Impact of multi-factorial composition on the size of superparamagnetic iron oxide nanoparticles (SPIONs) using Box-Behnken statistical design
Year: 2025
Type: article
Abstract: The physicochemical properties of superparamagnetic iron oxide nanoparticles (SPIONs), particularly particle size, critically influence their performance in biomedical applications such as drug delivery, imaging, and magnetic hyperthermia. However, co-precipitation, a commonly used synthesis method for SPIONs, often leads to broad particle size distribution due to limited control over key reaction parameters. This study aims to optimise the synthesis of SPIONs by evaluating the influence of pH, surfactant concentration (PEG-400), and sonication time on particle size using a Box-Behnken response surface methodology (RSM). A total of 15 experimental runs were conducted, and a linear model was revealed to be the most suitable prediction model for particle size. The optimal synthesis conditions of 10 min of sonication time, 1 % PEG-400 and pH 9 produced SPIONs with a particle size of 145 nm. However, the analysis of variance did not indicate significant interactions among the parameters, potentially due to limitations in nanoparticle size measurement methods. Transmission electron microscopy (TEM) confirmed the spherical morphology and aggregation behaviour of the nanoparticles. Furthermore, in vitro cytotoxicity on 4T1 breast cancer cells demonstrated that the SPIONs were non-toxic up 25 μg/mL. These findings provide a valuable reference for the controlled synthesis of SPIONs and support their potential application in cancer theranostics.
Source: Nano-Structures & Nano-Objects
Authors: Siau Wui Chin, Min Li Yong, Sarah Lee Tung Law, Omar Hassanien, Jack Hau Ooi, Liang Ee Low, Kooi Yeong Khaw, Chee Wun How, Yong Sze Ong
Institution: Monash University Malaysia
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,130
Funder: Monash University Malaysia
Awards: G2904740624, G8572498334
OpenAlex ID: W4411438765
