ABSTRACT
We develop a combined theoretical and experimental method for estimating the amount of heating that occurs in metallic nanoparticles that are being imaged in an electron microscope. We model the thermal transport between the nanoparticle and the supporting material using molecular dynamics and equivariant neural network potentials. The potentials are trained to Density Functional Theory (DFT) calculations, and we show that an ensemble of potentials can be used as an estimate of the errors the neural network make in predicting energies and forces. This can be used both to improve the networks during the training phase, and to validate the performance when simulating systems too big to be described by DFT. The energy deposited into the nanoparticle by the electron beam is estimated by measuring the mean free path of the electrons and the average energy loss, both are done with Electron Energy Loss Spectroscopy (EELS) within the microscope. In combination, this allows us to predict the heating incurred by a nanoparticle as a function of its size, its shape, the support material, and the electron beam energy and intensity.
ABSTRACT
Motivated by the need for low electron dose transmission electron microscopy imaging, we report the optimal frame dose (i.e.e-/Å2) range for object detection and segmentation tasks with neural networks. The MSD-net architecture shows promising abilities over the industry standard U-net architecture in generalising to frame doses below the range included in the training set, for both simulated and experimental images. It also presents a heightened ability to learn from lower dose images. The MSD-net displays mild visibility of a Au nanoparticle at 20-30 e-/Å2, and converges at 200 e-/Å2 where a full segmentation of the nanoparticle is achieved. Between 30 and 200 e-/Å2 object detection applications are still possible. This work also highlights the importance of modelling the modulation transfer function when training with simulated images for applications on images acquired with scintillator based detectors such as the Gatan Oneview camera. A parametric form of the modulation transfer function is applied with varying ranges of parameters, and the effects on low electron dose segmentation is presented.
ABSTRACT
Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolutional neural networks can be used to reconstruct the exit wave from a short focal series of HRTEM images, with a fidelity comparable to conventional exit wave reconstruction. We use a fully convolutional neural network based on the U-Net architecture, and demonstrate that we can train it on simulated exit waves and simulated HRTEM images of graphene-supported molybdenum disulphide (an industrial desulfurization catalyst). We then apply the trained network to analyse experimentally obtained images from similar samples, and obtain exit waves that clearly show the atomically resolved structure of both the MoS2 nanoparticles and the graphene support. We also show that it is possible to successfully train the neural networks to reconstruct exit waves for 3400 different two-dimensional materials taken from the Computational 2D Materials Database of known and proposed two-dimensional materials.
ABSTRACT
In situ characterization of materials in their operational state is a highly active field of research. Investigating the structure and response of materials under stimuli that simulate real working environments for technological applications can provide new insight and unique input to the synthesis and design of novel materials. Over recent decades, experimental setups that allow different stimuli to be applied to a sample inside an electron microscope have been devised, built, and commercialized. In this review, we focus on the in situ investigation of optically active materials using transmission electron microscopy. We illustrate two different approaches for exposing samples to light inside the microscope column, explaining the importance of different aspects of their mechanical construction and choice of light source and materials. We focus on the technical challenges of the setups and provide details of the construction, providing the reader with input on deciding which setup will be more useful for a specific experiment. The use of these setups is illustrated using examples from the literature of relevance to photocatalysis and nanoparticle synthesis.
Subject(s)
Catalysis , Microscopy, Electron, TransmissionABSTRACT
The interface between metal catalyst and support plays a critical role in heterogeneous catalysis. An epitaxial interface is generally considered to be rigid, and tuning its intrinsic microstructure with atomic precision during catalytic reactions is challenging. Using aberration-corrected environmental transmission electron microscopy, we studied the interface between gold (Au) and a titanium dioxide (TiO2) support. Direct atomic-scale observations showed an unexpected dependence of the atomic structure of the Au-TiO2 interface with the epitaxial rotation of gold nanoparticles on a TiO2 surface during carbon monoxide (CO) oxidation. Taking advantage of the reversible and controllable rotation, we achieved in situ manipulation of the active Au-TiO2 interface by changing gas and temperature. This result suggests that real-time design of the catalytic interface in operating conditions may be possible.
ABSTRACT
Imaging a reaction taking place at the molecular level could provide direct information for understanding the catalytic reaction mechanism. We used in situ environmental transmission electron microscopy and a nanocrystalline anatase titanium dioxide (001) surface with (1 × 4) reconstruction as a catalyst, which provided highly ordered four-coordinated titanium "active rows" to realize real-time monitoring of water molecules dissociating and reacting on the catalyst surface. The twin-protrusion configuration of adsorbed water was observed. During the water-gas shift reaction, dynamic changes in these structures were visualized on these active rows at the molecular level.
