Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones.

Over the last months, cases of SARS-CoV-2 surged repeatedly in many countries but could often be controlled with nonpharmaceutical interventions including social distancing. We analyzed deidentified Global Positioning System (GPS) tracking data from 1.15 to 1.4 million cell phones in Germany per day between March and November 2020 to identify encounters between individuals and statistically evaluate contact behavior. Using graph sampling theory, we estimated the contact index (CX), a metric for number and heterogeneity of contacts. We found that CX, and not the total number of contacts, is an accurate predictor for the effective reproduction number R derived from case numbers. A high correlation between CX and R recorded more than 2 wk later allows assessment of social behavior well before changes in case numbers become detectable. By construction, the CX quantifies the role of superspreading and permits assigning risks to specific contact behavior. We provide a critical CX value beyond which R is expected to rise above 1 and propose to use that value to leverage the social-distancing interventions for the coming months.

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.

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.

Direct Observation of Amorphous Precursor Phases in the Nucleation of Protein-Metal-Organic Frameworks.

Protein-metal-organic frameworks (p-MOFs) are a prototypical example of how synthetic biological hybrid systems can be used to develop next-generation materials. Controlling p-MOF formation enables the design of hybrid materials with enhanced biological activity and high stability. However, such control is yet to be fully realized due to an insufficient understanding of the governing nucleation and growth mechanisms in p-MOF systems. The structural evolution of p-MOFs was probed by cryo-transmission electron microscopy, revealing nonclassical pathways via dissolution-recrystallization of highly hydrated amorphous particles and solid-state transformation of a protein-rich amorphous phase. On the basis of these data, we propose a general description of p-MOF crystallization which is best characterized by particle aggregation and colloidal theory for future synthetic strategies.

Nanometer Resolution Elemental Mapping in Graphene-Based TEM Liquid Cells.

We demonstrate a new design of graphene liquid cell consisting of a thin lithographically patterned hexagonal boron nitride crystal encapsulated on both sides with graphene windows. The ultrathin window liquid cells produced have precisely controlled volumes and thicknesses and are robust to repeated vacuum cycling. This technology enables exciting new opportunities for liquid cell studies, providing a reliable platform for high resolution transmission electron microscope imaging and spectral mapping. The presence of water was confirmed using electron energy loss spectroscopy (EELS) via the detection of the oxygen K-edge and measuring the thickness of full and empty cells. We demonstrate the imaging capabilities of these liquid cells by tracking the dynamic motion and interactions of small metal nanoparticles with diameters of 0.5-5 nm. We further present an order of magnitude improvement in the analytical capabilities compared to previous liquid cell data with 1 nm spatial resolution elemental mapping achievable for liquid encapsulated bimetallic nanoparticles using energy dispersive X-ray spectroscopy (EDXS).

Observing the Dynamics of an Electrochemically Driven Active Material with Liquid Electron Microscopy.

Electrochemical liquid electron microscopy has revolutionized our understanding of nanomaterial dynamics by allowing for direct observation of their electrochemical production. This technique, primarily applied to inorganic materials, is now being used to explore the self-assembly dynamics of active molecular materials. Our study examines these dynamics across various scales, from the nanoscale behavior of individual fibers to the micrometer-scale hierarchical evolution of fiber clusters. To isolate the influences of the electron beam and electrical potential on material behavior, we conducted thorough beam-sample interaction analyses. Our findings reveal that the dynamics of these active materials at the nanoscale are shaped by their proximity to the electrode and the applied electrical current. By integrating electron microscopy observations with reaction-diffusion simulations, we uncover that local structures and their formation history play a crucial role in determining assembly rates. This suggests that the emergence of nonequilibrium structures can locally accelerate further structural development, offering insights into the behavior of active materials under electrochemical conditions.

Hydrocarbon contamination in angström-scale channels.

