Adaptive neuro fuzzy selection of important factors for prediction of plasmons in silver nanorods

Abstract

The major goal of this study was to find predictors of plasmon positions in silver nanorod (NR) optical absorption spectra. The goal of this study is to use an adaptive neural fuzzy inference system to identify the various input parameters for longitudinal surface plasmon resonance (LSPR) and transverse surface plasmon resonance (TSP). A seed strategy has been used for preparation of the silver NRs. During the preparation, the seed particles are synthesized in the presence of cetyltrimethylammonium bromide (CTAB). To produce the silver NRs, metal salt (AgNO3) has been added, as well as ascorbic acid (AA) and CTAB. Skillful prediction could play a pivotal role in the plasmon NR production management. The combination of CTAB and the seeds has the largest influence on the TSPR. The combination of CTAB and AA has the largest influence on the LSPR. The study considering different input parameters simultaneously, to the best of our knowledge, is the first on a small scale and should attract great general interest.