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Photocatalytic and antibacterial activity of graphene oxide/cellulose-doped TiO 2 quantum dots: in silico molecular docking studies

Muhammad Ikram, Fahad Rasheed, Ali Haider, Sadia Naz, Anwar Ul‐Hamid, Anum Shahzadi, Junaid Haider, Iram Shahzadi et al.

Nanoscale Advances, 2022

25 citations

Abstract

Graphene oxide (GO) and cellulose nanocrystal (CNC)-doped TiO2 quantum dots (QDs) were effectively synthesized by employing the co-precipitation method for the degradation of dyes and antimicrobial applications. A series of characterizations, i.e., XRD, FTIR, UV-visible spectroscopy, EDS, FE-SEM, and HR-TEM, was used to characterize the prepared samples. A reduction in PL intensity was observed, while the band gap energy (E g) decreased from 3.22 to 2.96 eV upon the incorporation of GO/CNC in TiO2. In the Raman spectra, the D and G bands were detected, indicating the presence of graphene oxide in the composites. Upon doping, the crystallinity of TiO2 increased. HR-TEM was employed to estimate the interlayer d-spacing of the nanocomposites, which matched well with the XRD data. The photocatalytic potential of the prepared samples was tested against methylene blue, methylene violet, and ciprofloxacin (MB:MV:CF) when exposed to visible light for a certain period. The antibacterial activity of GO/CNC/TiO2 QDs against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) bacteria in vitro was tested to determine their potential for medicinal applications. The molecular docking investigations of CNC-TiO2 and GO/CNC-doped TiO2 against DNA gyrase and FabI from E. coli and S. aureus were found to be consistent with the results of the in vitro bactericidal activity test. We believe that the prepared nanocomposites will be highly efficient for wastewater treatment and…

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Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.

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