- Molecular-Scale Computational Techniques in Interfacial Science

Author(s):  
J. T. Woodward ◽  
J. A. N. Zasadzinski

The Scanning Tunneling Microscope (STM) offers exciting new ways of imaging surfaces of biological or organic materials with resolution to the sub-molecular scale. Rigid, conductive surfaces can readily be imaged with the STM with atomic resolution. Unfortunately, organic surfaces are neither sufficiently conductive or rigid enough to be examined directly with the STM. At present, nonconductive surfaces can be examined in two ways: 1) Using the AFM, which measures the deflection of a weak spring as it is dragged across the surface, or 2) coating or replicating non-conductive surfaces with metal layers so as to make them conductive, then imaging with the STM. However, we have found that the conventional freeze-fracture technique, while extremely useful for imaging bulk organic materials with STM, must be modified considerably for optimal use in the STM.


2003 ◽  
Vol 771 ◽  
Author(s):  
Adosh Mehta ◽  
Pradeep Kumar ◽  
Jie Zheng ◽  
Robert M. Dickson ◽  
Bobby Sumpter ◽  
...  

AbstractDipole emission pattern imaging experiments on single chains of common conjugated polymers (solubilized poly phenylene vinylenes) isolated by ink-jet printing techniques have revealed surprising uniformity in transition moment orientation perpendicular to the support substrate. In addition to uniform orientation, these species show a number of striking differences in photochemical stability, polarization anisotropy,[1] and spectral signatures[2] with respect to similar (well-studied) molecules dispersed in dilute thin-films. Combined with molecular mechanics simulation, these results point to a structural picture of a folded macromolecule as a highly ordered cylindrical nanostructure whose long-axis (approximately collinear with the conjugation axis) is oriented, by an electrostatic interaction, perpendicular to the coverglass substrate. These results suggest a number of important applications in nanoscale photonics and molecular-scale optoelectronics.


Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.


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