Counting and mapping of subwavelength nanoparticles from a single shot scattering pattern

Particle counting is of critical importance for nanotechnology, environmental monitoring, pharmaceutical, food and semiconductor industries. Here we introduce a super-resolution single-shot optical method for counting and mapping positions of subwavelength particles on a surface. The method is based...

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Main Authors: Chan Eng Aik, Rendón-Barraza Carolina, Wang Benquan, Pu Tanchao, Ou Jun-Yu, Wei Hongxin, Adamo Giorgio, An Bo, Zheludev Nikolay I.
Format: Article
Language:English
Published: De Gruyter 2023-01-01
Series:Nanophotonics
Subjects:
Online Access:https://doi.org/10.1515/nanoph-2022-0612
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author Chan Eng Aik
Rendón-Barraza Carolina
Wang Benquan
Pu Tanchao
Ou Jun-Yu
Wei Hongxin
Adamo Giorgio
An Bo
Zheludev Nikolay I.
author_facet Chan Eng Aik
Rendón-Barraza Carolina
Wang Benquan
Pu Tanchao
Ou Jun-Yu
Wei Hongxin
Adamo Giorgio
An Bo
Zheludev Nikolay I.
author_sort Chan Eng Aik
collection DOAJ
description Particle counting is of critical importance for nanotechnology, environmental monitoring, pharmaceutical, food and semiconductor industries. Here we introduce a super-resolution single-shot optical method for counting and mapping positions of subwavelength particles on a surface. The method is based on the deep learning analysis of the intensity profile of the coherent light scattered on the group of particles. In a proof of principle experiment, we demonstrated particle counting accuracies of more than 90%. We also demonstrate that the particle locations can be mapped on a 4 × 4 grid with a nearly perfect accuracy (16-pixel binary imaging of the particle ensemble). Both the retrieval of number of particles and their mapping is achieved with super-resolution: accuracies are similar for sets with closely located optically unresolvable particles and sets with sparsely located particles. As the method does not require fluorescent labelling of the particles, is resilient to small variations of particle sizes, can be adopted to counting various types of nanoparticulates and high rates, it can find applications in numerous particles counting tasks in nanotechnology, life sciences and beyond.
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spelling doaj.art-e55115a0c49a45d7b5bf5f31001bede82024-11-25T11:19:09ZengDe GruyterNanophotonics2192-86062192-86142023-01-0112142807281210.1515/nanoph-2022-0612Counting and mapping of subwavelength nanoparticles from a single shot scattering patternChan Eng Aik0Rendón-Barraza Carolina1Wang Benquan2Pu Tanchao3Ou Jun-Yu4Wei Hongxin5Adamo Giorgio6An Bo7Zheludev Nikolay I.8Centre for Disruptive Photonic Technologies, The Photonics Institute, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371Singapore, SingaporeCentre for Disruptive Photonic Technologies, The Photonics Institute, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371Singapore, SingaporeCentre for Disruptive Photonic Technologies, The Photonics Institute, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371Singapore, SingaporeCentre for Photonic Metamaterials and Optoelectronics Research Centre, University of Southampton, SouthamptonSO17 1BJ, UKCentre for Photonic Metamaterials and Optoelectronics Research Centre, University of Southampton, SouthamptonSO17 1BJ, UKSchool of Computer Science and Engineering, Nanyang Technological University, 639798Singapore, SingaporeCentre for Disruptive Photonic Technologies, The Photonics Institute, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371Singapore, SingaporeSchool of Computer Science and Engineering, Nanyang Technological University, 639798Singapore, SingaporeCentre for Disruptive Photonic Technologies, The Photonics Institute, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371Singapore, SingaporeParticle counting is of critical importance for nanotechnology, environmental monitoring, pharmaceutical, food and semiconductor industries. Here we introduce a super-resolution single-shot optical method for counting and mapping positions of subwavelength particles on a surface. The method is based on the deep learning analysis of the intensity profile of the coherent light scattered on the group of particles. In a proof of principle experiment, we demonstrated particle counting accuracies of more than 90%. We also demonstrate that the particle locations can be mapped on a 4 × 4 grid with a nearly perfect accuracy (16-pixel binary imaging of the particle ensemble). Both the retrieval of number of particles and their mapping is achieved with super-resolution: accuracies are similar for sets with closely located optically unresolvable particles and sets with sparsely located particles. As the method does not require fluorescent labelling of the particles, is resilient to small variations of particle sizes, can be adopted to counting various types of nanoparticulates and high rates, it can find applications in numerous particles counting tasks in nanotechnology, life sciences and beyond.https://doi.org/10.1515/nanoph-2022-0612nanoparticle countingnanoparticle imagingsub-rayleigh counting
spellingShingle Chan Eng Aik
Rendón-Barraza Carolina
Wang Benquan
Pu Tanchao
Ou Jun-Yu
Wei Hongxin
Adamo Giorgio
An Bo
Zheludev Nikolay I.
Counting and mapping of subwavelength nanoparticles from a single shot scattering pattern
Nanophotonics
nanoparticle counting
nanoparticle imaging
sub-rayleigh counting
title Counting and mapping of subwavelength nanoparticles from a single shot scattering pattern
title_full Counting and mapping of subwavelength nanoparticles from a single shot scattering pattern
title_fullStr Counting and mapping of subwavelength nanoparticles from a single shot scattering pattern
title_full_unstemmed Counting and mapping of subwavelength nanoparticles from a single shot scattering pattern
title_short Counting and mapping of subwavelength nanoparticles from a single shot scattering pattern
title_sort counting and mapping of subwavelength nanoparticles from a single shot scattering pattern
topic nanoparticle counting
nanoparticle imaging
sub-rayleigh counting
url https://doi.org/10.1515/nanoph-2022-0612
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