Green synthesized silver nanoparticles in two stages: Box Behnken Design to machine learning

Abstract

In order to solve the modeling issues due to data scarcity problems in the disciplines utilizing statistical approximations, a novel two-stage idea is proposed. As a use case, nanoparticle biosynthesis was selected, for which an environmentally friendly process is of vital importance. First, Box Behnken Design was used for experimental setup, quadratic model formulation and data generation. The second stage consists of Machine Learning, in which the data generated in the previous stage were fed into a Neural Network to determine the relationship between the parameters. Obtained results showed that the proposed combined strategy provided better nanoparticle size estimations than the statistical approach alone. In the absence of publicly available databases, data generation using experimental design and machine learning, as proposed here, could be a faster, lower-cost, and greener solution. Our proposed method can be applied to a wide range of biotechnology and bioengineering applications with significant advanced knowledge.

Keywords:

• Response surface methodology
• data generation
• deep learning
• neural networks
• silver nanoparticle biosynthesis

Statements and declarations

Authors’ contributions

Gulizar Caliskan: Conceptualization, Methodology, Writing-Review & Editing, Resources, Investigation, Formal Analysis. Ayca Kumluca Topalli: Conceptualization, Methodology, Writing-Review & Editing, Formal analysis, Software.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. As corresponding author, on the behalf of all authors, I confirm that the manuscript has been read and approved for submission by all the named authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data will be made available on request.