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Updated: Jul 17, 2025

Designing Porous Silicon Films as Carriers of Nerve Growth Factor
Published on: January 25, 2019
Theoretical insights and implications of pH-dependent drug delivery systems using silica and carbon nanotube
Nashiour Rohman1, Khalid Ahmed2, Adam A Skelton3
1Department of Physics, College of Science, Sultan Qaboos University, P. O. Box 36, Al-khoudh, Muscat, P. C. 123, Oman.
Abstract:
In this paper we have studied the density functional theory of four drugs ibuprofen, alendronate, Sulfasalazine and paracetamol with quartz, propylamine, trimethylamine functionalized quartz and carboxyl modified carbon nanotube. The attractive and repulsive interaction energies between drugs and quartz is obtained at various pH values. The attractive and repulsive energies are well correlated with experimental drug loading and releasing behavior by mesoporous silica nanoparticles. Further, a theoretical model is developed that accounts the electrostatic interaction between silica and drug and the model can predict the drug loading and releasing behavior by silica nanoparticles at various pH values. Sulfasalazine can be taken orally and loaded with trimethyl ammonium functionalized mesoporous silica nanoparticles, which keeps the drug in tact with the carrier in the acidic environment of the stomach and releases it into the neutral or basic medium of the small intestine. Alendronate may be loaded and released from propylamine functionalized mesoporous silica nanoparticles in the ranges of 1-5 and > 8, respectively. Ibuprofen is absorbed in an acidic environment and released in basic conditions for carboxyl modified carbon nanotube. The loading and releasing pH ranges for paracetamol in trimethylammonium functionalized mesoporous silica nanoparticles are 4-8 and >8, respectively. We also convert the pH-dependent variant of the diffusion-controlled Higuchi equation. We have changed the original Higuchi equation to produce the pH-dependent variation by incorporating the Nernst-Planck equation into Flick's first law. The updated equation could be used to forecast when medication particles with varying release times will emerge from a nanoparticles matrix.
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