A physical-statistical model for density control of nanowires

TY - JOUR

T1 - A physical-statistical model for density control of nanowires

AU - Dasgupta, Tirthankar

AU - Weintraub, Benjamin

AU - Joseph, V. Roshan

N1 - Funding Information: V. Roshan Joseph is the Coca-Cola Associate Professor in the School of Industrial and Systems Engineering at the Georgia Institute of Technology. He received his Ph.D. degree in Statistics from the University of Michigan in 2002. His research interests are in developing statistical methodologies for solving complex engineering problems. He is a recipient of the CAREER Award from the National Science Foundation in 2005, the Jack Youden Prize from the American Society for Quality in 2005, and a Best Paper Award from IIE Transactions in 2009. He currently serves as an Associate Editor of Technometrics and Operations Research. Funding Information: The research of Joseph was supported by the U.S. National Science Foundation under grants DMS-0706436 and CMMI-0654369. The research of Dasgupta was supported by the U.S. National Science Foundation under grant CMMI-1000720.

PY - 2011/4

Y1 - 2011/4

N2 - In order to develop a simple, scalable, and cost-effective technique for controlling zinc oxide nanowire array growth density, layer-by-layer polymer thin films were used in a solution-based growth process. The objective of this article is to develop a model connecting the thickness of polymer films to the observed density of nanowires that would enable prediction, and consequently control, of nanowire array density. A physical-statistical model that incorporates available physical knowledge of the process in a statistical framework is proposed. Model parameters are estimated using the maximum likelihood method. Apart from helping scientists achieve the basic objective of prediction control and quantification of uncertainty, the model facilitates a better understanding of the fundamental scientific phenomena that explain the growth mechanism.

AB - In order to develop a simple, scalable, and cost-effective technique for controlling zinc oxide nanowire array growth density, layer-by-layer polymer thin films were used in a solution-based growth process. The objective of this article is to develop a model connecting the thickness of polymer films to the observed density of nanowires that would enable prediction, and consequently control, of nanowire array density. A physical-statistical model that incorporates available physical knowledge of the process in a statistical framework is proposed. Model parameters are estimated using the maximum likelihood method. Apart from helping scientists achieve the basic objective of prediction control and quantification of uncertainty, the model facilitates a better understanding of the fundamental scientific phenomena that explain the growth mechanism.

KW - Fisher information

KW - Nanotechnology

KW - Poisson GLM

KW - diffusion

KW - maximum likelihood

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U2 - 10.1080/0740817X.2010.505124

DO - 10.1080/0740817X.2010.505124

M3 - Article

SN - 0740-817X

VL - 43

SP - 233

EP - 241

JO - IIE Transactions (Institute of Industrial Engineers)

JF - IIE Transactions (Institute of Industrial Engineers)

IS - 4

ER -