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namely polymersomes, by using an amphiphilic copolymer as lipid mimics. These vesicles were extruded by a dried film, after rehydration with a basic solution of sodium hydroxide, that filled the polymersome core. So prepared structure was used as nanoreactor for the in situ synthesis of magnetic nanoparticles. In detail, polymersomes were dispersed in a FeCl2/FeCl3 solution, and the mixture was electroporated to open up pores within the membrane. By this method, ultrasmall magnetic nanoparticles (average diameter of 2.5 nm) were coprecipitated in the bilayer of the polymersome (Bain et al. 2015).

      Hugounenq et al. exploited a polyol synthesis for the preparation of a multicore superstructure, henceforth referred to as nanoflower. These colloidal nanoparticles were obtained by alkaline hydrolysis of iron(II) and iron(III) in the presence of diethylene glycol and N‐methyldiethanolamine, by addition of NaOH and annealing at high temperature (220 °C). The flower‐like structure resulted from the maghemite nanoparticles assembly whose size is about 11 nm, whereas the overall nanocluster size ranged from 24 to 55 nm. The magnetic nanoflowers exhibited a higher‐level heating performance in magnetic hyperthermia, with a SAR value close to 2000 W g−1 (Hugounenq et al. 2012). Recently, these nanostructures have been used as a model for the in vivo analysis of heating performance once the nanomaterials are administered to tumor target. So the authors found that the suppression of effective heating efficiency is neither due to Brownian mechanism inhibition nor to particles aggregation, but it is mainly related to the irregular distribution of the nanomaterial in the tumor matrix (Coral et al. 2018).

      3.3.2 Inorganic Coatings

      Various preparation methods of magnetic clusters considered inorganic coatings for the stabilization of multistructures. The magnetic nanoparticles encapsulation within a silica sphere is one of the most followed approaches, as described and developed by Taboada et al. First, the nanoparticles were aggregated in a controlled manner by a sol‐gel method in a mixture of acetone and hexane. The magnetic clusters served as nucleation sites for the subsequent condensation of hydrolyzed tetramethoxy silane (TMOS), leading to the formation of the silica shell. The obtained structures had a diameter of approximately 100 nm, and the production of grams of magnetic clusters was demonstrated (Taboada et al. 2009). Also, Niu et al. chose the encapsulation into silica for the preparation of their nanocomposite (Niu et al. 2010). By using an oil‐in‐water approach, the hydrophobic magnetic nanoparticles were first incorporated into the copolymer micelles core composed of polystyrene100block‐poly(acrylic acid)16. Therefore, a silica layer was grown on top of the micelles by using 3‐mercaptopropyltrimethoxysilane (MPMTS) as priming molecule. Also, in that case, nanostructures with a final diameter smaller than 100 nm were fabricated.

      Kralj and Makovec (2015) assessed the preparation of superparamagnetic nanostructures with highly anisotropic shapes, called nanochains. In this work, their group used some commercial nanoclusters, obtained through a nanoemulsion process and finally stabilized by a tuned silica shell. These nanostructures were formed by interaction between nanoclusters and a controlled magnetic field in presence of PVP molecules. By this way, the authors achieved the formation of small chains composed of different monomers, by tuning the field strength and exposure duration, the PVP concentration, and the stirring speed. The purpose of this clustering approach should lead to applications of the nanochains in the cancer treatment and in the ability to magnetically manipulate liquid and photonic crystals (Kralj and Makovec 2015).

      Recently, Tregubov et al. used a metal‐organic framework as a substrate for the magnetic nanoclusters coating. The authors used the citrate‐capped iron oxide nanoparticles as starting material, obtained by autoclave and by using urea as precipitant. This magnetic core with a size of 80 nm and composed by several single magnetic domains was further coated by a matrix of iron(III) trimesate (also known as MIL‐100(Fe)) and synthesized by autoclaving the mixture of nanoparticles and metal precursor. Finally, a carboxymethyl dextran layer was grafted onto the surface of the system for achieving optimal colloidal stability. An advantage of such multilayered object was the assessed biocompatibility since the iron(III) trimesate can be readily degraded in physiological conditions because of the collapse of the framework in the presence of phosphates (Tregubov et al. 2018).

      3.3.3 Polymer‐Assisted Clustering

      Polymers, large molecules composed of many repeated subunits, are probably the most studied class of molecules used in the nanoparticle clustering. These compounds have several specific physical–chemical properties that allow to, first, help the nanoparticles assembly and, second, provide some specific functional groups on the surface for the conjugation of ligands.

      In the last decade, magnetic nanocubes have aroused interest in the field of materials science as a heat mediator, due to different crystalline and shape anisotropies, compared to the most common spheres, resulting in higher heat capacity (Guardia et al. 2012; Noh et al. 2012). From clustering point of view, this class of nanomaterial is not straightforward to be managed because of the strong interparticle interaction. Materia et al. modified the previously reported procedure for spherical particles adjusting the solvent mixture and the injection rate of the polar solvent. When clustered, the nanocubes showed a lower SAR value, due to the suppression of Brownian contribution into the blocked polymeric superstructure. On the opposite, the relaxivities analysis resulted in a very low r1 value and a definite increase of the r2/r1 ratio, in comparison to the individual nanocubes (Materia et al. 2015). Recently, the same group investigated how the heat performance of the iron oxide nanocubes could be preserved or even enhanced by clustering. A possible answer is represented by

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