摘要
为有效缩短蒸馏水中脉冲激光烧蚀制备A g纳米粒子胶体工艺中繁琐的实验过程,采用LmN et PF神经网络平台对制备工艺与平均粒径及粒径分布的关系进行建模,并将其运用到平均粒径及其分布的预测中去,讨论了激光能量密度、激光重复率、烧蚀时间和平均粒径及其分布的关系。克服了以往单因素实验法不能正确反映制备工艺和平均粒径及其分布之间复杂的非线性关系的弱点。预测和验证结果均表明实验值和网络预测值之间相对误差都在10%以内,从而表明神经网络能够更精确、更可靠地逼近它们之间的非线性关系。该方法为有效、快捷、经济地开发研制金属纳米粒子胶体提供了新的思路和有效手段。
In order to shorten the fussy experimental process in preparing colloidal solutions of silver nanoparticles by pulsed laser ablation in distilled water,a LmNet PF neural network model is developed to approach the complex nonlinear relationship between technology parameters and the average diameter and its distribution for preparing colloidal solutions of silver nanoparticles respectively.By using the const ructed neural network model,the relationship between the technology parameters（laser fluence,laser repetition,ablation time） and the average diameter and its distribution is discussed,and the weakness that the nonlinear relationship could not be ap-proached more accurately,effectively by using of single-factor-experiment method is overcomed.Predicted and test results showed that all the relative errors between the desired values and predicted outputs of the network are less than 10%,but the predicted data of the neural network model are well acceptable when comparing them to the real test values,hence providing a effective,economical way for preparing colloidal solutions of silver nanoparticles.
出处
《光电子技术》
CAS
北大核心
2009年第3期191-195,205,共6页
Optoelectronic Technology
基金
浙江省自然科学基金青年科技人才培养项目(R405031)
浙江省教育厅专项任务资助(20051441)
关键词
AG纳米粒子
激光烧蚀
平均粒径
粒径分布
人工神经网络
Ag colloidal particle
laser ablation
average diameter
size distribution
artifical neural network
