Cellulose acetate nanofiber modified with polydopamine polymerized MOFs for efficient removal of noxious organic dyes.
Esther EzeAhmed M OmerAhmed H HassaninAbdelazeem S EltaweilMohamed E El-KhoulyPublished in: Environmental science and pollution research international (2024)
The need to effectively remove toxic organic dyes from aquatic systems has become an increasingly critical issue in the recent years. In pursuit of this objective, polydopamine (PDA)-binary ZIF-8/UiO-66 (MOFs) was synthesized and incorporated into cellulose acetate (CA), producing ZIF-8/UiO-66/PDA@CA composite nanofibers under meticulously optimized conditions. The potential of fabricated nanofibers to remove cationic methylene blue (MB) dye was investigated. Various analysis tools including FTIR, XRD, SEM, zeta potential, BET, tensile strength testing, and XPS were employed. Results revealed a substantial leap in tensile strength, with ZIF-8/UiO-66/PDA@CA registering an impressive 2.8 MPa, as a marked improvement over the neat CA nanofibers (1.1 MPa). ZIF-8/UiO-66/PDA@CA nanofibers exhibit an outstanding adsorption capacity of 82 mg/g, notably outperforming the 22.4 mg/g capacity of neat CA nanofibers. In binary dye systems, these nanofibers exhibit a striking maximum adsorption capacity of 108 mg/g, establishing their eminence in addressing the complexities of wastewater treatment. Furthermore, the adsorption data fitted to the Langmuir isotherm, and the pseudo-second-order kinetic model. The fabricated nanofiber demonstrates good reproducibility and durability, consistently upholding its performance over five cycles. This suite of remarkable attributes collectively underscores its potential as a robust, durable, and highly promising solution for the effective and efficient removal of pernicious MB dye, in the context of both water quality improvement and environmental preservation.
Keyphrases
- aqueous solution
- metal organic framework
- wastewater treatment
- ionic liquid
- protein kinase
- machine learning
- risk assessment
- electronic health record
- highly efficient
- microbial community
- deep learning
- single cell
- big data
- patient safety
- high resolution
- artificial intelligence
- high speed
- atomic force microscopy
- single molecule