The Hidden Cost of Two-Dimensional TEM Reconstruction of Magnetic Nanoparticle Morphology

Abstract

Conventional transmission electron microscopy (TEM) records two-dimensional (2D) projections of three-dimensional (3D) objects, complicating the inference of nanoparticle morphology. This limitation is investigated for quasi-spherical iron oxide nanoparticles (IONPs) imaged after deposition on a TEM grid and after cellular internalization and immobilization in resin. A statistical inverse-reconstruction method is developed in which synthetic 3D particle populations are randomly oriented, projected onto a TEM-like plane, and compared with the experimental equivalent-diameter and axial-ratio distributions. Models of increasing geometrical complexity are considered, from monodisperse prolate spheroids to size- and shape-polydisperse prolate populations and triaxial ellipsoids. Size polydispersity is required to reproduce the projected equivalent-diameter distribution, whereas relaxing axial symmetry through a triaxial description enables simultaneous reproduction of the size and axial-ratio distributions. The inferred population represents a statistically compatible 3D morphology rather than a unique particle-by-particle reconstruction. Its physical relevance is assessed through a macrospin model incorporating the corresponding shape-anisotropy contributions. Application to magnetic fluid hyperthermia reveals a pronounced sensitivity of the predicted heating response to particle geometry and polydispersity. The approach provides a general framework for connecting conventional TEM measurements with morphology-dependent properties of nanoparticle ensembles.

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Cite

BibTeX RIS

BibTeX

@misc{lopezvazquez2026hidden,
  title = {{The Hidden Cost of Two-Dimensional TEM Reconstruction of Magnetic Nanoparticle Morphology}},
  author = {Iago López-Vázquez and Amira Páez and Yilian Fernández-Afonso and Roy W. Chantrell and Thomas S. van Zanten and Sergiu Ruta and Lucía Gutiérrez and Òscar Iglesias and David Serantes},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.04713},
  eprint = {2610.04713},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.04713},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - The Hidden Cost of Two-Dimensional TEM Reconstruction of Magnetic Nanoparticle Morphology
AU  - Iago López-Vázquez
AU  - Amira Páez
AU  - Yilian Fernández-Afonso
AU  - Roy W. Chantrell
AU  - Thomas S. van Zanten
AU  - Sergiu Ruta
AU  - Lucía Gutiérrez
AU  - Òscar Iglesias
AU  - David Serantes
PY  - 2026
DA  - 2026/10/03
PB  - arXiv
DO  - 10.48550/arxiv.2610.04713
UR  - https://arxiv.org/abs/2610.04713
AB  - Conventional transmission electron microscopy (TEM) records two-dimensional (2D) projections of three-dimensional (3D) objects, complicating the inference of nanoparticle morphology. This limitation is investigated for quasi-spherical iron oxide nanoparticles (IONPs) imaged after deposition on a TEM grid and after cellular internalization and immobilization in resin. A statistical inverse-reconstruction method is developed in which synthetic 3D particle populations are randomly oriented, projected onto a TEM-like plane, and compared with the experimental equivalent-diameter and axial-ratio distributions. Models of increasing geometrical complexity are considered, from monodisperse prolate spheroids to size- and shape-polydisperse prolate populations and triaxial ellipsoids. Size polydispersity is required to reproduce the projected equivalent-diameter distribution, whereas relaxing axial symmetry through a triaxial description enables simultaneous reproduction of the size and axial-ratio distributions. The inferred population represents a statistically compatible 3D morphology rather than a unique particle-by-particle reconstruction. Its physical relevance is assessed through a macrospin model incorporating the corresponding shape-anisotropy contributions. Application to magnetic fluid hyperthermia reveals a pronounced sensitivity of the predicted heating response to particle geometry and polydispersity. The approach provides a general framework for connecting conventional TEM measurements with morphology-dependent properties of nanoparticle ensembles.
ER  -

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