Showing 1–17 of 17 results for author: Xin, H L

Human Perception-Inspired Grain Segmentation Refinement Using Conditional Random Fields

Authors: Doruk Aksoy, Huolin L. Xin, Timothy J. Rupert, William J. Bowman

Abstract: Automated detection of grain boundaries (GBs) in electron microscope images of polycrystalline materials could help accelerate the nanoscale characterization of myriad engineering materials and novel materials under scientific research. Accurate segmentation of interconnected line networks, such as GBs in polycrystalline material microstructures, poses a significant challenge due to the fragmented… ▽ More Automated detection of grain boundaries (GBs) in electron microscope images of polycrystalline materials could help accelerate the nanoscale characterization of myriad engineering materials and novel materials under scientific research. Accurate segmentation of interconnected line networks, such as GBs in polycrystalline material microstructures, poses a significant challenge due to the fragmented masks produced by conventional computer vision (CV) algorithms, including convolutional neural networks. These algorithms struggle with thin masks, often necessitating post-processing for effective contour closure and continuity. Previous approaches in this domain have typically relied on custom post-processing techniques that are problem-specific and heavily dependent on the quality of the mask obtained from a CV algorithm. Addressing this issue, this paper introduces a fast, high-fidelity post-processing technique that is universally applicable to segmentation masks of interconnected line networks. Leveraging domain knowledge about grain boundary connectivity, this method employs conditional random fields and perceptual grouping rules to refine segmentation masks of any image with a discernible grain structure. This approach significantly enhances segmentation mask accuracy by correctly reconstructing fragmented GBs in electron microscopy images of a polycrystalline oxide. The refinement improves the statistical representation of the microstructure, reflected by a 51 % improvement in a grain alignment metric that provides a more physically meaningful assessment of complex microstructures than conventional metrics. This method enables rapid and accurate characterization, facilitating an unprecedented level of data analysis and improving the understanding of GB networks, making it suitable for a range of disciplines where precise segmentation of interconnected line networks is essential. △ Less

Submitted 4 November, 2025; v1 submitted 15 December, 2023; originally announced December 2023.

Comments: v3 = published version (OA, CC BY 4.0)

Journal ref: Mater. Charact. 230, 115694 (2025)

MnEdgeNet -- Accurate Decomposition of Mixed Oxidation States for Mn XAS and EELS L2,3 Edges without Reference and Calibration

Authors: Huolin L. Xin, Mike Hu

Abstract: Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer, and redox centers for electronic, electrocatalytic, and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date,… ▽ More Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer, and redox centers for electronic, electrocatalytic, and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurement of the Mn L2,3 edges is straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with a three-element oxidation state composition label. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. △ Less

Submitted 20 October, 2022; originally announced October 2022.

Periodic Artifact Reduction in Fourier transforms of Full Field Atomic Resolution Images

Authors: Robert Hovden, Yi Jiang, Huolin L. Xin, Lena F. Kourkoutis

Abstract: The discrete Fourier transform is among the most routine tools used in high-resolution scanning / transmission electron microscopy (S/TEM). However, when calculating a Fourier transform, periodic boundary conditions are imposed and sharp discontinuities between the edges of an image cause a cross patterned artifact along the reciprocal space axes. This artifact can interfere with the analysis of r… ▽ More The discrete Fourier transform is among the most routine tools used in high-resolution scanning / transmission electron microscopy (S/TEM). However, when calculating a Fourier transform, periodic boundary conditions are imposed and sharp discontinuities between the edges of an image cause a cross patterned artifact along the reciprocal space axes. This artifact can interfere with the analysis of reciprocal lattice peaks of an atomic resolution image. Here we demonstrate that the recently developed Periodic Plus Smooth Decomposition technique provides a simple, efficient method for reliable removal of artifacts caused by edge discontinuities. In this method, edge artifacts are reduced by subtracting a smooth background that solves Poisson's equation with boundary conditions set by the image's edges. Unlike the traditional windowed Fourier transforms, Periodic Plus Smooth Decomposition maintains sharp reciprocal lattice peaks from the image's entire field of view. △ Less

Submitted 14 October, 2022; originally announced October 2022.

