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Effective Adsorption and Removal of Doxorubicin from Aqueous Solutions Using Mesostructured Silica Nanospheres: Box-Behnken Design Optimization and Adsorption Performance Evaluation.

Khalid AlthumayriAhlem GuesmiWesam Abd El-FattahLotfi KhezamiTaoufik SoltaniNaoufel Ben HamadiAhmed Shahat
Published in: ACS omega (2023)
The aim of this study is to evaluate the efficacy of mesoporous silica nanospheres as an adsorbent to remove doxorubicin (DOX) from aqueous solution. The surface and structural properties of mesoporous silica nanospheres were investigated using BET, SEM, XRD, TEM, ζ potential, and point of zero charge analysis. To optimize DOX removal from aqueous solution, a Box-Behnken surface statistical design (BBD) with four times factors, four levels, and response surface modeling (RSM) was used. A high amount of adsorptivity from DOX (804.84 mg/g) was successfully done under the following conditions: mesoporous silica nanospheres dose = 0.02 g/25 mL; pH = 6; shaking speed = 200 rpm; and adsorption time = 100 min. The study of isotherms demonstrated how well the Langmuir equation and the experimental data matched. According to thermodynamic characteristics, the adsorption of DOX on mesoporous silica nanospheres was endothermic and spontaneous. The increase in solution temperature also aided in the removal of DOX. The kinetic study showed that the model suited the pseudo-second-order. The suggested adsorption method could recycle mesoporous silica nanospheres five times, with a modest reduction in its ability for adsorption. The most important feature of our adsorbent is that it can be recycled five times without losing its efficiency.
Keyphrases
  • aqueous solution
  • machine learning
  • transcription factor
  • cancer therapy
  • electronic health record
  • big data
  • neural network