biological samples, a wavelet transform is likely to be more appropriate as these have been demonstrated to be a powerful tool in denoising biological ET images.27 (2) The sampling method must be incoherent with respect to that basis. This condition is most readily interpreted as saying that artifacts arising from the limited number of tilt projections should appear “noiselike” and be distributed throughout the tomogram. In the case of single axis tomography, the limited number of projections acquired around the tilt axis is (according to the Fourier slice theorem28) equivalent to radial sampling in the Fourier domain. This form of sampling of Fourier space has been shown to be sufficiently incoherent to allow the application of CS,16,21 meaning that it is possible to apply CS to ET applications. Under these two conditions, CS reconstruction algorithms can recover the k largest coefficients in the chosen transform domain, subject to consistency with the measured data. In other words, within all the possible reconstructions that are consistent with the input data, SIRT will provide a solution while CS can find the sparsest, or simplest, in the chosen domain. In this work, we study free-standing iron oxide NPs and, therefore, decide to enforce sparsity in the image domain itself, as this helps to suppress background signals and missing wedge artifacts, and at the same time impose sparsity in the TV domain as we expect the object to have sharp boundaries and uniform density. A variety of algorithms are available to perform a CS reconstruction, the majority of which fit into the classes of greedy algorithms or convex optimization algorithms. Here we describe the specific implementation of our CS-ET algorithm only briefly—a more detailed paper about the compressed sensing algorithm is in preparation. We use an approach based on the conjugate gradient descent algorithm of Lustig et al.21 The algorithm is based on solving the unconstrained optimization 4667
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Figure 1. (a) Test object used for the simulation of a tilt series from 65 to +65 with 5 increment. (b) WBP and (c) SIRT reconstructions showing typical missing wedge artifacts. (d) Inverse Fourier transform used as a starting point for the CS-ET optimization process. (e) The CS-ET reconstruction. (f) The sparsity in the TV domain of the obtained object from (e).
problem defined by
2
min FX y X
2
þ λ ΨX
1
ð1Þ
)
)
where F is the undersampled Fourier transform operator, X is the vector describing the tomogram, y is the Fourier transform of the measured projection data, and Ψ is the chosen transformation that maps the image to a domain in which it can be sparsely represented. The term ΨX 1 in eq 1 is the l1-norm (defined as the sum of the absolute values) of the coefficients in the chosen transform domain. The l1-norm acts as a proxy for sparsity—i.e., minimizing the above objective produces a reconstruction that has the sparsest representation in the transform domain while remaining consistent with the acquired measurements. Conjugate gradient descent finds the solution to eq 1 by iteratively stepping closer to the solution along orthogonal vectors defined by the residual. For application to CS-ET, our algorithm consists of the following procedures. The first step is to apply a 1D Fourier transform to the projections, to transform the tilt series into the radially sampled Fourier domain. These Fourier data are then regridded from the radial to the Cartesian grid, and a 2D inverse Fourier transform is applied to obtain the initial image. The optimization then proceeds to minimize the l1-norm of the image in both the pixel and the gradient representations using conjugate gradient descent. In practice, this means reducing the intensity of the pixels with the lowest intensity without adjusting those with the highest intensity, unless this is
inconsistent with the measured data. The optimization problem requires a nonlinear solution, and therefore every 100 iterations the algorithm was reinitialized, where the starting image was obtained by setting any pixels with intensity less than 50% of the maximum intensity to zero, to force the solution to the global minimum l1-norm and therefore minimize artifacts from the missing wedge. Simulated CS-ET Reconstructions. To illustrate the capabilities of CS-ET in comparison with WBP and SIRT algorithms, a NP test object was generated using MATLAB (Figure 1a), with facets, concavities (bottom NP), and a closed internal cavity (top NP), mimicking the likely morphological features of the iron oxide NPs studied here. Simulated tilt series data from 65 to +65 with 5 increment were obtained using a Radon transform in MATLAB, and corrupted by Poisson noise at a level estimated from real HAADF-STEM projections of the NPs. A WBP reconstruction from such data (Figure 1b) shows the typical limited-angle acquisition artifacts: starlike features due to the large tilt increment, elongation and blurring in the missing wedge direction, and an overall erroneous density distribution. SIRT provides a better reconstruction (Figure 1c) by iteratively comparing the reprojected volume with the acquired projections and using any difference to improve the reconstruction. Although SIRT enforces consistency between the reconstruction and the input projections, it is, however, still prone to missing wedge artifacts arising from the limited data set and the nonuniqueness of the solution. 4668
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Figure 2. CS-ET (top row) and SIRT (middle row) reconstructions from 27 (a ,d), 13 (b, e), and 9 (c, f) projections. The bottom row shows the SIRT reconstructions thresholded using Otsu’s method.