Journal ref: Microscopy and Microanalysis, 21(2), 436-441 (2015)

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

Authors: Lingli Kong, Zhengran Ji, Huolin L. Xin

Abstract: The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based a… ▽ More The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal-noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9 %, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy. △ Less

Submitted 26 September, 2022; originally announced September 2022.

Comments: 23 pages, 6 figures

Bulk High-Entropy Hexaborides

Authors: Mingde Qin, Qizhang Yan, Yi Liu, Haoren Wang, Chunyang Wang, Tianjiao Lei, Kenneth S. Vecchio, Huolin L. Xin, Timothy J. Rupert, Jian Luo

Abstract: For the first time, a group of CaB6-typed cubic rare earth high-entropy hexaborides have been successfully fabricated into dense bulk pellets (>98.5% in relative densities). The specimens are prepared from elemental precursors via in-situ metal-boron reactive spark plasma sintering. The sintered bulk pellets are determined to be single-phase without any detectable oxides or other secondary phases.… ▽ More For the first time, a group of CaB6-typed cubic rare earth high-entropy hexaborides have been successfully fabricated into dense bulk pellets (>98.5% in relative densities). The specimens are prepared from elemental precursors via in-situ metal-boron reactive spark plasma sintering. The sintered bulk pellets are determined to be single-phase without any detectable oxides or other secondary phases. The homogenous elemental distributions have been confirmed at both microscale and nanoscale. The Vickers microhardness are measured to be 16-18 GPa at a standard indentation load of 9.8 N. The nanoindentation hardness and Young's moduli have been measured to be 19-22 GPa and 190-250 GPa, respectively, by nanoindentation test using a maximum load of 500 mN. The material work functions are determined to be 3.7-4.0 eV by ultraviolet photoelectron spectroscopy characterizations, which are significantly higher than that of LaB6. △ Less

Submitted 11 April, 2021; originally announced April 2021.

TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images

Authors: Ruoqian Lin, Rui Zhang, Chunyang Wang, Xiao-Qing Yang, Huolin L. Xin

Abstract: Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences… ▽ More Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data. △ Less

Submitted 20 February, 2021; v1 submitted 16 December, 2020; originally announced December 2020.

0.71-Å resolution electron tomography enabled by deep learning aided information recovery

Authors: Chunyang Wang, Guanglei Ding, Yitong Liu, Huolin L. Xin

Abstract: Electron tomography, as an important 3D imaging method, offers a powerful method to probe the 3D structure of materials from the nano- to the atomic-scale. However, as a grant challenge, radiation intolerance of the nanoscale samples and the missing-wedge-induced information loss and artifacts greatly hindered us from obtaining 3D atomic structures with high fidelity. Here, for the first time, by… ▽ More Electron tomography, as an important 3D imaging method, offers a powerful method to probe the 3D structure of materials from the nano- to the atomic-scale. However, as a grant challenge, radiation intolerance of the nanoscale samples and the missing-wedge-induced information loss and artifacts greatly hindered us from obtaining 3D atomic structures with high fidelity. Here, for the first time, by combining generative adversarial models with state-of-the-art network architectures, we demonstrate the resolution of electron tomography can be improved to 0.71 angstrom which is the highest three-dimensional imaging resolution that has been reported thus far. We also show it is possible to recover the lost information and remove artifacts in the reconstructed tomograms by only acquiring data from -50 to +50 degrees (44% reduction of dosage compared to -90 to +90 degrees full tilt series). In contrast to conventional methods, the deep learning model shows outstanding performance for both macroscopic objects and atomic features solving the long-standing dosage and missing-wedge problems in electron tomography. Our work provides important guidance for the application of machine learning methods to tomographic imaging and sheds light on its applications in other 3D imaging techniques. △ Less

Submitted 27 March, 2020; originally announced March 2020.

