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Updated: May 11, 2026

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Regression Modeling of Oxygen-Functionalized Single-Walled Carbon Nanotubes in Aqueous Dispersions
Hoa Le1, Amos Abioye2, Hai V Nguyen3
1Philadelphia College of Pharmacy, Department of Pharmaceutical Sciences, Saint Joseph's University, 600 S. 43rd St, Philadelphia, Pennsylvania 19104, United States.
None:
Due to high drug loading and effective cell permeability, single-walled carbon nanotubes (SWCNTs) are promising nanocarriers for drug delivery systems. However, SWCNTs tend to aggregate in dispersion media because of their high aspect ratio and intrinsic hydrophobicity. Thus, the dispersion and stability of SWCNTs in water strongly depend on the dispersion method and CNT surface characteristics. Finding the optimal processing parameters and techniques for SWCNT dispersion remains challenging. This study proposes a surface modification technique (oxidation) and ultrasonication methodology to achieve well-dispersed and stable SWCNT dispersions in water. A D-optimal design was utilized to construct the experiment and investigate the effects of oxidation time and sonication conditions on hydrodynamic particle sizes (HDSs), polydispersity indices (PDIs), zeta potentials (ZPs), and Raman peak area ratios (RATs). The dynamic light scattering method was utilized to determine HDS, PDI, and ZP. Raman spectroscopy was employed to assess impurities and structural defects within SWCNTs by analyzing the Raman peak area ratio. FTIR spectroscopy and thermal gravimetric analysis were used to examine oxygenated groups on CNT surfaces. The morphology and elemental compositions of SWCNTs were determined using scanning electron microscopy combined with energy-dispersive X-ray analysis spectroscopy. The contour profiler was used to determine the optimal design space that produced dispersions with HDS, PDI, and ZP values below 250 nm, 0.350, -30 mV, respectively, and minimal defects in SWCNTs. The optimal dispersion conditions were identified using the desirability function. Overall, the models that described the relationship between the input and output factors were validated, enabling accurate prediction of responses and the optimization of input variables.
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