Issue 4, 2024

Biomass@MOF nanohybrid materials for competitive drug adsorption: analysis by conventional macroscopic models and statistical physical models

Abstract

This study discloses the design of nanohybrid Biomass@MOF resulting from the functionalization of a hydrochar (HC) through hydrothermal treatment (HT) of corn cob residues and MIL-53(Al). The nanohybrid, composed of 71 wt% Biomass and 29 wt% MOF, demonstrates stability and effectiveness after leaching and stability tests. Physicochemical characterization (SEM, BET, TGA, FTIR, XRD, XRF, and XPS) of the resulting hybrid confirms the development of a new nanostructure formed via in situ growth of MOF crystals by hybridizing reticular oxygen species present on the HC surface. HC@MIL-53(Al) served as an adsorbent for pharmaceutical compounds ketorolac (KTC) and naproxen (NPX) in a two-component and competing system. Under extreme conditions, HC@MIL-53(Al) proves to be a high-performing adsorbent, removing 100% of KTC and NPX in a mixture when their concentration is up to 150 ppm. Conventional isotherm models for bicomponent systems along with physical-statistical microscopic models revealed that the complex structure of the new composite utilizes functional groups of HC (–OH) and MIL-53(Al) (C[double bond, length as m-dash]C) for physisorption of both contaminants, leading to the formation of adsorbate–adsorbate multilayers. These characteristics are further validated by a desorption study, demonstrating the ability of recovered HC@MIL-53(Al) to adsorb both contaminants with approximately 90% of their initial capacity after 5 cycles.

Graphical abstract: Biomass@MOF nanohybrid materials for competitive drug adsorption: analysis by conventional macroscopic models and statistical physical models

Supplementary files

Article information

Article type
Paper
Submitted
18 Sad 2023
Accepted
05 Gur 2024
First published
16 Gur 2024

Environ. Sci.: Nano, 2024,11, 1543-1558

Biomass@MOF nanohybrid materials for competitive drug adsorption: analysis by conventional macroscopic models and statistical physical models

B. F. Rivadeneira-Mendoza, L. S. Quiroz-Fernández, F. F. da Silva, R. Luque, A. M. Balu and J. M. Rodríguez-Díaz, Environ. Sci.: Nano, 2024, 11, 1543 DOI: 10.1039/D3EN00843F

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