George Stell - US grants

George Stell is supported by a grant from the Theoretical and Computational Chemistry Program to continue his research in the statistical mechanics of liquids. The research is directed at trying to understand complex fluid properties in terms of the long and short range interactions between idealized particles. Variations of the Ornstein-Zernike integral equation method are used which are validated by Monte Carlo simulations. Special emphasis is placed on understanding ionic and dipolar fluids as well as fluids that are chemically associating. The role of ionic association due to strong electrostatic interactions is being studied with special focus on the effect that ion/ion pair and ion-pair/ion-pair interactions have on the thermodynamics of phase separation and on fluid structure near the critical point. This research provides an important molecular level understanding of the detailed interaction of molecular species in condensed phases. This level of understanding is important in the field of chemistry since the large majority of chemical reactions take place either in aqueous solution or in the liquid phase. A more thorough understanding of the thermodynamic and transport properties of condensed phase systems, particularly ionic and polar fluids, will aid in the design and engineering of efficient chemical engineering processes.

NIRT: Molecular Assembly for Hybrid Electronics
Abstract

This proposal was received in response to Nanoscale Science and Engineering initiative, NSF 02-148, category NIRT. The extension of integrated circuits into sub-10-nm range promises enormous benefits for computing, networking, and signal processing. However, fabrication of such devices using current paradigms based on CMOS and current VLSI technology are not possible. We believe that this crisis may only be resolved by a radical paradigm shift, which would simultaneously change the approach to fabrication of electron devices and to VLSI circuit architecture. Our approach is to use a biologically inspired approach called "Self-Evolving Neuromorphic Networks". This approach is based on artificial models of the neocortex and is structured to have a high degree of parallelism and intrinsic redundancy. In this approach molecular circuit elements, "self-assembled" by molecular chemistry, can be allowed to grow randomly, forming circuit elements (molecular transistors), which connect lithographically patterned metal grids. However, the random aspect of molecular self-assembly has to be carefully understood and controlled. At present, there is no detailed understanding of this process. It is this crucial gap that we address in this proposal. The devices that we are proposing need molecular wires that can switch into and out of a conductive state. The molecules bridge the metal wires with inherent randomness. Our aims are to predict and control the bridging and switching, through deterministic chemistry of the molecule-metal interaction, as well as through a statistical analysis of the assembly process. To accomplish these aims, we have a diverse team, which will interact strongly across the engineering/chemistry/physics boundaries. As part of the outreach of this project we plan to use the NIRT as a forum in which we will provide new types of educational settings for students (undergraduate and graduate) and high school teachers, and adopt a flexible program of research guided by feedback between theory and experiment, chemistry and physics and engineering.

Tremendous technological advances in miniaturization have enabled more and more transistors to be packed onto a silicon chip. However, the reduction of feature size on chips is limited not just by the resolution of the fabrication process, but also by the problem of quantum and classical fluctuations. Consequently, below a limiting dimension that we have nearly reached, a new paradigm that goes beyond conventional solid state electronics has to be developed for the next generation of electronics devices. In this project, we propose a new paradigm that is based on an artificial model of the neocortex: In which molecular circuits are assembled in a manner similar to the synaptic connections present in the brain. Our paradigm if realized offers the possibility of the design of the next generation of computational devices, with speeds that, in theory, could be 10 orders of magnitude faster than the fastest existing parallel supercomputer.