FY2010 - Oak Ridge National Laboratory

Seed Money Fund—
Center for Nanophase Materials Sciences
We have devised a Monte Carlo method to simulate the segregation of the phase domains by considering
several factors. The first is the thermodynamic energy of the coexisting phases, which depends on the
temperature and the applied stress and is also a function of the local chemical composition. The second
factor is the energy of the phase boundaries, which is expressed as the square of the gradient of the phase
field variable. The strain energy is nonlocal whose range is the size of the phase domains; therefore, it is
difficult to include in an efficient simulation. We approximate the strain energy by the surface-to-volume
ratio of the phase domains. Because the presence of the domain boundaries releases the strain, the higher
the surface-to-volume ratio, the lower the strain energy is. Adding these terms together, our Monte Carlo
simulations show segregations of several different types of phase domains, which will be compared with
experimental observations.
05889
Rapid Functional Recognition Imaging in Scanning Probe Microscopy
Sergei V. Kalinin and Stephen Jesse
Project Description
We propose a scanning probe microscopy (SPM) data acquisition, processing, and control method for
rapid quantitative mapping of local properties and functionality in inorganic, molecular, polymer, and
biological systems. The method, further referred to as functional recognition imaging, is based on the
rapid acquisition and automatic de-noising, classification, and interpretation of spectral, multimodal, or
multispectral data sets (multidimensional data) at each spatial pixel. This recognition step substitutes for
classical homodyne-based data processing or simple postprocessing of multidimensional data.
Recognition data can be stored as an image, used as a feedback signal, or used as a trigger to control more
complex microscope operations such as manipulation or communication. When successful, the project
will open a direct pathway for rapid recognition imaging in all areas of nanoscience by providing a bridge
between advanced computational and modeling capabilities and SPM data.
Mission Relevance
The proposed paradigm for functional imaging potentially allows revolutionizing the landscape of
scanning probe microscopy by providing a reliable bridge between advanced modeling capabilities and
experimental data that can be incorporated during in-line microscope operation. The recent DOE Grand
Challenges and DOE workshop documents list the capability to probe and manipulate matter and
information on the nanoscale as one of the key targets for DOE research, suggesting high relevance for
energy-related fundamental research. The specific topic in this work—energy losses during mechanical
tip-surface contact—are highly relevant to fundamental aspects of mechanical behavior and friction in
spatially confined systems. Furthermore, methods for biological imaging and recognition are remaining a
traditional priority for the National Institutes of Health. Recognition imaging microscopy offers an ideal
pathway for this funding by providing a method to differentiate cells based on their phenotype. Hence,
cancer and molecular imaging programs are a natural source of funding. Notably, the proposed algorithm
can be potentially used in conjunction with other detected signals, including optical, mass-spectral, or
microwave.
Results and Accomplishments
We have demonstrated the recognition spectroscopic imaging for rapid identification of biological,
molecular, and atomic species in the SPM experiments. In this, the spectroscopic response (e.g., forcedistance
curve or broadband excitation response) is acquired on a spatially resolved grid on the sample
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