The mesoscale order of nacreous pearls.

A pearl's distinguished beauty and toughness are attributable to the periodic stacking of aragonite tablets known as nacre. Nacre has naturally occurring mesoscale periodicity that remarkably arises in the absence of discrete translational symmetry. Gleaning the inspiring biomineral design of a pearl requires quantifying its structural coherence and understanding the stochastic processes that influence formation. By characterizing the entire structure of pearls (∼3 mm) in a cross-section at high resolution, we show that nacre has medium-range mesoscale periodicity. Self-correcting growth mechanisms actively remedy disorder and topological defects of the tablets and act as a countervailing process to long-range disorder. Nacre has a correlation length of roughly 16 tablets (∼5.5 µm) despite persistent fluctuations and topological defects. For longer distances (>25 tablets , ∼8.5 µm), the frequency spectrum of nacre tablets follows [Formula: see text] behavior, suggesting that growth is coupled to external stochastic processes-a universality found across disparate natural phenomena, which now includes pearls.

Multiscale hierarchical structures from a nanocluster mesophase.

Spontaneous hierarchical self-organization of nanometre-scale subunits into higher-level complex structures is ubiquitous in nature. The creation of synthetic nanomaterials that mimic the self-organization of complex superstructures commonly seen in biomolecules has proved challenging due to the lack of biomolecule-like building blocks that feature versatile, programmable interactions to render structural complexity. In this study, highly aligned structures are obtained from an organic-inorganic mesophase composed of monodisperse Cd37S18 magic-size cluster building blocks. Impressively, structural alignment spans over six orders of magnitude in length scale: nanoscale magic-size clusters arrange into a hexagonal geometry organized inside micrometre-sized filaments; self-assembly of these filaments leads to fibres that then organize into uniform arrays of centimetre-scale bands with well-defined surface periodicity. Enhanced patterning can be achieved by controlling processing conditions, resulting in bullseye and 'zigzag' stacking patterns with periodicity in two directions. Overall, we demonstrate that colloidal nanomaterials can exhibit a high level of self-organization behaviour at macroscopic-length scales.

Correlative image learning of chemo-mechanics in phase-transforming solids.

Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (for example, due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. Here we developed a generalizable, physically constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on LiXFePO4, a technologically relevant battery positive electrode material. We uncovered the functional form of the composition-eigenstrain relation in this two-phase binary solid across the entire composition range (0 ≤ X ≤ 1), including inside the thermodynamically unstable miscibility gap. The learned relation directly validates Vegard's law of linear response at the nanoscale. Our physics-constrained data-driven approach directly visualizes the residual strain field (by removing the compositional and coherency strain), which is otherwise impossible to quantify. Heterogeneities in the residual strain arise from misfit dislocations and were independently verified by X-ray diffraction line profile analysis. Our work provides the means to simultaneously quantify chemical expansion, coherency strain and dislocations in battery electrodes, which has implications on rate capabilities and lifetime. Broadly, this work also highlights the potential of integrating correlative microscopy and image learning for extracting material properties and physics.

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching.

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

Nature and evolution of incommensurate charge order in manganites visualized with cryogenic scanning transmission electron microscopy.

Incommensurate charge order in hole-doped oxides is intertwined with exotic phenomena such as colossal magnetoresistance, high-temperature superconductivity, and electronic nematicity. Here, we map, at atomic resolution, the nature of incommensurate charge-lattice order in a manganite using scanning transmission electron microscopy at room temperature and cryogenic temperature ([Formula: see text]93 K). In diffraction, the ordering wave vector changes upon cooling, a behavior typically associated with incommensurate order. However, using real space measurements, we discover that the ordered state forms lattice-locked regions over a few wavelengths interspersed with phase defects and changing periodicity. The cations undergo picometer-scale ([Formula: see text]6 pm to 11 pm) transverse displacements, suggesting that charge-lattice coupling is strong. We further unearth phase inhomogeneity in the periodic lattice displacements at room temperature, and emergent phase coherence at 93 K. Such local phase variations govern the long-range correlations of the charge-ordered state and locally change the periodicity of the modulations, resulting in wave vector shifts in reciprocal space. These atomically resolved observations underscore the importance of lattice coupling and phase inhomogeneity, and provide a microscopic explanation for putative "incommensurate" order in hole-doped oxides.

