Assessment of the 3D localization of metallic nanoparticles in Pd/SiO2 cogelled catalysts by electron tomography

Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts

Accurate 3D nanometrology of catalysts with small nanometer-sized particles of light 3d or 4d metals supported on high-atomic-number oxides is crucial for understanding their functionality. However, performing quantitative 3D electron tomography analysis on systems involving metals like Pd, Ru, or Rh supported on heavy oxides (e.g., CeO2) poses significant challenges. The low atomic number (Z) of the metal complicates discrimination, especially for very small nanoparticles (1-3 nm). Conventional reconstruction methods successful for catalysts with 5d metals (e.g., Au, Pt, or Ir) fail to detect 4d metal particles in electron tomography reconstructions, as their contrasts cannot be effectively separated from those of the underlying support crystallites. To address this complex 3D characterization challenge, we have developed a full deep learning (DL) pipeline that combines multiple neural networks, each one optimized for a specific image-processing task. In particular, single-image super-resolution (SR) techniques are used to intelligently denoise and enhance the quality of the tomographic tilt series. U-net generative adversarial network algorithms are employed for image restoration and correcting alignment-related artifacts in the tilt series. Finally, semantic segmentation, utilizing a U-net-based convolutional neural network, splits the 3D volumes into their components (metal and support). This approach enables the visualization of subnanometer-sized 4d metal particles and allows for the quantitative extraction of catalytically relevant structural information, such as particle size, sphericity, and truncation, from compressed sensing electron tomography volume reconstructions. We demonstrate the potential of this approach by characterizing nanoparticles of a metal widely used in catalysis, Pd (Z = 46), supported on CeO2, a very high density (7.22 g/cm3) oxide involving a quite high-atomic-number element, Ce (Z = 58).

Quantitative Evaluation of Supported Catalysts Key Properties from Electron Tomography Studies: Assessing Accuracy Using Material-Realistic 3D-Models

Electron Tomography (ET) reconstructions can be analysed, via segmentation techniques, to obtain quantitative, 3D-information about individual nanoparticles in supported catalysts. This includes values of parameters out of reach for any other technique, like their volume and surface, which are required to determine the dispersion of the supported particle system or the specific surface area of the support; two figures that play a major role in the performance of this type of catalysts.

However, both the experimental conditions during the acquisition of the tilt series and the limited fidelity of the reconstruction and segmentation algorithms, restrict the quality of the ET results and introduce an undefined amount of error both in the qualitative features of the reconstructions and in all the quantitative parameters measured from them.

Here, a method based on the use of well-defined 3D geometrical models (phantoms), with morphological features closely resembling those observed in experimental images of an Au/CeO2 catalyst, has been devised to provide a precise estimation of the accuracy of the reconstructions. Using this approach, the influence of noise and the number of projections on the errors of reconstructions obtained using a Total Variation Minimization in 3D (TVM-3D) algorithm have been determined. Likewise, the benefits of using smart denoising techniques based on Undecimated Wavelet Transforms (UWT) have been also evaluated.

The results clearly reveal a large impact of usual noise levels on both the quality of the reconstructions and nanometrological measurement errors. Quantitative clues about the key role of UWT to largely compensate them are also provided.

Three-Dimensional TEM Study of Dendrimer-Encapsulated Pt Nanoparticles for Visualizing Structural Characteristics of the Whole Organic-Inorganic Hybrid Nanostructure

Here, we report three-dimensional (3-D) visualization of dendrimer-encapsulated Pt nanoparticles (Pt DENs) by using 3-D electron tomography to reveal intricate structural characteristics of their whole organic-inorganic hybrid nanostructure. We reconstructed the 3-D spatial volume of Pt DENs by back-projecting a tilt series of two-dimensional (2-D) projections of Pt nanoparticles encapsulated inside dendrimers negatively stained with uranyl acetate. The direct 3-D visualization of Pt DENs elucidated their encapsulation characteristics with the spatial imaging of Pt nanoparticles embraced inside dendrimers in three dimensions. The encapsulation characteristics of Pt DENs were further verified with selective electrochemical poisoning experiments. In addition, quantitative 3-D structural characterization of Pt DENs provided more accurate and precise size distributions of nanoparticles than those obtained from conventional 2-D transmission electron microscopy analysis relying only on a 3-D structure projected on a 2-D plane.

