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Activity Number:
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290
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Type:
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Contributed
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Date/Time:
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Tuesday, August 8, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #305853 |
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Title:
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Deriving Optimal Conditions for Large-Scale Controlled Synthesis of Nanostructures Using Statistical Methods
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Author(s):
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Tirthankar Dasgupta*+ and Christopher Ma and Roshan J. Venghazhiyil and Zhong L. Wang and C. F. Jeff Wu
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Companies:
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Georgia Institute of Technology and Georgia Institute of Technology and Georgia Institute of Technology and Georgia Institute of Technology and Georgia Institute of Technology
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Address:
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765 Ferst Drive, NW, Atlanta, GA, 30332,
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Keywords:
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nanotechnology ; experimental design ; generalized linear model ; robust process
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Abstract:
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In this paper, an effort is made to systematically investigate the best process conditions that ensures large-scale synthesis of different types of one dimensional cadmium selenide nanostructures. Through a designed experiment and rigorous statistical analysis of experimental data, models linking the probabilities of obtaining specific morphologies to the process variables are developed. A new iterative algorithm for fitting a Multinomial GLM is proposed and used. The optimum process conditions that maximize the above probabilities and, at the same time, make the synthesis process less sensitive to variations of process variables around set values are derived from the fitted models using Monte-Carlo simulations.
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