A Fast Algorithm for Scanning Transmission Electron Microscopy Imaging and 4D-STEM Diffraction Simulations.

Scanning transmission electron microscopy (STEM) is an extremely versatile method for studying materials on the atomic scale. Many STEM experiments are supported or validated with electron scattering simulations. However, using the conventional multislice algorithm to perform these simulations can require extremely large calculation times, particularly for experiments with millions of probe positions as each probe position must be simulated independently. Recently, the plane-wave reciprocal-space interpolated scattering matrix (PRISM) algorithm was developed to reduce calculation times for large STEM simulations. Here, we introduce a new method for STEM simulation: partitioning of the STEM probe into "beamlets," given by a natural neighbor interpolation of the parent beams. This idea is compatible with PRISM simulations and can lead to even larger improvements in simulation time, as well requiring significantly less computer random access memory (RAM). We have performed various simulations to demonstrate the advantages and disadvantages of partitioned PRISM STEM simulations. We find that this new algorithm is particularly useful for 4D-STEM simulations of large fields of view. We also provide a reference implementation of the multislice, PRISM, and partitioned PRISM algorithms.

Multibeam Electron Diffraction.

One of the primary uses for transmission electron microscopy (TEM) is to measure diffraction pattern images in order to determine a crystal structure and orientation. In nanobeam electron diffraction (NBED), we scan a moderately converged electron probe over the sample to acquire thousands or even millions of sequential diffraction images, a technique that is especially appropriate for polycrystalline samples. However, due to the large Ewald sphere of TEM, excitation of Bragg peaks can be extremely sensitive to sample tilt, varying strongly for even a few degrees of sample tilt for crystalline samples. In this paper, we present multibeam electron diffraction (MBED), where multiple probe-forming apertures are used to create multiple scanning transmission electron microscopy (STEM) probes, all of which interact with the sample simultaneously. We detail designs for MBED experiments, and a method for using a focused ion beam to produce MBED apertures. We show the efficacy of the MBED technique for crystalline orientation mapping using both simulations and proof-of-principle experiments. We also show how the angular information in MBED can be used to perform 3D tomographic reconstruction of samples without needing to tilt or scan the sample multiple times. Finally, we also discuss future opportunities for the MBED method.

py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis.

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

Explaining the Unusual Photoluminescence of Semiconductor Nanocrystals Doped via Cation Exchange.

Aliovalent doping of CdSe nanocrystals (NCs) via cation exchange processes has resulted in interesting and novel observations for the optical and electronic properties of the NCs. However, despite over a decade of study, these observations have largely gone unexplained, partially due to an inability to precisely characterize the physical properties of the doped NCs. Here, electrostatic force microscopy was used to determine the static charge on individual, cation-doped CdSe NCs in order to investigate their net charge as a function of added cations. While the NC charge was relatively insensitive to the relative amount of doped cation per NC, there was a remarkable and unexpected correlation between the average NC charge and PL intensity, for all dopant cations introduced. We conclude that the changes in PL intensity, as tracked also by changes in NC charge, are likely a consequence of changes in the NC radiative rate caused by symmetry breaking of the electronic states of the nominally spherical NC due to the Coulombic potential introduced by ionized cations.

Connectivity of centermost chromatophores in Rhodobacter sphaeroides bacteria.

