Machine learning enables precise holographic characterization of colloidal materials in real time

Altman, Lauren E; Grier, David G.

doi:10.1039/d2sm01283a

Cited by 5 publications

(3 citation statements)

References 77 publications

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“…and Grier, D.G. et al [ 20 , 21 ] devised a 3D particle tracking network rooted in YoLo, capable of achieving high-precision particle localization. Shao, S. et al [ 22 ] designed a convolutional neural network following the U-net architecture to facilitate particle localization.…”

Section: Introductionmentioning

confidence: 99%

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

Luo,

Zhang,

Tan

et al. 2024

Sensors

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In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a z-axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.

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Section: Introductionmentioning

confidence: 99%

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

Luo,

Zhang,

Tan

et al. 2024

Sensors

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“…15 In the context of microscopy, images of scattering patterns have been employed for simultaneous estimation of size and refractive index of particles with diameters down to about half the wavelength of light. 16,17 This lower size limit originates from the difficulty of accurately relating a measured scattering image to the particle size for particles near the diffraction limit.…”

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confidence: 99%

“…In addition to the scattering amplitude, the angular distribution of light scattering also contains information about particle size and morphology. − This forms the basis of particle characterization using multiangle light scattering (MALS) and scattering-based flow cytometry . In the context of microscopy, images of scattering patterns have been employed for simultaneous estimation of size and refractive index of particles with diameters down to about half the wavelength of light. , This lower size limit originates from the difficulty of accurately relating a measured scattering image to the particle size for particles near the diffraction limit.…”

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confidence: 99%

Dual-Angle Interferometric Scattering Microscopy for Optical Multiparametric Particle Characterization

Olsén,

García Rodríguez,

Skärberg

et al. 2024

Nano Lett.

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Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles to be in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles (radius <170 nm) without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that the hydrodynamic radius relates to the outer particle radius, while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single-particle level.

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Convolutional neural networks applied to differential dynamic microscopy reduces noise when quantifying heterogeneous dynamics

Martinez,

Siu,

Dang

et al. 2024

Soft Matter

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Machine learning enables precise holographic characterization of colloidal materials in real time

Abstract: Holographic particle characterization uses in-line holographic video microscopy to track and characterize individual colloidal particles dispersed in their native fluid media. Applications range from fundamental research in statistical physics to...

Cited by 5 publications

References 77 publications

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

Dual-Angle Interferometric Scattering Microscopy for Optical Multiparametric Particle Characterization

Convolutional neural networks applied to differential dynamic microscopy reduces noise when quantifying heterogeneous dynamics

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