Channeling of a sub-angstrom electron beam in a crystal mapped to two-dimensional molecular orbitals

Authors: Robert Hovden, Huolin L. Xin, David A. Muller

Abstract: The propagation of high-energy electrons in crystals is in general a complicated multiple scattering problem. However, along high-symmetry zone axes the problem can be mapped to the time evolution of a two-dimensional (2D) molecular system. Each projected atomic column can be approximated by the potential of a 2D screened hydrogenic atom. When two columns are in close proximity, their bound states… ▽ More The propagation of high-energy electrons in crystals is in general a complicated multiple scattering problem. However, along high-symmetry zone axes the problem can be mapped to the time evolution of a two-dimensional (2D) molecular system. Each projected atomic column can be approximated by the potential of a 2D screened hydrogenic atom. When two columns are in close proximity, their bound states overlap and form analogs to molecular orbitals. For sub-angstrom electron beams, excitation of anti-symmetric orbitals can result in the failure of the simple incoherent imaging approximation. As a result, the standard resolution test and the one-to-one correspondence of atomic positions of a crystal imaged along a zone-axis with closely spaced projected columns ("dumbbells") can fail dramatically at finite and realistic sample thicknesses. This is demonstrated experimentally in high angle annular dark field scanning transmission electron microscope (HAADF STEM) images of [211]-oriented Si showing an apparent inter-column spacing of 1.28(+-.09) Angstroms, over 64% larger than the actual 0.78 Angstrom spacing. Furthermore, the apparent spacing can be tuned with sample thickness and probe size to produce a larger, smaller, or even the actual spacing under conditions when the peaks of two adjacent Si columns should not even have been resolved given the electron probe size. △ Less

Submitted 5 December, 2012; originally announced December 2012.

Journal ref: Phys. Rev. B 86, 195415 (2012)

Revealing Correlation of Valence State with Nanoporous Structure in Cobalt Catalyst Nanoparticles by in Situ Environmental TEM

Authors: Huolin L. Xin, Elzbieta A. Pach, Rosa E. Diaz, Eric A. Stach, Miquel Salmeron, Haimei Zheng

Abstract: Simultaneously probing the electronic structure and morphology of materials at the nanometer or atomic scale while a chemical reaction proceeds is significant for understanding the underlying reaction mechanisms and optimizing a materials design. This is especially important in the study of nanoparticle catalysts, yet such experiments have rarely been achieved. Utilizing an environmental transmiss… ▽ More Simultaneously probing the electronic structure and morphology of materials at the nanometer or atomic scale while a chemical reaction proceeds is significant for understanding the underlying reaction mechanisms and optimizing a materials design. This is especially important in the study of nanoparticle catalysts, yet such experiments have rarely been achieved. Utilizing an environmental transmission electron microscope (ETEM) equipped with a differentially pumped gas cell, we are able to conduct nanoscopic imaging and electron energy loss spectroscopy (EELS) in situ for cobalt catalysts under reaction conditions. Analysis revealed quantitative correlation of the cobalt valence states to the particles' nanoporous structures. The in situ experiments were performed on nanoporous cobalt particles coated with silica while a 15 mTorr hydrogen environment was maintained at various temperatures (300-600\degreeC). When the nanoporous particles were reduced, the valence state changed from cobalt oxide to metallic cobalt and concurrent structural coarsening was observed. In situ mapping of the valence state and the corresponding nanoporous structures allows quantitatively analysis necessary for understanding and improving the mass activity and lifetime of cobalt-based catalysts, i.e., for Fischer-Tropsch synthesis that converts carbon monoxide and hydrogen into fuels, and uncovering the catalyst optimization mechanisms. △ Less

Submitted 4 April, 2012; originally announced April 2012.