The initial image used for the CS-ET reconstruction, obtained after applying the Fourier transform operations to the projections and regridding the Fourier data from a radial to a Cartesian grid, is shown in Figure 1d. This image was used as a starting point for the optimization process. Figure 1e shows the final result which closely resembles the input simulated NPs displayed in Figure 1a. Figure 1f shows the absolute value of the TV of the image in Figure 1e, confirming that the image is sparsely represented in the gradient domain. These results clearly demonstrate the significant reduction in artifacts that can be achieved through the use of a CS-ET reconstruction algorithm: both simulated NPs are clearly resolved in Figure 1e, as is the concavity on the bottom of the lower NP. Moreover, it is clear that the cavity in the top NP is contained within the particle, and the obtained reconstruction can be binarized easily by global thresholding on the image histogram. Furthermore, for beam-sensitive materials, the total dose, and thus normally the number of projections in a tilt series, must be kept below some critical threshold. In this context, we demonstrate the superiority of CS-ET over conventional SIRT reconstruction by simulating in Figure 2 the case where the number of projections used for the reconstruction has been reduced from 27
(corresponding to a 5 increment) to 13 and 9 (corresponding to 10 and 15 increments, respectively) but where the SNR in each projection remains unaltered. It can be seen that when the number of projections used for reconstruction is reduced, artifacts in the resulting tomograms produced using the SIRT algorithm increase substantially and thresholding becomes more difficult, whereas the CS-ET algorithm continues to recover the original shape (almost exactly). In fact, the CS-ET reconstruction from 9 projections is clearly a better representation of the original object than the SIRT reconstruction from 27 projections, further emphasizing the quality of reconstruction that is possible using CS-ET. Experimental Methods. Oleylamine-coated gold nanocrystals were prepared by reducing gold chloride (AuCl3) by sodium borohydride (NaBH4) in a micellar solution of didecyldimethylammonium bromide ([CH 3 (CH 2 )9 ]2 N(CH3 )2 Br), followed by the functionalization of gold nanocrystals with oleylamine. The metal NPs were reacted with iron pentacarbonyl (Fe(CO)5) in octadecene to grow metaliron oxide coreshell NPs. By variation of the reaction temperature from 190 to 320 C and the reaction time from 30 min to 4 h, the initial coreshell NPs evolved into various asymmetric metalmetal oxide core 4669
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Figure 3. (a) 0 HAADF-STEM projection of iron oxide NPs. False color volume rendering of the corresponding (b) SIRT and (c) CS-ET reconstructions. The red arrow indicates the NP chosen for the quantitative analysis.
Figure 4. Orthoslices, perpendicular to the tilt axis, through the SIRT (ac) and CS-ET (df) reconstructions obtained from 27 projections. The optical axis is oriented vertically.
shell morphologies and then to heterodimers, in which it was possible to tune the degree of exposure of the metal domain to the external environment. The gold atoms could be selectively dissolved via treatment with iodine molecules, and either iron oxide NPs enclosing an accessible concavity, that is, “nanocontainers”, or concave NPs were formed. In the concave NPs, the size of the cavity could be tuned by changing the size of the original gold region. The resulting NPs were washed several times with a mixture of 1:1 ethanol/toluene through centrifugation and were finally redispersed in hexane or toluene. To image the concave NPs, the solution was dispersed onto a carbon grid, and electron tomography was performed on a FEI Tecnai F20 field-emission gun scanning transmission electron microscope (STEM) operated at 200 kV. The high angle annular dark field STEM (HAADF-STEM) imaging mode was chosen to avoid unwanted diffraction contrast and satisfy the projection requirement needed for tomographic reconstruction.29 Data collection was carried out by tilting the specimen about a single axis from 70 to +60 with a 5 increment, using a Fischione
ultrahigh-tilt tomography holder, and acquiring the HAADFSTEM projections with the FEI software package Xplore3D. The tilt series was then exported to the FEI software Inspect3D for alignment and tomographic reconstruction using WBP and SIRT. The CS-ET reconstruction was undertaken using an algorithm implemented in MATLAB. Thresholding was then performed by Otsu’s method30 using MATLAB or ImageJ, to provide an objective threshold level to separate the NP from the background. Visualization was undertaken using Amira. Experimental Reconstruction using CS-ET. A HAADFSTEM projection of an agglomerate of iron oxide NPs is shown in Figure 3a. The shape and size of the concavity cannot be extracted accurately from a single 2D projection, as doing so would imply a strong assumption about the symmetry of the concavity and a favorable orientation of the NP with respect to the optical axis. From the voxel projection views of the reconstructions obtained with SIRT and CS-ET displayed in panels b and c of Figure 3, respectively, it is evident that both reconstructions provide useful qualitative information about the octahedral 4670
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Figure 5. Orthoslices through the SIRT (top) and CS-ET (bottom) reconstructions, from 27 (a, d), 13 (b, e), and 9 (c, f) projections. The optical axis is oriented vertically.