ABSTRACT
The growth kinetics play key roles in determining the chirality distribution of the grown single-walled carbon nanotubes (SWCNTs). However, the lack of comprehensive understandings on the SWCNT's growth mechanism at the atomic scale greatly hinders SWCNT chirality-selective synthesis. Here, we establish a general model, where the dislocation theory is a specific case, to describe the etching agent-dependent growth kinetics of SWCNTs on solid catalyst particles. In particular, the growth kinetics of SWCNTs in the absence of etching agent is validated by both in situ environmental transmission electron microscopy and ex situ chemical vapor deposition growth of SWCNTs. On the basis of the new theory of SWCNT's growth kinetics, we successfully explained the selective growth of (2n, n) SWCNTs. This study provides another degree of freedom for SWCNT controlled synthesis and opens a new strategy to achieve chirality-selective synthesis of (2n, n) SWCNTs using solid catalysts.
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Global warming caused by burning of fossil fuels is indisputably one of mankind's greatest challenges in the 21st century. To reduce the ever-increasing CO2 emissions released into the atmosphere, dry solid adsorbents with large surface-to-volume ratio such as carbonaceous materials, zeolites, and metal-organic frameworks have emerged as promising material candidates for capturing CO2 . However, challenges remain because of limited CO2 /N2 selectivity and long-term stability. The effective adsorption of CO2 gas (≈12 mol kg-1 ) on individual sheets of 2D transition metal carbides (referred to as MXenes) is reported here. It is shown that exposure to N2 gas results in no adsorption, consistent with first-principles calculations. The adsorption efficiency combined with the CO2 /N2 selectivity, together with a chemical and thermal stability, identifies the archetype Ti3 C2 MXene as a new material for carbon capture (CC) applications.
ABSTRACT
Preventing sintering of supported nanocatalysts is an important issue in nanocatalysis. A feasible way is to choose a suitable support. However, whether the metal-support interactions promote or prevent the sintering has not been fully identified. Now, completely different sintering behaviors of Au nanoparticles on distinct anatase TiO2 surfaces have been determined by inâ situ TEM. The full inâ situ sintering processes of Au nanoparticles were visualized on TiO2 (101) surface, which coupled the Ostwald ripening and particle migration coalescence. In contrast, no sintering of Au on TiO2 anatase (001) surface was observed under the same conditions. This facet-dependent sintering mechanism is fully explained by the density function theory calculations. This work not only offers direct evidence of the important role of supports in the sintering process, but also provides insightful information for the design of sintering-resistant nanocatalysts.
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Strain analysis from high-resolution transmission electron microscopy (HRTEM) images offers a convenient tool for measuring strain in materials at the atomic scale. In this paper we present a theoretical study of the precision and accuracy of surface strain measurements directly from aberration-corrected HRTEM images. We examine the influence of defocus, crystal tilt and noise, and find that absolute errors of at least 1-2% strain should be expected. The model structures include surface relaxations determined using molecular dynamics, and we show that this is important for correctly evaluating the errors introduced by image aberrations.
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Controlling the charge transfer between a semiconducting catalyst carrier and the supported transition metal active phase represents an elite strategy for fine turning the electronic structure of the catalytic centers, hence their activity and selectivity. These phenomena have been theoretically and experimentally elucidated for oxide supports but remain poorly understood for carbons due to their complex nanoscale structure. Here, we combine advanced spectroscopy and microscopy on model Pd/C samples to decouple the electronic and surface chemistry effects on catalytic performance. Our investigations reveal trends between the charge distribution at the palladium-carbon interface and the metal's selectivity for hydrogenation of multifunctional chemicals. These electronic effects are strong enough to affect the performance of large (~5 nm) Pd particles. Our results also demonstrate how simple thermal treatments can be used to tune the interfacial charge distribution, hereby providing a strategy to rationally design carbon-supported catalysts.Control over charge transfer in carbon-supported metal nanoparticles is essential for designing new catalysts. Here, the authors show that thermal treatments effectively tune the interfacial charge distribution in carbon-supported palladium catalysts with consequential changes in hydrogenation performance.
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In order to controllably grow single-wall carbon nanotubes (SWCNTs), a better understanding of the growth processes and how they are influenced by external parameters such as catalyst and gaseous environment is required. Here, we present direct evidence of growth termination of individual SWCNTs and successive growth of additional SWCNTs on Co catalyst particles supported on MgO by means of environmental transmission electron microscopy. Such in situ observations reveal the plethora of solid carbon formations at the local scale while it is happening and thereby elucidate the multitude of configurations resulting from identical external synthesis conditions, which should be considered in the quest for controlled SWCNT growth. Using CO and a mixture of CO and H2 as carbon sources, we show that the growth of SWCNTs terminates with a reduced tube-catalyst adhesion strength. Two main reasons for the cessation are proposed: insufficient active carbon species and a certain amount of stress exerted at the tube-catalyst interface. Interestingly, it was observed that catalyst particles stayed active in terms of nucleating additional solid carbon structures after growth termination of the first SWCNT. These observations elucidate the importance of an in-depth understanding of the role of catalysts and carbon sources in the continued growth of SWCNTs. Furthermore, it serves as a guide for further control of carbon nanostructure synthesis via catalyst engineering and synthesis optimization.
