Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Institutions: Universidade de Santiago de Compostela, Consejo Superior de Investigaciones Científicas, Universidad de Zaragoza, Instituto de Nanociencia y Materiales de Aragón, Instituto de Ciencia de Materiales de Madrid, University of York, Fundacion Agencia Aragonesa para la Investigacion y el Desarrollo, Sheffield Hallam University, Institut de Nanociència i Nanotecnologia de la Universitat de Barcelona, Universitat de Barcelona
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G1105844351, G1238531971, G1610934229, G2064873908, G2193586530, G2462415295, G2933350706, G3005324705, G3082032993, G3546882995, G3956782466, G4141989982, G4228227490, G4293424916, G4462967385, G491712853, G510348230, G5458209022, G5635532810, G593054264, G6218905885, G6258374248, G67688110, G6948919564, G7127239378, G717256432, G7395345766, G767070311, G778664895, G7805626310, G7863371703, G787121397, G822417135, G8389847929, G8497050386, G8667377315, G888415022, G8888022036, G983705713
OpenAlex ID: W7220739225