Metal-Organic Frameworks on Cotton-Linen Blend for Hydrophobicity

Abstract

Cotton Linen (CL) blended fabrics, extensively utilized in clothing, household items, and various applications, face limitations due to their inherent hydrophilicity, restricting their expansion into diverse fields. The emergence of multifunctional textiles has garnered attention, and the utilization of metal-organic framework materials in application development presents novel prospects for crafting versatile fabrics. This study focuses on creating a multifunctional composite, ZIF-8 CL blended fabric, boasting elevated hydrophobic properties through the incorporation of CL blended fabric as the foundational material. The experimentation involves compounding the fabric with ZIF-8, investigating the impact of different proportioning methods on the composite material. Characterization through SEM and other tests explores the influence of finishing conditions on the hydrophobic effect. The stability of hydrophobic CL blended fabrics is assessed via tests, including washing resistance, abrasion resistance, and exposure to chemical reagents. Results indicate that the PMHS fabric, post-hydrophobic protection and finishing, exhibits remarkable self-cleaning capabilities against water-based pollutants like mud, coffee and textile dye. The fabric displays excellent anti-sludge performance and demonstrates robust wear resistance. Moreover, it exhibits high stability under treatment conditions involving acid, alkali, and organic solvents. The findings contribute valuable insights and strategies for the future evolution of textile materials.

Authors and Affiliations

1Ibrahim Muhammad, 2Khan Talha

Keywords

Related Articles

Studies on Fusarium wilt of Watermelon by using Antifungal Agents: An Invitro in Sights

Watermelon (Citrullus lanatus) is highly prone to Fusarium wilt, caused by a deadly soil-borne pathogen called Fusarium oxysporum f. sp. niveum (FON). Changes in production practices in crops, reduction in usage of fumig...

Enhancing Malware Detection through Machine Learning: A Comparative Analysis of Random Forest and Naive Bayes Classification Systems

Malware, a type of malicious software encompassing viruses, worms, Trojans, backdoors, and spyware, poses a grave threat to the confidentiality, integrity, and functionality of computer systems, given their integral role...

Voice Vista

Blind image restoration involves leveraging prior information within an image to restore the sharpness of edges. De-blurring, on the other hand, aims to eliminate blurring artifacts caused by factors like defocus aberrat...

Preparation and Study of Stability Pharmaceutical Composition [PSMA-11] and Radiopharmaceutical [99mtc]-PSMA-11 Labeled With 99mtc Radionuclide

This work is devoted to the study of the synthesis of a pharmaceutical composition (PhC) [PSMA-11] in the form of a lyophilisate with optimization of the amount of ingredients and sodium pertechnetate (Na99mTcO4) from th...

Anti-Malware System Using Machine Learning Language

In today's interconnected digital landscape, the proliferation of malicious software, or malware, poses a grave threat to the security and integrity of computer systems and data. To combat this ever-evolving menace, ther...

Download PDF file
  • EP ID EP738881
  • DOI 10.62226/ijarst20241384
  • Views 6
  • Downloads 0

How To Cite

1Ibrahim Muhammad, 2Khan Talha (2024). Metal-Organic Frameworks on Cotton-Linen Blend for Hydrophobicity. International Journal of Advanced Research in Science and Technology (IJARST), 13(5), -. https://europub.co.uk/articles/-A-738881