The size of whole Rhodobacter sphaeroides prevents 3D visualization of centermost chromatophores in their native environment. This study combines cryo-focused ion beam milling with cryo-electron tomography to probe vesicle architecture both in situ and in 3D. Developing chromatophores are membrane-bound buds that remain in topological continuity with the cytoplasmic membrane and detach into vesicles when mature. Mature chromatophores closest to the cell wall are typically isolated vesicles, whereas centermost chromatophores are either linked to neighboring chromatophores or contain smaller, budding structures. Isolated chromatophores comprised a minority of centermost chromatophores. Connections between vesicles in growing bacteria are through ~10 nm-long, ~5 nm-wide linkers, and are thus physical rather than functional in terms of converting photons to ATP. In cells in the stationary phase, chromatophores fuse with neighboring vesicles, lose their spherical structure, and greatly increase in volume. The fusion and morphological changes seen in older bacteria are likely a consequence of the aging process, and are not representative of connectivity in healthy R. sphaeroides. Our results suggest that chromatophores can adopt either isolated or connected morphologies within a single bacterium. Revealing the organization of chromatophore vesicles throughout the cell is an important step in understanding the photosynthetic mechanisms in R. sphaeroides.

Atomic lattice disorder in charge-density-wave phases of exfoliated dichalcogenides (1T-TaS2).

Charge-density waves (CDWs) and their concomitant periodic lattice distortions (PLDs) govern the electronic properties in several layered transition-metal dichalcogenides. In particular, 1T-TaS2 undergoes a metal-to-insulator phase transition as the PLD becomes commensurate with the crystal lattice. Here we directly image PLDs of the nearly commensurate (NC) and commensurate (C) phases in thin, exfoliated 1T-TaS2 using atomic resolution scanning transmission electron microscopy at room and cryogenic temperature. At low temperatures, we observe commensurate PLD superstructures, suggesting ordering of the CDWs both in- and out-of-plane. In addition, we discover stacking transitions in the atomic lattice that occur via one-bond-length shifts. Interestingly, the NC PLDs exist inside both the stacking domains and their boundaries. Transitions in stacking order are expected to create fractional shifts in the CDW between layers and may be another route to manipulate electronic phases in layered dichalcogenides.

Mesophase Formation Stabilizes High-Purity Magic-Sized Clusters.

Magic-sized clusters (MSCs) are renowned for their identical size and closed-shell stability that inhibit conventional nanoparticle (NP) growth processes. Though MSCs have been of increasing interest, understanding the reaction pathways toward their nucleation and stabilization is an outstanding issue. In this work, we demonstrate that high concentration synthesis (1000 mM) promotes a well-defined reaction pathway to form high-purity MSCs (>99.9%). The MSCs are resistant to typical growth and dissolution processes. On the basis of insights from in situ X-ray scattering analysis, we attribute this stability to the accompanying production of a large (>100 nm grain size), hexagonal organic-inorganic mesophase that arrests growth of the MSCs and prevents NP growth. At intermediate concentrations (500 mM), the MSC mesophase forms, but is unstable, resulting in NP growth at the expense of the assemblies. These results provide an alternate explanation for the high stability of MSCs. Whereas the conventional mantra has been that the stability of MSCs derives from the precise arrangement of the inorganic structures (i.e., closed-shell atomic packing), we demonstrate that anisotropic clusters can also be stabilized by self-forming fibrous mesophase assemblies. At lower concentration (<200 mM or >16 acid-to-metal), MSCs are further destabilized and NPs formation dominates that of MSCs. Overall, the high concentration approach intensifies and showcases inherent concentration-dependent surfactant phase behavior that is not accessible in conventional (i.e., dilute) conditions. This work provides not only a robust method to synthesize, stabilize, and study identical MSC products but also uncovers an underappreciated stabilizing interaction between surfactants and clusters.

Charge transport and localization in atomically coherent quantum dot solids.

Epitaxial attachment of quantum dots into ordered superlattices enables the synthesis of quasi-two-dimensional materials that theoretically exhibit features such as Dirac cones and topological states, and have major potential for unprecedented optoelectronic devices. Initial studies found that disorder in these structures causes localization of electrons within a few lattice constants, and highlight the critical need for precise structural characterization and systematic assessment of the effects of disorder on transport. Here we fabricated superlattices with the quantum dots registered to within a single atomic bond length (limited by the polydispersity of the quantum dot building blocks), but missing a fraction (20%) of the epitaxial connections. Calculations of the electronic structure including the measured disorder account for the electron localization inferred from transport measurements. The calculations also show that improvement of the epitaxial connections will lead to completely delocalized electrons and may enable the observation of the remarkable properties predicted for these materials.