The prediction of optimal conditions for the surface grafting of β-cyclodextrin onto silk fabrics by an artificial neural network (ANN)

Abstract

Purpose

The purpose of this study is the chemical grafting of β-Cyclodextrin (β-CD) onto silk fabrics by the use of butane tetracarboxylic acid (BTCA) as a crosslinking agent and nano-TiO₂ (NTO) as a catalyst. The effects of different parameters involved in this particular process, e.g. β-CD, BTCA and NTO concentrations, are examined using the artificial neural network (ANN). The method is evaluated for its ability to predict certain properties of treated fabrics, including grafting yield, dry crease recovery angle (DCRA) and wet crease recovery angle (WCRA), tensile strength, elongation at break and methylene blue dye absorption.

Design/methodology/approach

This study was conducted to describe the cross-linking of silk with 1,2,3,4-BTCA as a crosslinking agent in a wet state at low temperatures using NTO catalyst to improve the dry and wet wrinkle recovery (DCRA and WCRA) of silk fabrics. An ANN was also used to model and analyze the effects of BTCA, β-CD and NTO concentrations on the grafting percentage and some properties of the treated samples.

Findings

According to the results, the wet and dry wrinkle recovery of the silk fabrics was improved for about 38% and 11%, respectively, as compared to the non-cross-linked fabrics, without significantly affecting the tensile strength retention of the fabrics.

Originality/value

This research model and analyze the effects of BTCA, β-CD and NTO concentrations on the grafting percentage and some properties of the treated samples for the first time.

Citation

Zare, A. and Payvandy, P. (2023), "The prediction of optimal conditions for the surface grafting of β-cyclodextrin onto silk fabrics by an artificial neural network (ANN)", Pigment & Resin Technology, Vol. 52 No. 2, pp. 183-191. https://doi.org/10.1108/PRT-08-2021-0090