New insights into controlling the twin structure of magnetic iron oxide nanoparticles
Source abstract
Abstract
Abstract The concept of twinning has been extensively investigated in a range of metallic (e.g. Au, Ag, Cu and Pt) nanoparticle systems. However, the experimental strategy for introducing twinning in magnetic nanoparticle systems remains a grand challenge and unexplored to date. This is important to control the magnetic properties of iron oxide nanoparticles via the control of internal structural characteristics (i.e. the number of defects or twinned planes in the nanoparticles). The present work, for the first time, demonstrates how a judicious choice of bulkier ligands, based on alkylammonium halides, can be used to control the rate of the reaction and, thus, the high yield (>75%) formation of novel multiply twinned (polycrystalline decahedral and singly twinned) and untwinned (single crystalline octahedra and cubes) iron oxide nanoparticles. Based on new mechanistic understanding presented in this work, we discovered the enhancement and suppression of twinned structure within magnetic nanoparticles at low and high rate of the reaction, respectively. The multiply twinned magnetic nanoparticles show superior coercivity, higher cellular uptake, and enhanced magnetic actuation ability compared to untwinned nanoparticles of similar sizes. Overall, our work represents a paradigm shift in magnetic nanomaterials by providing new mechanistic insights, and guidance for controlling twinning and morphologies across a range of magnetic nanomaterials. This will ultimately allow us to tailor the magnetic properties through engineering the number of defects or twinning and will also have broad implications in nanotechnology, materials science, and engineering.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Iron oxide chemistry and applications · Characterization and Applications of Magnetic Nanoparticles · Nanoparticle-Based Drug Delivery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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