Characterization techniques for nanoparticles: comparison and complementarity upon studying nanoparticle properties

Nanostructures have attracted huge interest as a rapidly growing class of materials for many applications. Several techniques have been used to characterize the size, crystal structure, elemental composition and a variety of other physical properties of nanoparticles. In several cases, there are physical properties that can be evaluated by more than one technique. Different strengths and limitations of each technique complicate the choice of the most suitable method, while often a combinatorial characterization approach is needed. In addition, given that the significance of nanoparticles in basic research and applications is constantly increasing, it is necessary that researchers from separate fields overcome the challenges in the reproducible and reliable characterization of nanomaterials, after their synthesis and further process (e.g. annealing) stages. The principal objective of this review is to summarize the present knowledge on the use, advances, advantages and weaknesses of a large number of experimental techniques that are available for the characterization of nanoparticles. Different characterization techniques are classified according to the concept/group of the technique used, the information they can provide, or the materials that they are destined for. We describe the main characteristics of the techniques and their operation principles and we give various examples of their use, presenting them in a comparative mode, when possible, in relation to the property studied in each case.

The benefit of thresholding carbon layers in electron tomographic tilt series by intensity downshifting

When performing electron tomography, tilt series of images are often acquired from samples that contain unwanted carbonaceous material, such as an embedding resin, a thin carbon support film or hydrocarbon contamination. The presence of such layers can introduce artefacts in reconstructions, obscuring features of interest. Here, we illustrate the benefit of preprocessing a high-angle annular dark-field tomographic tilt series by thresholding unwanted low-density materials using a simple intensity downshifting procedure. The resulting tomograms have fewer artefacts and segmentation can be performed more accurately. We present two representative examples taken from studies of catalyst nanoparticles and amyloid plaque core material from the human brain.

Mesoscale Characterization of Nanoparticles Distribution Using X-ray Scattering

The properties of many functional materials depend critically on the spatial distribution of an active phase within a support. In the case of solid catalysts, controlling the spatial distribution of metal (oxide) nanoparticles at the mesoscopic scale offers new strategies to tune their performance and enhance their lifetimes. However, such advanced control requires suitable characterization methods, which are currently scarce. Here, we show how the background in small-angle X-ray scattering patterns can be analyzed to quantitatively access the mesoscale distribution of nanoparticles within supports displaying hierarchical porosity. This is illustrated for copper catalysts supported on meso- and microporous silica displaying distinctly different metal distributions. Results derived from X-ray scattering are in excellent agreement with electron tomography. Our strategy opens unprecedented prospects for understanding the properties and to guide the synthesis of a wide array of functional nanomaterials.

Removing the effects of the "dark matter" in tomography

Electron tomography (ET) using different imaging modes has been progressively consolidating its position as a key tool in materials science. The fidelity of a tomographic reconstruction, or tomogram, is affected by several experimental factors. Most often, an unrealistic cloud of intensity that does not correspond to a real material phase of the specimen ("dark matter") blurs the tomograms and enhances artefacts arising from the missing wedge (MW). Here we show that by simple preprocessing of the background level of any tomographic tilt series, it is possible to minimise the negative effects of that "dark matter". Iterative reconstruction algorithms converge better, leading to tomograms with fewer streaking artefacts from the MW, more contrast, and increased accuracy. The conclusions are valid irrespective of the imaging mode used, and the methodology improves the segmentation and visualisation of tomograms of both crystalline and amorphous materials. We show examples of HAADF STEM and BF TEM tomography.

