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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. 

Despite the ubiquitous presence of passivation on most metal surfaces, the microscopic‐level picture of how surface passivation occurs has been hitherto unclear. Using the canonical example of the surface passivation of aluminum, here in situ atomistic transmission electron microscopy observations and computational modeling are employed to disentangle entangled microscopic processes and identify the atomic processes leading to the surface passivation. Based on atomic‐scale observations of the layer‐by‐layer expansion of the metal lattice and its subsequent transformation into the amorphous oxide, it is shown that the surface passivation occurs via a two‐stage oxidation process, in which the first stage is dominated by intralayer atomic shuffling whereas the second stage is governed by interlayer atomic disordering upon the progressive oxygen uptake. The first stage can be bypassed by increasing surface defects to promote the interlayer atomic migration that results in direct amorphization of multiple atomic layers of the metal lattice. The identified two‐stage reaction mechanism and the effect of surface defects in promoting interlayer atomic shuffling can find broader applicability in utilizing surface defects to tune the mass transport and passivation kinetics, as well as the composition, structure, and transport properties of the passivation films.

 

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.