ABSTRACT The significant influence of nanoparticle (NP) size and morphology on their physical and chemical properties has been extensively investigated in recent decades. However, equating morphology solely with the overall shape overlooks finer surface characteristics. In this study, we introduce an advanced shape‐fitting technique for the precise and automated extraction of NP characteristics from standard TEM images. This method captures detailed descriptors beyond size and shape, such as aspect ratio and radius of curvature, while maintaining statistical significance. Applying this approach, we identified a subtle difference in corner roundness between two batches of iron oxide nanocubes with identical size and aspect ratio distributions, synthesized consecutively under the same conditions. Yet, we report pronounced disparities in their magnetic properties and hyperthermia behavior. After ruling out internal variations through Mössbauer and X‐ray absorption spectroscopies, these discrepancies are attributed to the slight morphological differences between the nanocubes via surface spin disorder. Beyond providing a thorough reproducibility assessment, our findings underscore the critical importance of precise morphological characterization in establishing reliable shape–property relationships, essential for informed NP design and adherence to good manufacturing practices.
Poon et al. (Mon,) studied this question.
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