Figure 6. Estimation of the concavity volume from the CS-ET and SIRT reconstructions obtained with 9, 13, and 27 projections. Inset figures are isosurface renderings of the NP segmented to extract the concavity volume (as described in the text) from the SIRT and CS-ET reconstructions based on 27 projections.
shape of the NPs and the concavities. The superiority of CS-ET compared to SIRT is appreciated when one looks at the orthoslices parallel to the optic axis and tries to binarize the volumes for further quantitative analysis. In the SIRT reconstruction (see Figure 4, top row), facets oriented perpendicular to the optic axis are not well-resolved and particles close to each other appear merged. The CS-ET algorithm on the other hand, succeeds in interpolating the “missing wedge” regions and recovering all facets with near isotropic resolution (Figure 4, bottom row). Automated global thresholding techniques can then be used to binarize the volume, avoiding laborious manual segmentation (see Otsu segmentation of CS-ET and SIRT orthoslices in Figure S2 of the Supporting Information) and enabling quantitative nanometrology analysis of the tomograms to be performed. The clear superiority of CS-ET on both simulated and experimental
tomographic reconstructions suggests that it should be the method of choice for reconstructing NPs. We would like to emphasize however that CS-ET can also successfully recover more complicated structures by choosing the appropriate sparsifying transform. Compared to some discrete methods, CS-ET should be more versatile as it does not require prior knowledge about the number of gray values in the reconstruction. In addition to artifact reduction in the reconstruction, we showed with the simulation data that CS-ET is inherently good at reconstructing from undersampled data (noting that this is particularly appealing when the sample is sensitive to electron irradiation). We demonstrate this on experimental data by similarly reducing the number of projections used for reconstruction to 13 and 9 (corresponding to tilt increments of 10 and 15, respectively). The resulting reconstructions are shown in Figure 5. Note that for these reconstructions, no thresholding steps were required to reach the l1-minimum. We observe that the star artifacts originating from large tilt increments worsen in the SIRT reconstructions, while the CS-ET algorithm recovers the correct shape in all three scenarios. At the binarization stage, the Otsu technique succeeds in extracting the particles from the background in the CS-ET reconstruction, whereas manual adjustment of the threshold value is necessary in the case of SIRT (to compromise between reducing the missing wedge and the star artifacts on the one hand and retrieving all pixels belonging to the features of interest on the other); see Otsu binarization of SIRT and CS-ET orthoslices in Figure S3 of the Supporting Information. Both simulated and experimental results suggest that for beam-sensitive samples, where the total dose has to be kept to a minimum, high fidelity tomographic reconstructions can be achieved with CS-ET from fewer projections than are usually thought necessary. We are currently pursuing a detailed study to quantify the increase in reconstruction quality that can be achieved for beam-sensitive samples, but from the results presented in this paper, we are confident that a significant reduction 4671
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projections are recorded with a very low SNR. Using CS-ET, fewer projections, each with a greater SNR, may yield 3D reconstructions with greater fidelity than those obtained with conventional approaches. Conclusion. We have applied CS-ET to the 3D study of concave iron oxide NPs and demonstrated the capability of the technique to reduce missing wedge and star artifacts, yielding more straightforward and objective segmentation of the tomograms. CS-ET provides reliable tomographic measurements of NPs that can be used to extract quantitative measurements, such as the volume of the concavities. Furthermore, accurate CS-ET reconstructions can be achieved from fewer projections than are usually required. The reported results suggest that CS-ET is a promising technique for application in highly demanded characterization methods such as dynamic 3D studies, atomic scale ET and cryo-ET.