Comments: ACS Nano, accepted

Journal ref: ACS Nano, 2012, 6, pp 4241-4247

On-column 2p bound state with topological charge \pm1 excited by an atomic-size vortex beam in an aberration-corrected scanning transmission electron microscope

Authors: Huolin L. Xin, Haimei Zheng

Abstract: Atomic-size vortex beams have great potential in probing materials' magnetic moment at atomic scales. However, the limited depth of field of vortex beams constrains the probing depth in which the helical phase front is preserved. On the other hand, electron channeling in crystals can counteract beam divergence and extend the vortex beam without disrupting its topological charge. Specifically, in t… ▽ More Atomic-size vortex beams have great potential in probing materials' magnetic moment at atomic scales. However, the limited depth of field of vortex beams constrains the probing depth in which the helical phase front is preserved. On the other hand, electron channeling in crystals can counteract beam divergence and extend the vortex beam without disrupting its topological charge. Specifically, in this paper, we report atomic vortex beams with topological charge \pm1 can be coupled to the 2p columnar bound states and propagate for more 50 nm without being dispersed and losing its helical phase front. We gave numerical solutions to the 2p columnar orbitals and tabulated the characteristic size of the 2p states of two typical elements, Co and Dy, for various incident beam energies and various atomic densities. The tabulated numbers allow estimates of the optimal convergence angle for maximal coupling to 2p columnar orbital. We also have developed analytic formulae for beam energy, convergence-angle, and hologram dependent scaling for various characteristic sizes. These length scales are useful for the design of pitch-fork apertures and operations of microscopes in the vortex-beam imaging mode. △ Less

Submitted 30 March, 2012; originally announced April 2012.

Comments: 30 pages, 7 figures, Microscopy and Microanalysis, in press

Journal ref: Microscopy and Microanalysis,18, 2012, pp 711-719

3-D Tracking and Visualization of Hundreds of Pt-Co Fuel Cell Nanocatalysts During Electrochemical Aging

Authors: Yingchao Yu, Huolin L. Xin, Robert M. Hovden, Deli Wang, Eric D. Rus, Julia Mundy, David A. Muller, Héctor D. Abruña

Abstract: We present an electron tomography method that allows for the identification of hundreds of electrocatalyst nanoparticles with one-to-one correspondence before and after electrochemical aging. This method allows us to track, in three-dimensions (3-D), the trajectories and morphologies of each Pt-Co nanocatalyst on a fuel cell carbon support. The use of atomic-scale electron energy loss spectroscopi… ▽ More We present an electron tomography method that allows for the identification of hundreds of electrocatalyst nanoparticles with one-to-one correspondence before and after electrochemical aging. This method allows us to track, in three-dimensions (3-D), the trajectories and morphologies of each Pt-Co nanocatalyst on a fuel cell carbon support. The use of atomic-scale electron energy loss spectroscopic imaging enables the correlation of performance degradation of the catalyst with changes in particle/inter-particle morphologies, particle-support interactions and the near-surface chemical composition. We found that, aging of the catalysts under normal fuel cell operating conditions (potential scans from +0.6 V to +1.0 V for 30,000 cycles) gives rise to coarsening of the nanoparticles, mainly through coalescence, which in turn leads to the loss of performance. The observed coalescence events were found to be the result of nanoparticle migration on the carbon support during potential cycling. This method provides detailed insights into how nanocatalyst degradation occurs in proton exchange membrane fuel cells (PEMFCs), and suggests that minimization of particle movement can potentially slow down the coarsening of the particles, and the corresponding performance degradation. △ Less

Submitted 27 December, 2011; originally announced December 2011.

