Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns
Distinguishing two objects or point sources located closer than the Rayleigh distance is impossible in conventional microscopy. Understandably, the task becomes increasingly harder with a growing number of particles placed in close proximity. It has been recently demonstrated that subwavelength nano...
| Main Authors: | , , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/179708 |
| _version_ | 1826121011628605440 |
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| author | Wang, Benquan An, Ruyi Chan, Eng Aik Adamo, Giorgio So, Jin-Kyu Li, Yewen Shen, Zexiang An, Bo Zheludev, Nikolay I. |
| author2 | School of Physical and Mathematical Sciences |
| author_facet | School of Physical and Mathematical Sciences Wang, Benquan An, Ruyi Chan, Eng Aik Adamo, Giorgio So, Jin-Kyu Li, Yewen Shen, Zexiang An, Bo Zheludev, Nikolay I. |
| author_sort | Wang, Benquan |
| collection | NTU |
| description | Distinguishing two objects or point sources located closer than the Rayleigh distance is impossible in conventional microscopy. Understandably, the task becomes increasingly harder with a growing number of particles placed in close proximity. It has been recently demonstrated that subwavelength nanoparticles in closely packed clusters can be counted by AI-enabled analysis of the diffraction patterns of coherent light scattered by the cluster. Here, we show that deep learning analysis can return the actual positions of nanoparticles in the cluster. The Pearson correlation coefficient between the ground truth and reconstructed positions of nanoparticles exceeds 0.7 for clusters of ten nanoparticles and 0.8 for clusters of two nanoparticles of 0.16λ in diameter, even if they are separated by distances below the Rayleigh resolution limit of 0.68λ, corresponding to a lens with numerical aperture NA = 0.9. |
| first_indexed | 2024-10-01T05:25:47Z |
| format | Journal Article |
| id | ntu-10356/179708 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:25:47Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1797082024-08-19T15:35:07Z Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns Wang, Benquan An, Ruyi Chan, Eng Aik Adamo, Giorgio So, Jin-Kyu Li, Yewen Shen, Zexiang An, Bo Zheludev, Nikolay I. School of Physical and Mathematical Sciences School of Computer Science and Engineering Centre for Disruptive Photonic Technologies (CDPT) The Photonics Institute Physics Light scattered Numerical aperture Distinguishing two objects or point sources located closer than the Rayleigh distance is impossible in conventional microscopy. Understandably, the task becomes increasingly harder with a growing number of particles placed in close proximity. It has been recently demonstrated that subwavelength nanoparticles in closely packed clusters can be counted by AI-enabled analysis of the diffraction patterns of coherent light scattered by the cluster. Here, we show that deep learning analysis can return the actual positions of nanoparticles in the cluster. The Pearson correlation coefficient between the ground truth and reconstructed positions of nanoparticles exceeds 0.7 for clusters of ten nanoparticles and 0.8 for clusters of two nanoparticles of 0.16λ in diameter, even if they are separated by distances below the Rayleigh resolution limit of 0.68λ, corresponding to a lens with numerical aperture NA = 0.9. Ministry of Education (MOE) National Research Foundation (NRF) Published version This work was supported by the Singapore National Research Foundation (Grant No. NRF-CRP23-2019-0006), the Singapore Ministry of Education (Grant No. MOE2016-T3-1-006), and the Engineering and Physical Sciences Research Council UK (Grants No. EP/T02643X/1). 2024-08-19T05:16:48Z 2024-08-19T05:16:48Z 2024 Journal Article Wang, B., An, R., Chan, E. A., Adamo, G., So, J., Li, Y., Shen, Z., An, B. & Zheludev, N. I. (2024). Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns. Applied Physics Letters, 124(15), 151105-. https://dx.doi.org/10.1063/5.0194393 0003-6951 https://hdl.handle.net/10356/179708 10.1063/5.0194393 2-s2.0-85190068584 15 124 151105 en NRF-CRP23-2019-0006 MOE2016-T3-1-006 Applied Physics Letters © 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Physics Light scattered Numerical aperture Wang, Benquan An, Ruyi Chan, Eng Aik Adamo, Giorgio So, Jin-Kyu Li, Yewen Shen, Zexiang An, Bo Zheludev, Nikolay I. Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns |
| title | Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns |
| title_full | Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns |
| title_fullStr | Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns |
| title_full_unstemmed | Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns |
| title_short | Retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns |
| title_sort | retrieving positions of closely packed subwavelength nanoparticles from their diffraction patterns |
| topic | Physics Light scattered Numerical aperture |
| url | https://hdl.handle.net/10356/179708 |
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