FY2010 - Oak Ridge National Laboratory

More documents

Recommendations

Info

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 <strong>National</strong> 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 184
Seed Money Fund— Center for Nanophase Materials Sciences surface, yielding three-dimensional (or higher) spectral images and simplified using principal component analysis. The objects are identified using characteristic shape, for example, yielding appropriate identifiers. The neural net is trained for recognition of the single-pixel response with the principal component analysis (PCA) components as input and the identifier as output. Thus trained neural net is subsequently used for identification of the (unknown) objects in the subsequent imaging, for which shapes or other trivial identifiers are no longer available. This approach has been demonstrated for separation of M. lisodeicticus and P. fluorescense bacteria based on electromechanical response in a liquid environment. The studies have been extended for the rapidly acquired force-distance curves in SPM obtained from dynamic data. Furthermore, its viability has been demonstrated for other spectroscopic imaging modes, including energy loss spectroscopy in scanning transmission electron microscopy. Information Shared Guo, S., O. Ovchinnikov, M. E. Curtis, M. B. Johnson, S. Jesse, and S. V. Kalinin. 2010. “Spatially resolved probing of Preisach density in polycrystalline ferroelectric thin films.” J. Appl. Phys. 108, 084103. Jesse, S., and S. V. Kalinin. 2009. “Principal component and spatial correlation analysis of spectroscopic imaging data in scanning probe microscopy.” Nanotechnology 20, 085714. Jesse, S., S. Guo, A. Kumar, B. J. Rodriguez, R. Proksch, and S. V. Kalinin. 2010. “Resolution theory and static- and frequency-dependent cross-talk in piezoresponse force microscopy.” Nanotechnology 21, 405703. Nikiforov, M. P., A. A. Vertegel, V. V. Reukov, G. L. Thompson, S. V. Kalinin, and S. Jesse. 2009. “Functional recognition imaging using artificial neural networks: Applications to rapid cellular identification by broadband electromechanical response.” Nanotechnology 20, 405708. Nikiforov, M. P., G. L. Thompson, V. V. Reukov, S. Jesse, S. Guo, B. J. Rodriguez, K. Seal, A. A. Vertegel, and S. V. Kalinin. 2010. “Double-layer Mediated Electromechanical Response of Amyloid Fibrils in Liquid Environment.” ACS Nano 4, 689. Ovchinnikov, O., S. Jesse, P. Bintacchit, S. TrolierMcKinstry, and S. V. Kalinin. 2009. “Disorder identification in hysteresis data: recognition analysis of random-bond random-field Ising model.” Phys. Rev. Lett. 103, 157203. Ovchinnikov, O., S. Jesse, S. Guo, K. Seal, P. Bintachitt, I. Fujii, S. Trolier-McKinstry, and S. V. Kalinin. 2010. “Local measurements of Preisach density in polycrystalline ferroelectric capacitors using piezoresponse force microscopy.” Appl. Phys. Lett. 96, 112906. 185
Page 1 and 2:
ORNL/PPA-2011/1 Laboratory Directed
Page 3:
ORNL/PPA-2011/1 Oak Ridge National
Page 6 and 7:
NEUTRON SCIENCES ..................
Page 8 and 9:
NATIONAL SECURITY SCIENCE AND TECHN
Page 10 and 11:
05887 Controlling the Catalytic Pro
Page 13 and 14:
Introduction INTRODUCTION The Labor
Page 15 and 16:
Introduction projects for next-gene
Page 17 and 18:
Introduction ― Develop new mode
Page 19 and 20:
Introduction To select the best and
Page 21 and 22:
Introduction Fig. 2. Distribution o
Page 23:
SUMMARIES OF PROJECTS SUPPORTED THR
Page 26 and 27:
Director’s R&D Fund— Science fo
Page 28 and 29:
Page 30 and 31:
Page 32 and 33:
Page 34 and 35:
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 53 and 54:
Director’s R&D Fund— Neutron Sc
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
05306 Structure and Structure Evolu
Page 67 and 68:
05404 Asynchronous In Situ Neutron
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Director’s R&D Fund— Ultrascale
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Director’s R&D Fund— Systems Bi
Page 107 and 108:
Page 109 and 110:
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
Page 117:
Page 120 and 121:
Director’s R&D Fund— Advanced E
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 135 and 136:
Director’s R&D Fund— Emerging S
Page 137 and 138:
Page 139:
Page 142 and 143:
Director’s R&D Fund— Understand
Page 144 and 145:
Director’s R&D Fund— Understand
Page 146 and 147: Director’s R&D Fund— Understand
Page 153 and 154: Director’s R&D Fund— National S
Page 163: 05573 Rapid Radiochemistry Applicat
Page 166 and 167: Director’s R&D Fund— Energy Sto
Page 176 and 177: Director’s R&D Fund— General Re
Page 178 and 179: Director’s R&D Fund— General de
Page 180 and 181: Director’s R&D Fund— General su
Page 182 and 183: Director’s R&D Fund— General 05
Page 184 and 185: Director’s R&D Fund— General 20
Page 187 and 188: Seed Money Fund— Biosciences Divi
Page 193: Seed Money Fund— Biosciences Divi
Page 199 and 200: Seed Money Fund— Chemical Science
Page 205: Seed Money Fund— Chemical Science
Page 208 and 209: Seed Money Fund— Computational Sc
Page 210 and 211: Seed Money Fund— Computational Sc
Page 213 and 214: Seed Money Fund— Computer Science
Page 215 and 216: Seed Money Fund— Energy and Trans
Page 217 and 218: Seed Money Fund— Energy and Trans
Page 219: Seed Money Fund— Energy and Trans
Page 222 and 223: Seed Money Fund— Environmental Sc
Page 224 and 225: Seed Money Fund— Environmental Sc
Page 227 and 228: Seed Money Fund— Fusion Energy Di
Page 229 and 230: Seed Money Fund— Materials Scienc
Page 241 and 242: Seed Money Fund— Measurement Scie
Page 247 and 248:
05858 Fabrication of Ultrathin Grap
Page 249 and 250:
Seed Money Fund— Measurement Scie
Page 251:
Seed Money Fund— Measurement Scie
Page 254 and 255:
Seed Money Fund— Global Nuclear S
Page 256 and 257:
Seed Money Fund— Neutron Scatteri
Page 259 and 260:
Seed Money Fund— Reactor and Nucl
Page 261 and 262:
Page 263 and 264:
Page 265:
Page 268 and 269:
Seed Money Fund— Physics Division
Page 270 and 271:
Seed Money Fund— Physics Division
Page 272 and 273:
Seed Money Fund— Research Acceler
Page 275 and 276:
Laboratory-Wide Fellowships— Wein
Page 277 and 278:
Page 279 and 280:
Page 281:
Page 284 and 285:
Laboratory-Wide Fellowships— Wign
Page 286 and 287:
Laboratory-Wide Fellowships— Wign
Page 288 and 289:
Index of Project Contributors Coope
Page 290 and 291:
Index of Project Contributors Mille
Page 292 and 293:
Index of Project Contributors Yang,
Page 294:
Index of Project Numbers 05501 ....
show all

FY2010 - Oak Ridge National Laboratory

Create successful ePaper yourself

Delete template?

Save as template?