Bulletin of the American Physical Society
2008 APS March Meeting
Volume 53, Number 2
Monday–Friday, March 10–14, 2008; New Orleans, Louisiana
Session H4: Selected Applications Using Materials Science |
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Sponsoring Units: DMP Chair: Yvan Bruynseraede, Katholieke Universiteit Leuven Room: Morial Convention Center 206 |
Tuesday, March 11, 2008 8:00AM - 8:36AM |
H4.00001: Integrated Functionality: Nanosensors Invited Speaker: Integrated nanosensors are nanostructured systems in which several sensors of different types have been integrated on a single platform including those sensitive to optical, magnetic, chemical, or biological stimuli [1,2]. Nanoparticle-based detector systems rely on the development of nanoparticles as sensing species. I discuss state-of-the-art nanosensors which are based on various advanced materials: nanoshells and nanorice, gold nanoparticles with attached fluorescent dyes, nanopores, carbon nanotubes, neuroelectronic hybrid systems, semiconductor nanocrystals and quantum-dot quantum wells (QDQW's). Phonon-assisted optical processes in semiconductor nanocrystals and QDQW's are highly sensitive to their shape and geometry i. a. due to the non-adiabaticity of the exciton-phonon systems. This sensitivity opens new perspectives for applications of quantum dots in optical sensing. For example, the simultaneous consideration of the tetrahedral shape of a CdS/HgS/CdS QDQW, interface optical phonons, and non-adiabatic phonon-assisted transitions allows for control of the photoluminescence of a QDQW [3]. Quantum-dot-based systems are also considered as examples of communication with nanodevices, which is a prerequisite of their integration. Nanosensors allow for building a new class of integrated devices, which provide the elemental base for ``intelligent sensors'' capable of data processing, storage and analysis. The development of integrated nanosensors could have many applications in several fields such as process monitoring, robotics, environmental, medical, consumer, homeland security{\ldots} [1] I. K. Schuller, http://nanosensors.ucsd.edu/Introduction.htm. [2] J. T. Devreese and Y. Bruynseraede, Integrated Nanosensors. \textit{McGraw-Hill 2008 Yearbook of Science {\&} Technology, }McGraw-Hill, 2008. [3] V. A. Fonoberov, E. P. Pokatilov, V. M. Fomin, and J. T. Devreese, \textit{Phys. Rev. Lett.} \textbf{92}, 127402 (2004). [Preview Abstract] |
Tuesday, March 11, 2008 8:36AM - 9:12AM |
H4.00002: ABSTRACT WITHDRAWN |
Tuesday, March 11, 2008 9:12AM - 9:48AM |
H4.00003: Nanodevice sensors measured with rf- and microwave reflectometry Invited Speaker: Nanodevices can be extremely sensitive sensors, but they typically operate at low temperatures and have high impedances. This makes it hard to measure these devices at high frequencies, however this problem can be overcome by impedance matching these devices with resonant circuits using rf and microwave techniques. We will review a number of nanodevices probed with rf and microwave methods, both dissipative and non dissipative nanodevices can be measured in this way. Typical examples of dissipative devices are single electron transistors, quantum point contacts and scanning tunneling probes. The change in dissipation of the device will translate into a magnitude change of the reflected signal. Non-dissipative devices like parametric capacitances or inductances of superconducting circuits give rise to a shift in the resonance frequency of the resonant circuits which results in a phase shift of the reflected signal. Using these methods drastically increases the operation frequency and often also the sensitivity of the measured quantity. The non dissipative devices also have very low back-action and can potentially approach the limits set by quantum mechanics. [Preview Abstract] |
Tuesday, March 11, 2008 9:48AM - 10:24AM |
H4.00004: Detection of Explosive Materials Invited Speaker: High explosives present a challenge for detection methods because of their range of physical properties, which range from volatile liquids to nonvolatile solids. They share the common feature of possessing both oxidizing and reducing chemical properties within a single molecule or an intimate chemical mixture. Our research group has been focused on the synthesis of new luminescent polymers, which undergo electron transfer quenching by a variety of organic high explosives, such as TNT, RDX, and PETN. The application to imaging trace explosive particle residues will be described. Density functional calculations show an excellent correlation between the sensor response and the lowest unoccupied molecular orbital of the explosive analyte. For volatile high explosives, such as organic peroxides (e.g. TATP), vapor sensors based on chemically sensitive transistors containing different metal phthalocyanines have been explored. The mechanism of current response in these films has been shown to be a result of surface Lewis acid-base chemistry or redox catalysis at the metal centers. The link between surface chemistry and electronic resonse has led to a simple peroxide specific vapor sensor array. [Preview Abstract] |
Tuesday, March 11, 2008 10:24AM - 11:00AM |
H4.00005: Materials Informatics: Using machine learning techniques with large amounts of ab-initio computed or experimental data Invited Speaker: Machine learning techniques can be applied to large amounts experimental or computed materials data in order to identify the underlying factors that determine a target property. While the use of experimental data is complicated by the fact that it is mostly non-standardized in property or structure databases, experimental data still tends to be richer in information than computed data. One problem that can be addressed with machine learning techniques is the prediction of structure. By using structure prototype as a mathematical descriptor, and constructing its correlation in chemical spaces through machine learning techniques, it is possible to create a highly effective structure prediction method. Previously, we demonstrated that by simply applying maximum entropy ideas to a large experimental structure database of binary metals, it was possible to suggest a short list of candidate structures for new compounds which contains the proper ground state with very high probability [Ref]. This list of probable structures can then be computed with ab initio energy methods. We have now extended this method to multi-component and non-metal systems by prototyping the $\approx $ 100,000 structure records in the International Crytallographic Structure Database, and a similar accuracy of prediction is achieved in these high component spaces. We believe that such a machine learning approach solves the crystal structure prediction for many practical purposes. Machine learning techniques can also be used to point at likely errors in experimental structure databases and I will give some examples of this. In the long-term computed data is more likely to form the input for machine learning techniques as it is well defined and obtained under controlled conditions. Using high-throughput ab-initio computing techniques we have determined the structure and energy for several thousand compounds and have begun to data mine this information for property models relevant to energy generation and storage. [Preview Abstract] |
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