以接續式擬均勻設計法進行奈米銀微粒合成並尋找最適化製造條件

本研究以化學還原法合成奈米銀微粒，採用甲醛當還原劑，並以實驗數據建構的模式尋找最適化製造條件。首先考慮可能影響反應程序的因子為反應溫度、甲醛和氫氧化鈉與硝酸銀的莫耳比、保護劑PVP與硝酸銀的重量比以及不同的分子量。膠態產品主要描述的部分為平均粒徑以及硝酸銀還原成銀微粒的轉化率。安排44個實驗設計點，即可建構可信度高的模式，並提供我們獲得最適化製造條件: (a) 平均粒徑為28.63nm，轉化率為47.94% (b) 渴望目標值，平均粒徑為38.8nm，轉化率為97.41% (c) 平均粒徑為32.66nm、轉化率為85%，且與實驗結果相當接近。本研究以客觀的方式進行探討，首先應用部分因子實驗設計篩選影響力較小的因子(甲醛與硝酸銀的莫耳比)，並在接近最適化點的實驗條件下進行對照實驗，實驗結果顯示以PVP分子量為10,000進行實驗可得到較小的粒徑分佈。所以實驗因子從5個減少至3個因子，接下來以接續式擬均勻設計方法處理這個系統。均勻設計法 (UD) 能多因子多水平的分佈實驗點，接著使用類神經網路 (ANN) 或迴歸模式，可有效率建立未知系統的模式，所以應用在非線性多變數系統模式的建立。如果實驗的成本高，通常先採用較少分割水平的均勻設計來進行實驗。若實驗數據無法架構足以代表系統的類神經網路模式。因此我們研究室開發了接續式擬均勻設計 (Sequential Pseudo-uniform Design) 來接續均勻設計，即在原本的實驗範圍內安排實驗點。接著採用統計分析模式，確認模式可信度高，即可利用模式求得最佳操作條件，獲得期望的目標值，並以實驗驗證是否吻合。

關鍵字

接續式擬均勻設計；奈米銀；化學還原法；類神經網路

並列摘要

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.

並列關鍵字

artificial neural network ； chemical reduction ； nanosized silver particles ； sequential pseudo-uniform design

參考文獻

1.Chou, K. S.; Chiang, Y. R. Synthesis of Nanosized Silver Particles by Chemical Reduction Method. Materials Chemistry and Physics. 2000, 64, 241.

2.Chen, J.; Sheui, R. G. Using Taguchi’s Method and Orthogonal Function Approximation to Design Optimal Manipulated Trajectory in Batch Process. Ind. Eng. Chem. Res. 2002, 41, 2226.

3.Chang, J. S.; Lin, J. P. Product and Process Development via Pseudo-Sequential Uniform Design. Ind. Eng. Chem. Res. 2004, 43, 4278.

7.Jang, J. S. R.; Sun, C. T.; Mizutani, E. Neural-Fuzzy and Soft Computing; Prentice-Hall International: USA, 1997.

8.Montgomery, D. C. Design and Analysis of Experiments; A Wiley-Interscience Publication, New York, 1995.

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以接續式擬均勻設計法進行奈米銀微粒合成並尋找最適化製造條件

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