Characterization of nanoparticle size distributions using a microfluidic device with integrated optical microcavities

Malmir, Kiana; Okell, William; Trichet, A. A. P.; Smith, Jason M.

doi:10.1039/d2lc00180b

Cited by 7 publications

(2 citation statements)

References 44 publications

(87 reference statements)

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“…Optical microcavities are important optical components, with dimensions reaching the level of a few micrometers or even nanometers. [7][8][9] Micro-resonators often have high Q values to confine photons into micro-nano scales, which have been rapidly developed in fields such as low-threshold lasers, 10 and sensors. 11 Liquid crystals (LC) are unique functional materials with liquid fluidity and crystalline birefringence.…”

Section: Introductionmentioning

confidence: 99%

Laser mode control based on chiral liquid crystal microcavities

Liu,

Zhang,

et al. 2024

J. Mater. Chem. C

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Section: Introductionmentioning

confidence: 99%

Laser mode control based on chiral liquid crystal microcavities

Liu,

Zhang,

et al. 2024

J. Mater. Chem. C

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“…[4][5][6] Particle characterization with on-line optical method has attracted attention, in which the most used yet roughest parameter is particle size. [7][8][9] However, the other properties such as surface morphology and specie of a particle usually play a more important role in many situations. [10][11][12] For example, in water quality monitoring, the microalgae named diatom is usually used as the bioindicator of pollution, instead of other microplastic particles of the same size.…”

Section: Introductionmentioning

confidence: 99%

Moment‐Based Shape‐Learning Holography for Fast Classification of Microparticles

Luo

Jiao

et al. 2023

Advanced Photonics Research

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Compact computational in‐line holography based on deep learning is an attractive single‐shot approach to image microparticles dispersed in 3D volume. The particle shape contains valuable information for species classification, but the dataset acquisition process for network training suffers from labor consuming and low efficiency due to the complex and varied 2D shapes of different particle species. Herein, a moment‐based shape‐learning holography (MSLH) is proposed where the shape of a microparticle is mathematically characterized using varying weights of Zernike moments. By decomposing and recombining feature shapes of pollen particles into numerous new characteristic shapes as incremental data, MSLH improves the efficiency of dataset preparation. The depth‐encoded shape‐learning is achieved using a U‐net with self‐attention mechanism, which enables fast axial depth determination. The shape reconstruction uses a wavelet‐based method with more explicit physical meanings, making MSLH a hybrid data‐and‐model driven approach that requires fewer primary data. Validation results show that MSLH achieves high accuracy in axial position and shape reconstruction, while maintaining good classification effectiveness. It is believed that MSLH is an easy‐to‐setup, efficient‐to‐construct, and fast‐to‐output approach for shape‐based classifications of 3D distributed microparticles in dynamic fluid.

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