Comments: Nano Letters, accepted

Data Processing For Atomic Resolution EELS

Authors: Paul Cueva, Robert Hovden, Julia A. Mundy, Huolin L. Xin, David A. Muller

Abstract: The high beam current and sub-angstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopic (EELS) mapping with atomic resolution. These spectral maps are often dose-limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of… ▽ More The high beam current and sub-angstrom resolution of aberration-corrected scanning transmission electron microscopes has enabled electron energy loss spectroscopic (EELS) mapping with atomic resolution. These spectral maps are often dose-limited and spatially oversampled, leading to low counts/channel and are thus highly sensitive to errors in background estimation. However, by taking advantage of redundancy in the dataset map one can improve background estimation and increase chemical sensitivity. We consider two such approaches- linear combination of power laws and local background averaging-that reduce background error and improve signal extraction. Principal components analysis (PCA) can also be used to analyze spectrum images, but the poor peak-to-background ratio in EELS can lead to serious artifacts if raw EELS data is PCA filtered. We identify common artifacts and discuss alternative approaches. These algorithms are implemented within the Cornell Spectrum Imager, an open source software package for spectroscopic analysis. △ Less

Submitted 13 December, 2011; originally announced December 2011.

Journal ref: Microscopy and Microanalysis, Vol. 18 pp 667-675 (2012)

Atomic-resolution spectroscopic imaging of ensembles of nanocatalyst particles across the life of a fuel cell

Authors: Huolin L. Xin, Julia A. Mundy, Zhongyi Liu, Randi Cabezas, Robert Hovden, Lena Fitting Kourkoutis, Junliang Zhang, Nalini P. Subramanian, Rohit Makharia, Frederick T. Wagner, David A. Muller

Abstract: The thousandfold increase in data-collection speed enabled by aberration-corrected optics allows us to overcome an electron microscopy paradox - how to obtain atomic-resolution chemical structure in individual nanoparticles, yet record a statistically significant sample from an inhomogeneous population. This allowed us to map hundreds of Pt-Co nanoparticles to show atomic-scale elemental distribut… ▽ More The thousandfold increase in data-collection speed enabled by aberration-corrected optics allows us to overcome an electron microscopy paradox - how to obtain atomic-resolution chemical structure in individual nanoparticles, yet record a statistically significant sample from an inhomogeneous population. This allowed us to map hundreds of Pt-Co nanoparticles to show atomic-scale elemental distributions across different stages of the catalyst aging in a proton-exchange-membrane fuel cell, and relate Pt-shell thickness to treatment, particle size, surface orientation, and ordering. △ Less

Submitted 6 December, 2011; v1 submitted 29 November, 2011; originally announced November 2011.

Comments: 28 pages, 5 figures, accepted, nano letters

Journal ref: Nano Lett., 2012, 12 (1), pp 490--497

Determining On-Axis Crystal Thickness with Quantitative Position-Averaged Incoherent Bright-Field Signal in an Aberration-corrected STEM

Authors: Huolin L. Xin, Ye Zhu, David A. Muller

Abstract: An accurate determination of specimen thickness is essential for quantitative analytical electron microscopy. Here we demonstrate that a position-averaged incoherent bright-field signal recorded on an absolute scale can be used to determine the thickness of on-axis crystals with a precision of \pm1.6 nm. This method measures both the crystalline and the non-crystalline parts (surface amorphous lay… ▽ More An accurate determination of specimen thickness is essential for quantitative analytical electron microscopy. Here we demonstrate that a position-averaged incoherent bright-field signal recorded on an absolute scale can be used to determine the thickness of on-axis crystals with a precision of \pm1.6 nm. This method measures both the crystalline and the non-crystalline parts (surface amorphous layers) of the sample. However, it avoids the systematic error resulting from surface plasmons contributions to the inelastic mean free path thickness estimated by electron energy loss spectroscopy. △ Less

Submitted 27 March, 2012; v1 submitted 31 October, 2011; originally announced October 2011.

