Bayesian Analysis for Robust Synthesis of Nanostructures

• Autorzy: Nader Ebrahimi , Mahmoud Shehadeh , Kristin McCullough
• Języki publikacji: EN

Abstrakty

EN

Nanomaterials, because of their unique properties such as extremely small size and increased ratio of surface area to volume, have a great potential in many industrial applications that involve electronics, sensors, solar cells, super-strong materials, coatings, drug delivery, and nanomedicine. They have the potential also to improve the environment by direct applications of these materials to detect, prevent and remove pollutants. While nanomaterials present seemingly limitless possibilities, they bring with them new challenges. Among them is the precise control of the morphology of nanomaterials, which is extremely critical to the development of advanced nanodevices with various functionalities. The one-dimensional nanostructures of Cadmium Selenide (CdSe) have been found to represent morphologies of nanowires, nanobelts, and nanosaws, however, their synthesis is by trial and error. Predictive modeling and control methods are essential to process yield and productivity improvement. The process yield (response) is a vector whose elements correspond to the number of appearances of the different types of nanostructures, namely nanosaws, nanowires, and nanobelts. The goal in this paper is to apply existing Bayesian methodologies to describe the growths of these nanostructures in terms of process variables and to predict the probability of transition from one nanostructure to another when changes are made to one or more process variables. We also propose a Bayesian algorithm to identify the optimal process conditions that maximize the predicted probability of each type of nanostructure.

Słowa kluczowe

EN

Generalized linear models; Multinomial; Bayesian statistical modeling; Markov Chain Monte Carlo; Predictive analysis and control; CdSe nanostructures

Kategorie tematyczne

• 62.23.Eg: Nanodots
• 87.15.A-: Theory, modeling, and computer simulation
• 73.63.Fg: Nanotubes
• 62.25.-g: Mechanical properties of nanoscale systems(for structure of nanoscale systems, see 61.46.-w; for structural transitions in nanoscale materials, see 64.70.Nd; for electronic transport in nanoscale systems, see 73.63.-b)
• 42.62.Be: Biological and medical applications(see also 87.50.W-, 87.63.L-, and 87.80.Cc in biological and medical physics)

• 80M31: Monte Carlo methods
• 82D80: Nanostructures and nanoparticles
• 78M10: Finite element methods
• 74N05: Crystals
• 74F15: Electromagnetic effects

• Wydawca: De Gruyter Open
• Czasopismo: Nanoscale Systems: Mathematical Modeling, Theory and Applications
• Rocznik: 2012
• Tom: 1
• Strony: 172-186

Daty

otrzymano

2012-09-15

poprawiono

2012-12-10

zaakceptowano

2012-12-12

online

2012-12-28

Twórcy

autor

Nader Ebrahimi

• Division of Statistics, Northern Illinois University,
  DeKalb, IL 60115, USA

autor

Mahmoud Shehadeh

• Division of Statistics, Northern Illinois University,
  DeKalb, IL 60115, USA

autor

Kristin McCullough

• Division of Statistics, Northern Illinois University,
  DeKalb, IL 60115, USA

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Identyfikatory

DOI

10.2478/nsmmt-2012-0010