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Title page for etd-0729105-135627


URN etd-0729105-135627 Statistics This thesis had been viewed 2041 times. Download 987 times.
Author Yuan-Ping Lee
Author's Email Address No Public.
Department Chemical Engineering
Year 2004 Semester 2
Degree Master Type of Document Master's Thesis
Language English Page Count 135
Title Synthesis and Optimization of Nanosized Silver Particles Via Sequential Pseudo-Uniform Design Method
Keyword
  • sequential pseudo-uniform design
  • nanosized silver particles
  • chemical reduction
  • artificial neural network
  • artificial neural network
  • chemical reduction
  • nanosized silver particles
  • sequential pseudo-uniform design
  • Abstract A data-driven model based optimization on the synthesis of nanosized silver particles by chemical reduction using formaldehyde in aqueous solution was studied in this work. Effects of the possible processing variables such as the reaction temperature T, the mole ratios of [formaldehyde]/[AgNO3] and [NaOH]/[AgNO3], PVP/AgNO3, and the molecular weight of protective agent PVP (polyvinyl-pyrrolidone) were considered. The colloid dispersion products were mainly characterized for its mean particle size and conversion of silver nitrate. The identified model based on the 44 designed experiments can provide us the optimal conditions for achieving (a) the minimum mean particle size (28.63nm) with conversion (47.94%), (b) the desired targets (mean particle size, 38.8nm and conversion, 97.41%), and (c) the desired targets (mean particle size, 32.66nm and conversion, 85%) closely. To accomplish the objectives of this work, the fractional factorial design was first applied to screen the insignificant factor [formaldehyde]/[AgNO3]. By the contrast experiment done at the near-optimal condition for achieving the minimum particle size of the product, the PVP with MW (10,000) was chosen. A resulting 3 significant factors problem were then solved by the developed SPUD (Sequential Pseudo-Uniform Design) method.
     The application of the uniform design (UD) method to nonlinear multivariate calibration by an artificial neural network (ANN) or a regression model can be used to build a model for an unknown process efficiently because it allows many levels for each factor. If the cost of each experiment is high, low partitioned levels are usually proposed first to carry out the experiments. However, if a reliable model cannot be obtained from the designed experiments, the developed (SPUD) method in our laboratory can be employed to locate additional experiments in the experimental region. Once the identified model is verified as reliable based on the statistical analysis, the optimal operating conditions can be determined to guide the process to the desired objective and were demonstrated experimentally in this work.
    Advisor Committee
  • Jyh-Shyong Chang - advisor
  • none - co-chair
  • none - co-chair
  • Files indicate access worldwide
    Date of Defense 2005-07-06 Date of Submission 2005-07-29


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