纳米粒子的绿色合成由于其生态友好和可持续的方法以降低的成本合成纳米粒子而受到关注。人工神经网络(ANN)和响应面模型(RSM)对于减少纳米粒子合成中的实验工作量很重要。在这项工作中,使用植物提取物的体积进行Desmostachyabipinnata提取物的金纳米颗粒合成的优化,氯化金的浓度,反应时间,pH值,和温度作为工艺参数,输出为吸光度。从RSM获得实验设计,并使用ANN进一步优化模型。进行了RSM产生的32次实验运行,并将实验获得的结果与RSM和ANN产生的结果进行了比较。对人工神经网络的不同算法进行了测试,以获得最佳算法。优化研究导致第20次运行15毫升的最大响应,2.5mM,45分钟,7和40°C作为参数。通过RSM获得的优化输入参数为10ml,2mM,30分钟,6和30°C。通过紫外光谱证实了金纳米颗粒的形成,XRD,和SEM。人工神经网络的不同算法,比如LevenMarquardt,缩放共轭梯度,使用贝叶斯网络。发现Levenmarquardt算法是最适合当前研究的算法。
Green synthesis of nanoparticles has gained attention due to its eco-friendly and sustainable approach to synthesize nanoparticles at a reduced cost. Artificial neural network (ANN) and response surface model (RSM) are important to reduce experimental efforts in nanoparticle synthesis. In this work, optimization of gold nanoparticle synthesis by Desmostachya bipinnata extract was performed using the volume of plant extract, concentration of auric chloride, reaction time, pH, and temperature as process parameters, and the output was absorbance. The experimental design was obtained from RSM and the model was optimized further using ANN. Thirty-two experimental runs generated by RSM were performed and the results obtained experimentally were compared with those generated by RSM and ANN. Different algorithms of ANN were tested to obtain the best one. The optimization studies resulted in a maximum response for 20th run with 15 ml, 2.5 mM, 45 min, 7, and 40 °C as parameters. Optimized input parameters obtained by RSM were 10 ml, 2 mM, 30 min, 6, and 30 °C. The formation of gold nanoparticles was confirmed by UV spectroscopy, XRD, and SEM. Different algorithms of ANN, such as leven marquardt, scaled conjugate gradient, and bayesian network were used. Leven marquardt algorithm was found to be the most suitable algorithm for the current
study.