Journal of Engineered Fibers and Fabrics (Dec 2023)

A sustainable blend of Tencel/jute fibers as an alternative to cotton/polyester for clothing

  • Zahid Sarwar,
  • Faheem Ahmad,
  • Adnan Ahmad,
  • Sheraz Ahmad

DOI
https://doi.org/10.1177/15589250231215458
Journal volume & issue
Vol. 18

Abstract

Read online

The increasing concern about the environment has enhanced the utilization of sustainable products in every field of life, from common household items to fabrics. Although cotton is a plant-based fiber, its cultivation requires large amounts of water and pesticides. Therefore, the future of product development demands environmental friendliness. To overcome the problems of cotton related to the environment, a novel sustainable fabric was developed by using Tencel and jute fiber for the apparel industry to reduce the usage of cotton. In this current study, six blended yarns of two counts (10 & 20 Ne) of Tencel and jute (90:10, 80:20, and 70:30) were developed by ring spinning to weave fabric in two weave designs (Plain & Twill). These developed fabrics were assessed for mechanical, comfort, and hand properties. The results revealed that the blended fabric with a high content of Tencel is good in terms of mechanical, comfort, and hand properties, except for stiffness, pilling resistance, resilience, and drapeability. Additionally, fabric woven with a fine yarn count is also good in the above-stated properties, except for stiffness, resilience, and wrinkle recovery. However, fabrics woven with a twill weave design have good comfort and hand properties, except for smoothness and drapability. On the other hand, fabric with a plain weave design is good in terms of mechanical properties, except for tear strength. The comparison of TJ blended fabric with PC blended fabric shows that the tensile strength, air permeability and OMMC of JT blended fabric is 8.31%, 96%, and 29.46% higher than PC blended fabric but tear strength is 99% less. Furthermore, statistical analysis was performed to check the effect of the input variables on the response factors, and it was found that the effect of yarn count on all output responses is statistically the most significant.