Automated Interpretation and Extraction of Topographic Information from Time of Flight Secondary Ion Mass Spectrometry Data.

Time of flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface-sensitive characterization tool allowing the imaging of chemical properties over a wide range of organic and inorganic material systems. This technique allows precise studies of chemical composition with sub-100-nm lateral and nanometer depth spatial resolution. However, comprehensive interpretation of ToF-SIMS results is challenging because of the very large data volume and high dimensionality. Furthermore, investigation of samples with pronounced topographical features is complicated by systematic and measureable shifts in the mass spectrum. In this work we developed an approach for the interpretation of the ToF-SIMS data, based on the advanced data analytics. Along with characterization of the chemical composition, our approach allows extraction of the sample surface morphology from a time of flight registration technique. This approach allows one to perform correlated investigations of surface morphology, biological function, and chemical composition of Arabidopsis roots.

Atomic intercalation to measure adhesion of graphene on graphite.

The interest in mechanical properties of two-dimensional materials has emerged in light of new device concepts taking advantage of flexing, adhesion and friction. Here we demonstrate an effective method to measure adhesion of graphene atop highly ordered pyrolytic graphite, utilizing atomic-scale 'blisters' created in the top layer by neon atom intercalates. Detailed analysis of scanning tunnelling microscopy images is used to reconstruct atomic positions and the strain map within the deformed graphene layer, and demonstrate the tip-induced subsurface translation of neon atoms. We invoke an analytical model, originally devised for graphene macroscopic deformations, to determine the graphite adhesion energy of 0.221±0.011 J m-2. This value is in excellent agreement with reported macroscopic values and our atomistic simulations. This implies mechanical properties of graphene scale down to a few-nanometre length. The simplicity of our method provides a unique opportunity to investigate the local variability of nanomechanical properties in layered materials.

Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures.

Development of new generation electronic devices necessitates understanding and controlling the electronic transport in ferroic, magnetic, and optical materials, which is hampered by two factors. First, the complications of working at the nanoscale, where interfaces, grain boundaries, defects, and so forth, dictate the macroscopic characteristics. Second, the convolution of the response signals stemming from the fact that several physical processes may be activated simultaneously. Here, we present a method of solving these challenges via a combination of atomic force microscopy and data mining analysis techniques. Rational selection of the latter allows application of physical constraints and enables direct interpretation of the statistically significant behaviors in the framework of the chosen physical model, thus distilling physical meaning out of raw data. We demonstrate our approach with an example of deconvolution of complex transport behavior in a bismuth ferrite-cobalt ferrite nanocomposite in ambient and ultrahigh vacuum environments. Measured signal is apportioned into four electronic transport patterns, showing different dependence on partial oxygen and water vapor pressure. These patterns are described in terms of Ohmic conductance and Schottky emission models in the light of surface electrochemistry. Furthermore, deep data analysis allows extraction of local dopant concentrations and barrier heights empowering our understanding of the underlying dynamic mechanisms of resistive switching.

Complete information acquisition in dynamic force microscopy.

Scanning probe microscopy has emerged as a primary tool for exploring and controlling the nanoworld. A critical part of scanning probe measurements is the information transfer from the tip-surface junction to the measurement system. This process reduces responses at multiple degrees of freedom of the probe to relatively few parameters recorded as images. Similarly, details of dynamic cantilever response at sub-microsecond time scales, higher-order eigenmodes and harmonics are lost by transitioning to the millisecond time scale of pixel acquisition. Hence, information accessible to the operator is severely limited, and its selection is biased by data processing methods. Here we report a fundamentally new approach for dynamic Atomic Force Microscopy imaging based on information-theory analysis of the data stream from the detector. This approach allows full exploration of complex tip-surface interactions, spatial mapping of multidimensional variability of material's properties and their mutual interactions, and imaging at the information channel capacity limit.

Better Catalysts through Microscopy: Mesoscale M1/M2 Intergrowth in Molybdenum-Vanadium Based Complex Oxide Catalysts for Propane Ammoxidation.

In recent decades, catalysis research has transformed from the predominantly empirical field to one where it is possible to control the catalytic properties via characterization and modification of the atomic-scale active centers. Many phenomena in catalysis, such as synergistic effect, however, transcend the atomic scale and also require the knowledge and control of the mesoscale structure of the specimen to harness. In this paper, we use our discovery of atomic-scale epitaxial interfaces in molybdenum-vanadium based complex oxide catalysts systems (i.e., Mo-V-M-O, M = Ta, Te, Sb, Nb, etc.) to achieve control of the mesoscale structure of this complex mixture of very different active phases. We can now achieve true epitaxial intergrowth between the catalytically critical M1 and M2 phases in the system that are hypothesized to have synergistic interactions, and demonstrate that the resulting catalyst has improved selectivity in the initial studies. Finally, we highlight the crucial role atomic scale characterization and mesoscale structure control play in uncovering the complex underpinnings of the synergistic effect in catalysis.

Deep data analysis of conductive phenomena on complex oxide interfaces: physics from data mining.

Spatial variability of electronic transport in BiFeO3-CoFe2O4 (BFO-CFO) self-assembled heterostructures is explored using spatially resolved first-order reversal curve (FORC) current voltage (IV) mapping. Multivariate statistical analysis of FORC-IV data classifies statistically significant behaviors and maps characteristic responses spatially. In particular, regions of grain, matrix, and grain boundary responses are clearly identified. k-Means and Bayesian demixing analysis suggest the characteristic response be separated into four components, with hysteretic-type behavior localized at the BFO-CFO tubular interfaces. The conditions under which Bayesian components allow direct physical interpretation are explored, and transport mechanisms at the grain boundaries and individual phases are analyzed. This approach conjoins multivariate statistical analysis with physics-based interpretation, actualizing a robust, universal, data-driven approach to problem solving, which can be applied to exploration of local transport and other functional phenomena in other spatially inhomogeneous systems.

Local crystallography analysis for atomically resolved scanning tunneling microscopy images.

Scanning probe microscopy has emerged as a powerful and flexible tool for atomically resolved imaging of surface structures. However, due to the amount of information extracted, in many cases the interpretation of such data is limited to being qualitative and semi-quantitative in nature. At the same time, much can be learned from local atom parameters, such as distances and angles, that can be analyzed and interpreted as variations of local chemical bonding, or order parameter fields. Here, we demonstrate an iterative algorithm for indexing and determining atomic positions that allows the analysis of inhomogeneous surfaces. This approach is further illustrated by local crystallographic analysis of several real surfaces, including highly ordered pyrolytic graphite and an Fe-based superconductor FeTe0.55Se0.45. This study provides a new pathway to extract and quantify local properties for scanning probe microscopy images.