Nonspecific molecular adsorption such as airborne contamination occurs on most surfaces including those of 2D materials and alters their properties. While surface contamination is studied using a plethora of techniques, the effect of contamination on confined systems such as nanochannels/pores leading to their clogging is still lacking. We report a systematic investigation of hydrocarbon adsorption in angstrom (Å) slit channels of varying heights. Hexane is chosen to mimic the hydrocarbon contamination and the clogging of the Å-channels is evaluated via a helium gas flow measurement. The level of hexane adsorption, in other words, the degree of clogging depends on the size difference between the channels and hexane. A dynamic transition of the clogging and revival process is shown in sub-2 nm thin channels. Long-term storage and stability of our Å-channels are demonstrated here for up to three years, alleviating the contamination and unclogging the channels using thermal treatment. This study highlights the importance of the nanochannels' stability and demonstrates the self-cleansing nature of sub-2 nm thin channels enabling a robust platform for molecular transport and separation studies. We provide a method to assess the cleanliness of nanoporous membranes, which is vital for the practical applications of nanofluidics in various fields such as molecular sensing, separation and power generation.

Prismatic 2.0 - Simulation software for scanning and high resolution transmission electron microscopy (STEM and HRTEM).

Scanning transmission electron microscopy (STEM), where a converged electron probe is scanned over a sample's surface and an imaging, diffraction, or spectroscopic signal is measured as a function of probe position, is an extremely powerful tool for materials characterization. The widespread adoption of hardware aberration correction, direct electron detectors, and computational imaging methods have made STEM one of the most important tools for atomic-resolution materials science. Many of these imaging methods rely on accurate imaging and diffraction simulations in order to interpret experimental results. However, STEM simulations have traditionally required large calculation times, as modeling the electron scattering requires a separate simulation for each of the typically millions of probe positions. We have created the Prismatic simulation code for fast simulation of STEM experiments with support for multi-CPU and multi-GPU (graphics processing unit) systems, using both the conventional multislice and our recently-introduced PRISM method. In this paper, we introduce Prismatic version 2.0, which adds many new algorithmic improvements, an updated graphical user interface (GUI), post-processing of simulation data, and additional operating modes such as plane-wave TEM. We review various aspects of the simulation methods and codes in detail and provide various simulation examples. Prismatic 2.0 is freely available both as an open-source package that can be run using a C++ or Python command line interface, or GUI, as well within a Docker container environment.

Ring-opening polymerization-induced crystallization-driven self-assembly of poly-L-lactide-block-polyethylene glycol block copolymers (ROPI-CDSA).

The self-assembly of block copolymers into 1D, 2D and 3D nano- and microstructures is of great interest for a wide range of applications. A key challenge in this field is obtaining independent control over molecular structure and hierarchical structure in all dimensions using scalable one-pot chemistry. Here we report on the ring opening polymerization-induced crystallization-driven self-assembly (ROPI-CDSA) of poly-L-lactide-block-polyethylene glycol block copolymers into 1D, 2D and 3D nanostructures. A key feature of ROPI-CDSA is that the polymerization time is much shorter than the self-assembly relaxation time, resulting in a non-equilibrium self-assembly process. The self-assembly mechanism is analyzed by cryo-transmission electron microscopy, wide-angle x-ray scattering, Fourier transform infrared spectroscopy, and turbidity studies. The analysis revealed that the self-assembly mechanism is dependent on both the polymer molecular structure and concentration. Knowledge of the self-assembly mechanism enabled the kinetic trapping of multiple hierarchical structures from a single block copolymer.

ChronoMID-Cross-modal neural networks for 3-D temporal medical imaging data.