’ ASSOCIATED CONTENT
bS
Supporting Information. Figures showing HRTEM projection of a group of iron oxide NPs, Otsu segmentation of orthoslices selected from the SIRT and CS-ET reconstructions obtained with 27 projections, and Otsu segmentation of the same orthoslice from the SIRT and CS reconstructions obtained with 27, 13, and 9 projections. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected];
[email protected]. Present Addresses ^
Now at BIONAND, Andalusian Centre for Nanomedicine and Biotechnology, Parque Tecnologico de Andalucía, c/Severo Ochoa, 35, 29590 Campanillas, Malaga, Spain. Author Contributions
)
in the total number of projections can be afforded for most samples without alteration of the reconstruction fidelity. 3D Nanometrology of Concavities. For a quantitative comparison of SIRT and CS-ET, we performed 3D analysis on the NP indicated by the red arrow in Figure 3. The segmentation routine was developed in MATLAB, using features from the image processing toolbox. In the SIRT reconstruction, an unsharp masking filter was applied to help define the edges of the NP. This was followed by Otsu thresholding, binarization, and quantification of relevant size and shape parameters, which will be reported in detail in a future publication. The same postprocessing steps were performed on the SIRT and CS-ET reconstructions based on 9 and 13 projections. For each reconstruction, an estimation of the size of the concavity was obtained by subtracting the thresholded concave NP from its convex hull. The resulting measurements of the concavity volume are shown in Figure 6, where it can be seen that the SIRT reconstruction underestimates the volume of the concavity relative to CS-ET. This can also be seen qualitatively in the surface rendering of the particle displayed in Figure 6. These findings are consistent with the simulation results shown in Figure 2 and experimental results shown in Figure 4, which both confirm that the NP volume tends to be overestimated in the SIRT reconstruction (consequently leading to underestimation of the concavity volume). We attribute this overestimation to the elongation of features in the direction of the optic axis (missing wedge artifacts) and the poorer definition of the NP boundary. Furthermore, at reduced numbers of projections, the SIRT reconstructions degrade substantially, leading to worsening underestimation of the concavity volume. Using these parameters for studying the “key-lock” action method of the NPs would lead to substantial error, as we are interested in the exact volume and surface area of the NP concavities where the destabilization of the gold nanocrystals takes place. By contrast, the quantitative results obtained from the CS-ET reconstructions remain remarkably consistent, affirming that CS-ET is robust at significant undersampling. Discussion. We have shown how the application of compressed sensing techniques can be of great value in the 3D reconstruction of nanoparticles, revealing the morphology in greater detail and with fewer artifacts than when using conventional reconstruction algorithms. The reduction of missing wedge and star artifacts allows simpler and more objective segmentation of tomograms, and this gives greater confidence in the 3D quantitative analysis of nanoscale structures (e.g., concavity volume). Moreover, the binarized tomogram can be used as a faithful 3D model for further simulations with the objective to understand the physical properties of the nanomaterials.31,32 The ability to reconstruct high-fidelity tomograms using CS-ET from relatively few projections enables far more rapid acquisition, and that opens up new possibilities. Drawing on the pioneering work of Zewail and co-workers,33 we should now be able to undertake time-resolved 3D in situ nanoscale studies, following morphological, compositional, and electromagnetic changes to NPs brought about by external stimuli (e.g., heat, magnetic fields, electric current, light). Reconstruction from only a few projections is also of great benefit in the reconstruction of 3D atomic structures of NPs, where the location of atoms in 3D is determined from only a few zone-axis lattice projections.34,35 Lastly, we note that CS-ET should also prove of benefit in the life sciences, where cryo-tomography of nanoscale cellular structures requires careful consideration of total dose and the implementation of dose fractionation,8 in which individual
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These authors contributed equally to this work.
’ ACKNOWLEDGMENT Z.S., R.L., and P.A.M. acknowledge financial support from the European Union under the Framework 6 program under a contract for an Integrated Infrastructure Initiative, Reference 026019 ESTEEM. A.F. and G.B. acknowledge Liberato Manna and George Chandramohan for providing the materials and the fruitful discussion. D.J.H., A.J.S., and L.F.G. acknowledge financial support from the EPSRC (EP/F047991/1). ’ REFERENCES (1) Herzing, A. A.; Kiely, C. J.; Carley, A. F.; Landon, P.; Hutchings, G. J. Science 2008, 321, 1331–1335. (2) Figuerola, A.; Di Corato, R.; Manna, L.; Pellegrino, T. Pharmacol. Res. 2010, 62 (2), 126–143. (3) Jordan, A.; Scholz, R.; Wust, P.; Schirra, H.; Schiestel, T.; Schmidt, H.; Felix, R. J. Magn. Magn. Mater. 1999, 194, 185–196. (4) Lee, J.; Huh, Y.; Jun, Y.; Seo, J.; Jang, J.; Song, H.; Kim, S.; Cho, E.; Yoon, H.; Suh, J.; Cheon, J. Nat. Med. 2006, 13, 95–99. (5) Li, P.; Miser, D. E.; Rabiei, S.; Yadav, R. T.; Hajaligol, M. R. Appl. Catal., B 2003, 43 (2), 151–162. (6) Cheng, K.; Peng, S.; Xu, C. J.; Sun, S. H. J. Am. Chem. Soc. 2009, 131 (30), 10637–10644. 4672
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