A quantitative electron tomography study of ruthenium particles on the interior and exterior surfaces of carbon nanotubes

The efficiency of filling carbon nanotubes (CNTs) by ultrasound-assisted wet impregnation is quantified by electron tomography (ET). For image analysis, a method that combines edge detection with single-value thresholding is proposed and validated. A high proportion (80 wt %) of the ruthenium was deposited inside the tube at an average particle size of 2-4 nm. Particles located on the outer surface of the CNT had a size of 1-3 nm. The local ruthenium loading measured by ET (3.2 wt %) closely matched the value from elemental analysis (3.5 wt %). In addition, a few 1 nm-sized ruthenium particles were detected inside the carbon wall, which contained pores/cracks. Direct imaging and quantification is a powerful tool to understand and possibly model the unique properties of CNT-based catalysts.

Measuring Location, Size, Distribution, and Loading of NiO Crystallites in Individual SBA-15 Pores by Electron Tomography

By the combination of electron tomography with image segmentation, the properties of 299 NiO crystallites contained in 6 SBA-15 pores were studied. A statistical analysis of the particle size showed that crystallites between 2 and 6 nm were present with a distribution maximum at 3 and 4 nm, for the number-weighted and volume-weighted curves, respectively. Interparticle distances between nearest neighbors were 1-3 nm with very few isolated crystallites. In the examined pores, a local loading twice the applied average of 24 wt % NiO was found. This suggests that a very high local loading combined with a high dispersion is achievable.

Antiangiogenic and anti-invasive effects of sunitinib on experimental human glioblastoma

Angiogenesis inhibitors appear to be promising therapies for highly vascularized tumors such as glioblastoma multiforme (GBM). Sunitinib is an oral multitargeted tyrosine kinase inhibitor with both antiangiogenic and antitumor activities due to selective inhibition of various receptor tyrosine kinases, including those important for angiogenesis (vascular endothelial growth factor receptors and platelet-derived growth factor receptors). Here we evaluated the antitumor activities of sunitinib on orthotopic models of GBM in vitro and in vivo. Sunitinib potently inhibited angiogenesis that was stimulated by implantation of U87MG and GL15 cells into organotypic brain slices at concentrations as low as 10 nM. At high dose (10 microM), sunitinib induced direct antiproliferative and proapoptotic effects on GL15 cells and decreased invasion of these cells implanted into brain slices by 49% (p < 0.001). Treatment was associated with decreases in Src (35%) and focal adhesion kinase (44%) phosphorylation. However, anti-invasive activity was not observed in vivo at the highest dose level utilized (80 mg/kg per day). Survival experiments involving athymic mice bearing intracerebral U87MG GBM demonstrated that oral administration of 80 mg/kg sunitinib (five days on, two days off) improved median survival by 36% (p < 0.0001). Sunitinib treatment caused a 74% reduction in microvessel density (p < 0.05), an increase in tumor necrosis, and a decrease in number of GBM cells positive for MIB antibody. Sunitinib exhibited potent antiangiogenic activity that was associated with a meaningful prolongation of survival of mice bearing intracerebral GBM. These data support the potential utility of sunitinib in the treatment of GBM.

Chemical Activity of Palladium Clusters: Sorption of Hydrogen

Samples of Pd/SiO2 with average cluster size of palladium (d(Pd)) ranging from 1 to 10 nm were prepared by different methods, such as incipient wetness impregnation (IW), ion exchange (IE), and sol-gel (SG). The dispersion (D(Pd)) for prepared Pd/SiO2 samples varied with the method of preparation and showed a trend of IW < IE < SG. Chemical interaction between the dispersed nanopalladium and hydrogen gas was calorimetrically studied as a function of hydrogen uptake. Measured profiles of interaction energy vary with the d(Pd) of studied samples. The initial heat of hydrogen chemisorption (Q(Hi)) also steeply increases for particles with d(Pd) smaller than 2 nm.