ChronoMID-neural networks for temporally-varying, hence Chrono, Medical Imaging Data-makes the novel application of cross-modal convolutional neural networks (X-CNNs) to the medical domain. In this paper, we present multiple approaches for incorporating temporal information into X-CNNs and compare their performance in a case study on the classification of abnormal bone remodelling in mice. Previous work developing medical models has predominantly focused on either spatial or temporal aspects, but rarely both. Our models seek to unify these complementary sources of information and derive insights in a bottom-up, data-driven approach. As with many medical datasets, the case study herein exhibits deep rather than wide data; we apply various techniques, including extensive regularisation, to account for this. After training on a balanced set of approximately 70000 images, two of the models-those using difference maps from known reference points-outperformed a state-of-the-art convolutional neural network baseline by over 30pp (> 99% vs. 68.26%) on an unseen, balanced validation set comprising around 20000 images. These models are expected to perform well with sparse data sets based on both previous findings with X-CNNs and the representations of time used, which permit arbitrarily large and irregular gaps between data points. Our results highlight the importance of identifying a suitable description of time for a problem domain, as unsuitable descriptors may not only fail to improve a model, they may in fact confound it.

Synthetic 2-D lead tin sulfide nanosheets with tuneable optoelectronic properties from a potentially scalable reaction pathway.

Solventless thermolysis of molecular precursors followed by liquid phase exfoliation allows access to two-dimensional IV-VI semiconductor nanomaterials hitherto unreachable by a scalable processing pathway. Firstly, the use of metal dithiocarbamate precursors to produce bulk alloys in the series Pb1-x Sn x S (0 ≤ x ≤ 1) by thermolysis is demonstrated. The bulk powders are characterised by powder X-ray diffraction (pXRD), Raman spectroscopy, scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) spectroscopy. It was found that there is a transition from cubic structures for the Pb-rich alloys including the end compound, PbS (0 ≤ x ≤ 0.4) to layered orthorhombic structures for Sn-rich alloys and the end compound SnS (0.5 ≤ x ≤ 1.0). A smooth elemental progression from lead-rich to tin-rich monochalcogenides across the series of materials is observed. Liquid phase exfoliation was applied to produce two dimensional (2D) nanosheets for a mixed Pb1-x Sn x S alloy (where x = 0.8) in 1-methyl-2-pyrrolidone (NMP) using the synthetic bulk powder as starting material. The nanosheet products were characterized by SEM, atomic force microscopy (AFM) and high angle annular dark field scanning transmission electron microscopy (HAADF STEM). First principle calculations of Pb1-x Sn x S alloys show that the Sn content x modifies the size of the band gap by several 100 meV and that x changes the gap type from indirect in SnS to direct in Pb0.2Sn0.8S. These results are supported by UV-Vis spectroscopy of exfoliated Pb0.2Sn0.8S. The method employed demonstrates a new, scalable, processing pathway which can potentially be used to synthesize a range of synthetic layered structures that can be exfoliated to as-yet unaccessed 2D materials with tunable electronic properties.

Correction: Synthetic 2-D lead tin sulfide nanosheets with tuneable optoelectronic properties from a potentially scalable reaction pathway.

[This corrects the article DOI: 10.1039/C8SC04018D.].

Exfoliation of natural van der Waals heterostructures to a single unit cell thickness.

Weak interlayer interactions in van der Waals crystals facilitate their mechanical exfoliation to monolayer and few-layer two-dimensional materials, which often exhibit striking physical phenomena absent in their bulk form. Here we utilize mechanical exfoliation to produce a two-dimensional form of a mineral franckeite and show that the phase segregation of chemical species into discrete layers at the sub-nanometre scale facilitates franckeite's layered structure and basal cleavage down to a single unit cell thickness. This behaviour is likely to be common in a wider family of complex minerals and could be exploited for a single-step synthesis of van der Waals heterostructures, as an alternative to artificial stacking of individual two-dimensional crystals. We demonstrate p-type electrical conductivity and remarkable electrochemical properties of the exfoliated crystals, showing promise for a range of applications, and use the density functional theory calculations of franckeite's electronic band structure to rationalize the experimental results.

Asymmetric MoS2 /Graphene/Metal Sandwiches: Preparation, Characterization, and Application.

The polarizable organic/water interface is used to construct MoS2 /graphene nanocomposites, and various asymmetrically dual-decorated graphene sandwiches are synthesized. High-resolution transmission electron microscopy and 3D electron tomography confirm their structure. These dual-decorated graphene-based hybrids show excellent hydrogen evolution activity and promising capacitance performance.