TY - JOUR

T1 - Single-Particle Characterization by Elastic Light Scattering

AU - Romanov, Andrey V.

AU - Yurkin, Maxim A.

N1 - Funding Information: The work was supported by the Russian Foundation for Basic Research (grant No. 19‐32‐90073). The authors thank two anonymous reviewers for their constructive comments.

PY - 2021/2

Y1 - 2021/2

N2 - The field of light-scattering characterization of single particles has seen a rapid growth over the last 30 years largely due to the progress in measurement and simulation capabilities. In particular, several methods have been developed to reliably characterize various particles, described by a model with several characteristics, with geometric resolution significantly better than the diffraction limit. However, their development has been largely fragmentary, limited to specific experimental set-ups. To fill this gap, these lines of development are reviewed within a unified framework. While focusing on characterization algorithms themselves, the experimental aspects related to the isolation and measurement of single particles are also discussed. The existing characterization methods are divided into three classes. The widest class is that of model-driven methods based on solving parametric inverse light-scattering problems, using a direct inversion of a low-dimensional mapping, a nonlinear regression, or neural networks. Other classes include model-free reconstruction methods and data-driven classification methods. This review is designed to be extensive in including all relevant literature, but the discussion of semi-quantitative imaging methods, such as tomography or holography-based reconstruction, is deliberately omitted. Throughout the review the development of various characterization methods is described, they are critically compared, and promising directions of future research are highlighted.

AB - The field of light-scattering characterization of single particles has seen a rapid growth over the last 30 years largely due to the progress in measurement and simulation capabilities. In particular, several methods have been developed to reliably characterize various particles, described by a model with several characteristics, with geometric resolution significantly better than the diffraction limit. However, their development has been largely fragmentary, limited to specific experimental set-ups. To fill this gap, these lines of development are reviewed within a unified framework. While focusing on characterization algorithms themselves, the experimental aspects related to the isolation and measurement of single particles are also discussed. The existing characterization methods are divided into three classes. The widest class is that of model-driven methods based on solving parametric inverse light-scattering problems, using a direct inversion of a low-dimensional mapping, a nonlinear regression, or neural networks. Other classes include model-free reconstruction methods and data-driven classification methods. This review is designed to be extensive in including all relevant literature, but the discussion of semi-quantitative imaging methods, such as tomography or holography-based reconstruction, is deliberately omitted. Throughout the review the development of various characterization methods is described, they are critically compared, and promising directions of future research are highlighted.

KW - characterization

KW - elastic light scattering

KW - inverse problem

KW - machine learning

KW - optimization

KW - single particle

UR - http://www.scopus.com/inward/record.url?scp=85099352053&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/92bfd784-d573-3f41-995d-115cafb42cf8/

U2 - 10.1002/lpor.202000368

DO - 10.1002/lpor.202000368

M3 - Review article

AN - SCOPUS:85099352053

VL - 15

JO - Laser and Photonics Reviews

JF - Laser and Photonics Reviews

SN - 1863-8880

IS - 2

M1 - 2000368

ER -