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[This corrects the article DOI: 10.1186/s40679-017-0047-0.].
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The production of aromatic hydrocarbons from cellulose by zeolite-catalyzed fast pyrolysis involves a complex reaction network sensitive to the zeolite structure, crystallinity, elemental composition, porosity, and acidity. The interplay of these parameters under the reaction conditions represents a major roadblock that has hampered significant improvement in catalyst design for over a decade. Here, we studied commercial and laboratory-synthesized ZSM-5 zeolites and combined data from 10 complementary characterization techniques in an attempt to identify parameters common to high-performance catalysts. Crystallinity and framework aluminum site accessibility were found to be critical to achieve high aromatic yields. These findings enabled us to synthesize a ZSM-5 catalyst with enhanced activity, which offers the highest aromatic hydrocarbon yield reported to date.
Subject(s)
Biomass , Hydrocarbons, Aromatic/chemistry , Zeolites/chemistry , Catalysis , KineticsABSTRACT
Oxygen evolution was investigated on model, mass-selected RuO2 nanoparticles in acid, prepared by magnetron sputtering. Our investigations include electrochemical measurements, electron microscopy, scanning tunneling microscopy and X-ray photoelectron spectroscopy. We show that the stability and activity of nanoparticulate RuO2 is highly sensitive to its surface pretreatment. At 0.25 V overpotential, the catalysts show a mass activity of up to 0.6 A mg-1 and a turnover frequency of 0.65 s-1, one order of magnitude higher than the current state-of-the-art.
ABSTRACT
A connection between microscopic structure and macroscopic properties is expected for almost all material systems. High-resolution transmission electron microscopy is a technique offering insight into the atomic structure, but the analysis of large image series can be time consuming. The present work describes a method to automatically estimate the atomic structure in two-dimensional materials. As an example graphene is chosen, in which the positions of the carbon atoms are reconstructed. Lattice parameters are extracted in the frequency domain and an initial atom positioning is estimated. Next, a plausible neighborhood structure is estimated. Finally, atom positions are adjusted by simulation of a Markov random field model, integrating image evidence and the strong geometric prior. A pristine sample with high regularity and a sample with an induced hole are analyzed. False discovery rate-controlled large-scale simultaneous hypothesis testing is used as a statistical framework for interpretation of results. The first sample yields, as expected, a homogeneous distribution of carbon-carbon (C-C) bond lengths. The second sample exhibits regions of shorter C-C bond lengths with a preferred orientation, suggesting either strain in the structure or a buckling of the graphene sheet. The precision of the method is demonstrated on simulated model structures and by its application to multiple exposures of the two graphene samples.
ABSTRACT
Magnetite (Fe3O4) is an important magnetic mineral to Earth scientists, as it carries the dominant magnetic signature in rocks, and the understanding of its magnetic recording fidelity provides a critical tool in the field of palaeomagnetism. However, reliable interpretation of the recording fidelity of Fe3O4 particles is greatly diminished over time by progressive oxidation to less magnetic iron oxides, such as maghemite (γ-Fe2O3), with consequent alteration of remanent magnetization potentially having important geological significance. Here we use the complementary techniques of environmental transmission electron microscopy and off-axis electron holography to induce and visualize the effects of oxidation on the magnetization of individual nanoscale Fe3O4 particles as they transform towards γ-Fe2O3. Magnetic induction maps demonstrate a change in both strength and direction of remanent magnetization within Fe3O4 particles in the size range dominant in rocks, confirming that oxidation can modify the original stored magnetic information.
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Employing rationally designed model systems with precise atom-by-atom particle size control, we demonstrate by means of combining noninvasive in situ indirect nanoplasmonic sensing and ex situ scanning transmission electron microscopy that monomodal size-selected platinum cluster catalysts on different supports exhibit remarkable intrinsic sintering resistance even under reaction conditions. The observed stability is related to suppression of Ostwald ripening by elimination of its main driving force via size-selection. This study thus constitutes a general blueprint for the rational design of sintering resistant catalyst systems and for efficient experimental strategies to determine sintering mechanisms. Moreover, this is the first systematic experimental investigation of sintering processes in nanoparticle systems with an initially perfectly monomodal size distribution under ambient conditions.
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The majority of the nanowire synthesis methods utilize catalyst particles to guide the nanowire geometry. In contrast, catalyst-free methods are attractive for facile fabrication of pure nanowires without the need for catalyst preparation. Nonetheless, how nanowire growth is guided without a catalyst is still widely disputed and unclear. Here, we show that the nanowire growth during metal oxidation is limited by a nucleation of a new layer. On the basis of in situ transmission electron microscope investigations we found that the growth occurs layer by layer at the lowest specific surface energy planes. Atomic layers nucleate at the edge of twin boundary ridges and form a long-range ordering along the twin boundary. We anticipate our study to be a starting point to employ defects for nanowire growth control and consequently shaping the geometry of nanowires in a similar manner as in the catalyst-assisted growth method.