Iron oxide magnetic aggregates: Aspects of synthesis, computational approaches and applications.

Superparamagnetic magnetite nanoparticles have been central to numerous investigations in the past few decades for their use in many applications, such as drug delivery, medical diagnostics, magnetic separation, and material science. However, the properties of single magnetic nanoparticles are sometimes not sufficient to accomplish tasks where a strong magnetic response is required. In light of this, aggregated magnetite nanoparticles have been proposed as an alternative advanced material, which may expand and combine some of the advantages of single magnetic nanoparticles, including superparamagnetism, with an enhanced magnetic moment and increased colloidal stability. This review comprehensively discusses the current literature on aggregates made of magnetic iron oxide nanoparticles. This review is divided into three sections. First, the current synthetic strategies for magnetite nanoparticle aggregates are discussed, together with the influence of different stabilizers on the primary crystals and the final aggregate size and morphology. The second section is dedicated to computational approaches, such as density functional methods (which permit accurate predictions of electronic and magnetic properties and shed light on the behavior of surfactant molecules on iron oxide surfaces) and molecular dynamics simulations (which provide additional insight into the influence of ligands on the surface chemistry of iron oxide nanocrystals). The last section discusses current and possible future applications of iron oxide magnetic aggregates, including wastewater treatment, water purification, medical applications, and magnetic aggregates for materials displaying structural colors.

Magnetically Guided Synthesis of Anisotropic Porous Carbons toward Efficient CO2 Capture and Magnetic Separation of Oil.

Conventional synthetic strategies do not allow one to impart structural anisotropy into porous carbons, thus leading to limited control over their textural properties. While structural anisotropy alters the mechanical properties of materials, it also introduces an additional degree of directionality to increase the pore connectivity and thus the flux in the designed direction. Accordingly, in this work the structure of porous carbons prepared from resorcinol-formaldehyde gels has been rendered anisotropic by integrating superparamagnetic colloids to the sol-gel precursor solution and by applying a uniform magnetic field during the sol-gel transition, which enables the self-assembly of magnetic colloids into chainlike structures to template the growth of the gel phase. Notably, the anisotropic pore structure is maintained upon pyrolysis of the gel, leading to hierarchically porous carbon monoliths with tunable structure and porosities. With an advantage granted to anisotropic materials, these porous carbons showed higher porosity, a higher CO2 uptake capacity of 3.45 mmol g-1 at 273 K at 1.1 bar, and faster adsorption kinetics compared to the ones synthesized in the absence of magnetic field. Moreover, these materials were also used as magnetic sorbents with fast adsorption kinetics for efficient oil-spill cleanup and retrieved easily by using an external magnetic field.

Preparation and Machine-Learning Methods of Nacre-like Composites from the Self-Assembly of Magnetic Colloids Exposed to Rotating Magnetic Fields.

Composite materials designed by nature, such as nacre, can display unique mechanical properties and have therefore been often mimicked by scientists. In this work, we prepared composite materials mimicking the nacre structure in two steps. First, we synthesized a silica gel skeleton with a layered structure using a bottom-up approach by modifying a sol-gel synthesis. Magnetic colloids were added to the sol solution, and a rotating magnetic field was applied during the sol-gel transition. When exposed to a rotating magnetic field, magnetic colloids organize in layers parallel to the plane of rotation of the field and template the growing silica phase, resulting in a layered anisotropic silica network mimicking the nacre's inorganic phase. Heat treatment has been applied to further harden the silica monoliths. The final nacre-inspired composite is created by filling the porous structure with a monomer, leading to a soft elastomer upon polymerization. Compression tests of the platelet-structured composite show that the mechanical properties of the nacre-like composite material far exceed those of nonstructured composite materials with an identical chemical composition. Increased toughness and a nearly 10-fold increase in Young's modulus were achieved. The natural brittleness and low elastic deformation of silica monoliths could be overcome by mimicking the natural architecture of nacre. Pattern recognition obtained with a classification of machine learning algorithms was applied to achieve a better understanding of the physical and chemical parameters that have the highest impact on the mechanical properties of the monoliths. Multivariate statistical analysis was performed to show that the structural control and the heat treatment have a very strong influence on the mechanical properties of the monoliths.

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

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.