Comments: 25 pages, 6 figures. Related to http://arxiv.org/abs/1110.5018. Microscopy and Microanalysis, accepted

Journal ref: Microscopy and Microanalysis, 18, 2012, pp 720-727

Quantitative chemical mapping at the atomic scale

Authors: Huolin L. Xin, Christian Dwyer, David A. Muller

Abstract: Atomic-scale mapping of the chemical elements in materials is now possible using aberration-corrected electron microscopes but delocalization and multiple scattering can confound image interpretation. Here we report atomic-resolution measurements with the elastic and inelastic signals acquired on an absolute scale. By including dynamical scattering in both the elastic and inelastic channels we obt… ▽ More Atomic-scale mapping of the chemical elements in materials is now possible using aberration-corrected electron microscopes but delocalization and multiple scattering can confound image interpretation. Here we report atomic-resolution measurements with the elastic and inelastic signals acquired on an absolute scale. By including dynamical scattering in both the elastic and inelastic channels we obtain quantitative agreement between theory and experiment. Our results enable a close scrutiny of the inelastic scattering physics and demonstrate the possibility of element-specific atom counting. △ Less

Submitted 23 October, 2011; originally announced October 2011.

Pt-Decorated PdCo@Pd/C Core-Shell Nanoparticles with Enhanced Stability and Electrocatalytic Activity for Oxygen Reduction Reaction

Authors: Deli Wang, Huolin L. Xin, Yingchao Yu, Hongsen Wang, Eric Rus, David A. Muller, Hector D. Abruña

Abstract: A simple method for the preparation of PdCo@Pd core-shell nanoparticles supported on carbon has been developed using an adsorbate-induced surface segregation effect. The stability and electrocatalytic activity for the oxygen reduction of PdCo@Pd nanoparticles was enhanced by a small amount of Pt, deposited via a spontaneous displacement reaction. The facile method described herein is suitable for… ▽ More A simple method for the preparation of PdCo@Pd core-shell nanoparticles supported on carbon has been developed using an adsorbate-induced surface segregation effect. The stability and electrocatalytic activity for the oxygen reduction of PdCo@Pd nanoparticles was enhanced by a small amount of Pt, deposited via a spontaneous displacement reaction. The facile method described herein is suitable for large-scale lower cost production and significantly lowers the Pt loading and thus cost. The as-prepared PdCo@Pd and Pd-decorated PdCo@Pd nanocatalysts have higher methanol-tolerance for the ORR when compared to Pt/C, and are promising cathode catalysts for fuel cell applications. △ Less

Submitted 21 November, 2010; originally announced November 2010.

Comments: 12 pages, 10 figures, accepted by JACS

Journal ref: J. Am. Chem. Soc., 2010, 132 (50), pp 17664--17666

Extended Depth of Field for High Resolution Scanning Transmission Electron Microscopy

Authors: Robert Hovden, Huolin L. Xin, David A. Muller

Abstract: Aberration-corrected scanning transmission electron microscopes (STEM) provide sub-angstrom lateral resolution; however, the large convergence angle greatly reduces the depth of field. For microscopes with a small depth of field, information outside of the focal plane quickly becomes blurred and less defined. It may not be possible to image some samples entirely in focus. Extended depth-of-field t… ▽ More Aberration-corrected scanning transmission electron microscopes (STEM) provide sub-angstrom lateral resolution; however, the large convergence angle greatly reduces the depth of field. For microscopes with a small depth of field, information outside of the focal plane quickly becomes blurred and less defined. It may not be possible to image some samples entirely in focus. Extended depth-of-field techniques, however, allow a single image, with all areas in-focus, to be extracted from a series of images focused at a range of depths. In recent years, a variety of algorithmic approaches have been employed for bright field optical microscopy. Here, we demonstrate that some established optical microscopy methods can also be applied to extend the ~6 nm depth of focus of a 100 kV 5th-order aberration-corrected STEM (alpha_max = 33 mrad) to image Pt-Co nanoparticles on a thick vulcanized carbon support. These techniques allow us to automatically obtain a single image with all the particles in focus as well as a complimentary topography map. △ Less

Submitted 21 October, 2010; originally announced October 2010.

Comments: Accepted, Microscopy and Microanalysis