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Surface segregation is a common phenomenon in alloys exposed to reactive atmospheres, yet the atomic mechanisms underlying surface structure and composition dynamics remains largely unexplored. Using a combination of environmental transmission electron microscopy observations and atomistic modeling, here we report the surface segregation process of Pt atoms in a dilute Pt(Cu) alloy and determine the distribution of Pt atoms at both atomically flat and stepped surfaces of the Pt(Cu) alloy at elevated temperature and in a hydrogen gas atmosphere. Through directly probing Pt segregation, we find that Pt atoms segregated on the (100) surface exhibit a p(2×2) ordering, with ~25% Pt occupancy. In contrast, on the stepped (410) surface, hydrogen adsorption induces Pt segregation, initially occurring at the step edges, which then expands to the terrace sites upon increased hydrogen coverage, resulting in an ordered distribution of segregated Pt atoms with ~22% occupancy. These observations offer mechanistic insights into the structure and composition dynamics of the topmost atomic layer of the alloy in response to environmental stimuli and hold practical implications for the design and optimization of catalysts based on Pt group metals. 

Despite the critical role of sintering phenomena in constraining the long-term durability of nano-sized particles, a clear understanding of nanoparticle sintering has remained elusive due to the challenges in atomically tracking the neck initiation and discerning different mechanisms. Through the integration of in-situ transmission electron microscopy and atomistic modeling, this study uncovers the atomic dynamics governing the neck initiation of Pt-Fe nanoparticles via a surface self-diffusion process, allowing for coalescence without significant particle movement. Real-time imaging reveals that thermally activated surface morphology changes in individual nanoparticles induce significant surface self-diffusion. The kinetic entrapment of self-diffusing atoms in the gaps between closely spaced nanoparticles leads to the nucleation and growth of atomic layers for neck formation. This surface self-diffusion-driven sintering process is activated at a relatively lower temperature compared to the classic Ostwald ripening and particle migration and coalescence processes. The fundamental insights have practical implications for manipulating the morphology, size distribution, and stability of nanostructures by leveraging surface self-diffusion processes. 

The microscopic mechanisms underpinning the spontaneous surface passivation of metals from ubiquitous water have remained largely elusive. Here, using in situ environmental electron microscopy to atomically monitor the reaction dynamics between aluminum surfaces and water vapor, we provide direct experimental evidence that the surface passivation results in a bilayer oxide film consisting of a crystalline-like Al(OH)₃top layer and an inner layer of amorphous Al₂O₃. The Al(OH)₃layer maintains a constant thickness of ~5.0 Å, while the inner Al₂O₃layer grows at the Al₂O₃/Al interface to a limiting thickness. On the basis of experimental data and atomistic modeling, we show the tunability of the dissociation pathways of H₂O molecules with the Al, Al₂O₃, and Al(OH)₃surface terminations. The fundamental insights may have practical significance for the design of materials and reactions for two seemingly disparate but fundamentally related disciplines of surface passivation and catalytic H₂production from water. 

Despite the well-known tendency for many alloys to undergo ordering transformations, the microscopic mechanism of ordering and its dependence on alloy composition remains largely unknown. Using the example of Pt 85 Fe 15 and Pt 65 Fe 35 alloy nanoparticles (NPs), herein we demonstrate the composition-dependent ordering processes on the single-particle level, where the nanoscale size effect allows for close interplay between surface and bulk in controlling the phase evolution. Using in situ electron microscopy observations, we show that the ordering transformation in Pt 85 Fe 15 NPs during vacuum annealing occurs via the surface nucleation and growth of L1 2 -ordered Pt 3 Fe domains that propagate into the bulk, followed by the self-sacrifice transformation of the surface region of the L1 2 Pt 3 Fe into a Pt skin. By contrast, the ordering in Pt 65 Fe 35 NPs proceeds via an interface mechanism by which the rapid formation of an L1 0 PtFe skin occurs on the NPs and the transformation boundary moves inward along with outward Pt diffusion. Although both the “nucleation and growth” and the “interface” mechanisms result in a core–shell configuration with a thin Pt-rich skin, Pt 85 Fe 15 NPs have an L1 2 Pt 3 Fe core, whereas Pt 65 Fe 35 NPs are composed of an L1 0 PtFe core. Using atomistic modeling, we identify the composition-dependent vacancy-assisted counterdiffusion of Pt and Fe atoms between the surface and core regions in controlling the ordering transformation pathway. This vacancy-assisted diffusion is further demonstrated by oxygen annealing, for which the selective oxidation of Fe results in a large number of Fe vacancies and thereby greatly accelerates the transformation kinetics. 

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design. 

Abstract A challenge in the synthesis of single‐wall carbon nanotubes (SWCNTs) is the lack of control over the formation and evolution of catalyst nanoparticles and the lack of control over their size or chirality. Here, zeolite MFI nanosheets (MFI‐Ns) are used to keep cobalt (Co) nanoparticles stable during prolonged annealing conditions. Environmental transmission electron microscopy (ETEM) shows that the MFI‐Ns can influence the size and shape of nanoparticles via particle/support registry, which leads to the preferential docking of nanoparticles to four or fewer pores and to the regulation of the SWCNT synthesis products. The resulting SWCNT population exhibits a narrow diameter distribution and SWCNTs of nearly all chiral angles, including sub‐nm zigzag (ZZ) and near‐ZZ tubes. Theoretical simulations reveal that the growth of these unfavorable tubes from unsupported catalysts leads to the rapid encapsulation of catalyst nanoparticles bearing them; their presence in the growth products suggests that the MFI‐Ns prevent nanoparticle encapsulation and prologue ZZ and near‐ZZ SWCNT growth. These results thus present a path forward for controlling nanoparticle formation and evolution, for achieving size‐ and shape‐selectivity at high temperature, and for controlling SWCNT synthesis.