Solvothermal Synthesis Combined with Design of Experiments—Optimization Approach for Magnetite Nanocrystal Clusters

Abstract

Magnetite nanocrystal clusters are being investigated for their potential applications in catalysis, magnetic separation, and drug delivery. Controlling their size and size distribution is of paramount importance and often requires tedious trial-and-error experimentation to determine the optimal conditions necessary to synthesize clusters with the desired properties. In this work, magnetite nanocrystal clusters were prepared via a one-pot solvothermal reaction, starting from an available protocol. In order to optimize the experimental factors controlling their synthesis, response surface methodology (RSM) was used. The size of nanocrystal clusters can be varied by changing the amount of stabilizer (tribasic sodium citrate) and the solvent ratio (diethylene glycol/ethylene glycol). Tuning the experimental conditions during the optimization process is often limited to changing one factor at a time, while the experimental design allows for variation of the factors’ levels simultaneously. The efficiency of the design to achieve maximum refinement for the independent variables (stabilizer amount, diethylene glycol/ethylene glycol (DEG/EG) ratio) towards the best conditions for spherical magnetite nanocrystal clusters with desirable size (measured by scanning electron microscopy and dynamic light scattering) and narrow size distribution as responses were proven and tested. The optimization procedure based on the RSM was then used in reverse mode to determine the factors from the knowledge of the response to predict the optimal synthesis conditions required to obtain a good size and size distribution. The RSM model was validated using a plethora of statistical methods. The design can facilitate the optimization procedure by overcoming the trial-and-error process with a systematic model-guided approach.

Biomedical Applications of Iron Oxide Nanoparticles: Current Insights Progress and Perspectives

“…This is because of their distinctive performances based on their magnetic properties and nanoscale structure [22,23]. For the design of multi-functional MNPs, controlled surface engineering is critical for obtaining the required efficiency on the selected application [24]. The uses of MNPs as magnetic resonance imaging (MRI) agents for sensitive and precise diagnosis tools and synergistic combination with other imaging modalities were broadly explored.…”

Engineered magnetic oxides nanoparticles as efficient sorbents for wastewater remediation: a review

“…They also should have a higher surface area, narrow bandgaps for higher visible light activity, and an unambiguous ability to absorb heavy metals (Abdel Maksoud et al 2021c;Singh et al 2019b;Bhateria and Singh 2019;Gusain et al 2019). Although iron oxides exist naturally, they can also be synthesized and fabricated via many routes such as co-precipitation (de Mello et al 2019), hydrothermal (Phumying et al 2021), sol-gel (de Oliveira Guidolin et al 2021, and solvothermal (Medinger et al 2021).…”

“…Several magnetite nanoparticles such as Fe 3 O 4 nanocrystals (Medinger et al 2021), Fe 3 O 4 nanoparticles (Sani et al 2021;Namikuchi et al 2021;Gandon et al 2021), Fe 3 O 4 / graphene (Farghali et al 2021), and Fe 3 O 4 @Au@mTiO 2 composites (Rahman et al 2021) were prepared by the solvothermal technique. Many hematite composites have also been created, for example, α-Fe 2 O 3 nanoplates and nanocubes (Guo et al 2021a), α-Fe 2 O 3 thin films (Platnich et al 2021), Fe 2 O 3 photoanode (Zhang et al 2021b), Cu-doped α-Fe 2 O 3 nanoplates (Guo et al 2021b), and Au@ TiO 2 /α-Fe 2 O 3 (Mezni et al 2021).…”

Simultaneous optimisation of shape and magnetisation of nanoparticles synthesised using a green bioinspired route

“…30,31 A range of optimisation studies have been conducted including central composite designs, Box-Behnken designs, and optimal designs to map particle size, yield, and magnetisation for hydrothermal and co-precipitation magnetite syntheses. [32][33][34][35][36][37] However, previous studies reveal several drawbacks: all studies performed a single standalone design and did not optimise the system beyond their one-off experiment, with the exception of Medinger et al, 37 who used a feedback loop to refine the response surface methodology. Moreover, studies attempted to optimise only a single response at a time.…”