journal article·2023-06Open access

Differential carbonization-shrinkage induced hierarchically rough PAN/PDMS nanofiber composite membrane for robust multimodal superhydrophobic applications

Adnan Ahmad, Hasan Albargi, Mumtaz Ali, Misbah Batool, Ahsan Nazir, Muhammad Bilal Qadir, Zubair Khaliq, Salman Noshear Arshad, Mohammed Jalalah, Farid A. Harraz

Abstract

Inducing roughness to achieve superhydrophobic surfaces through nanoparticlesʹ inclusion is a well-known concept; however, the consistency and secondary pollutants are challenges to be addressed. As a potential solution, we proposed a superhydrophobic nanofibrous membrane through the electrospinning of polyacrylonitrile and hydrophobic polydimethylsiloxane (PAN/H-PDMS) blended solution and post-heat treatment process. During carbonization, a drastic differential shrinkage between PAN and H-PDMS induces a hierarchically nanorough surface of the electrospun nanofiber. Thanks to the synergistic combination of micro-nano scale hierarchical roughness, a significant improvement in superhydrophobicity was observed with the water contact angle (WCA) of 163.48° and sliding angle (SA) of 4.2°. The proposed composite superhydrophobic nanofibrous membrane (CSN-M) exhibited excellent robustness against the tape peel, abrasion, and bending cycles by maintaining WCA higher than 158° and SA less than 6.5°. The outstanding self-healing feature recovered the WCA to 162.25° and lowered the SA to 5.0° after heat treatment at 60 °C. In addition, the CSN-M revealed a tensile modulus of 12.11 Mpa, a hydrostatic pressure of 39.18 cmH2O, and excellent breathability. The developed CSN-M is strong, with high permeability and outstanding mechano-chemical durability, making it a suitable choice for water/oil separation and self-cleaning applications.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Surface Modification and Superhydrophobicity · Advanced Sensor and Energy Harvesting Materials · Electrospun Nanofibers in Biomedical Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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