Holographic characterization of colloidal fractal aggregates

Wang, Chen; Cheong, Fook Chiong; Ruffner, David B.; Zhong, Xiao Yan; Ward, Michael D.; Grier, David G.

doi:10.1039/c6sm01790h

Cited by 33 publications

(50 citation statements)

References 42 publications

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“…In the experiments of Wang et al, the aggregates tumble as they flow through a microfluidic channel [10]. Each aggregate is typically imaged in several orientations, and the resulting values of n ef f and a ef f are averaged together.…”

Section: Orientation Dependence Of N Ef F and A Ef Fmentioning

confidence: 99%

Computational assessment of an effective-sphere model for characterizing colloidal fractal aggregates with holographic microscopy

Fung

Hoang

2019

Journal of Quantitative Spectroscopy and Radiative Transfer

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We perform simulations to evaluate a recent experimental technique for using in-line holographic microscopy and an effective-sphere model to measure the population-averaged fractal dimension D f of an ensemble of colloidal fractal aggregates. In this technique, models based on Lorenz-Mie scattering by a uniform sphere are fit to digital holograms of a population of fractal aggregates to determine the effective refractive indices n ef f and effective radii a ef f of the aggregates. A scaling relationship between n ef f and a ef f based on the Maxwell Garnett effective-medium theory then determines D f . Here we use a multisphere superposition code to calculate the exact holograms produced by aggregates with tunable fractal dimensions D f . We show that n ef f and a ef f become less sensitive to the aggregate orientation as D f increases. We also show that the Maxwell Garnett scaling relationship correctly determines D f to within 10.5% when multiple scattering is negligible and the population-averaged coefficient of determination R 2 p > 0.6, indicating that the holograms are well-described by the effective-sphere model.

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Section: Orientation Dependence Of N Ef F and A Ef Fmentioning

confidence: 99%

Computational assessment of an effective-sphere model for characterizing colloidal fractal aggregates with holographic microscopy

Fung

Hoang

2019

Journal of Quantitative Spectroscopy and Radiative Transfer

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“…(4) to experimentally measured holograms. To do so, each video frame must first be corrected by subtracting off the camera's dark count [8], and then normalizing by the microscope's background intensity distribution [1]. Such fits typically yield a sphere's position with a precision of 1 nm in the plane and 3 nm axially [20,35].…”

Section: Holographic Image Formationmentioning

confidence: 99%

“…When applied to a stream of dispersed particles, holographic characterization measurements provide insights into the joint distribution of particle size and composition that cannot be obtained in any other way. This technique has been demonstrated on both homogeneous and heterogeneous [2,3] dispersions of colloidal spheres, and has been extended to work for colloidal clusters [4][5][6], and aggregates [7,8], as well as colloidal rods [9] and other aspherical particles [10,11]. Applications include monitoring protein aggregation in biopharmaceuticals [7], detecting agglomeration in semiconductor polishing slurries [12], gauging the progress of colloidal synthesis reactions [13,14], performing microrheology [15], microrefractometry [16], and microporosimetry [17] measurements, assessing the quality of dairy products [18], and monitoring contaminants in wastewater [3].…”

Section: Introduction: Holographic Particle Characterizationmentioning

confidence: 99%

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

Hannel¹,

Abdulali²,

O'Brien³

et al. 2018

Opt. Express

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Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive indexes. Extracting this wealth of information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques. We demonstrate that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN), can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques. Our CNN implementation localizes holographic features precisely enough to bootstrap more detailed analyses based on the Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade proves to be less precise, but is so computationally efficient that it creates new opportunities for applications that emphasize speed and low cost. We demonstrate its use as a real-time targeting system for holographic optical trapping.

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“…Deep learning, which is an approach based on the application of neural networks (NNs) [39][40][41], has already enabled advances in imaging [42,43] and enabled automated classification of objects in images [44,45], such as label-free cell classification [46], as well as object classification through scattering media [47][48][49] and through scattering pattern imaging [50,51]. Using NNs to determine particle size and refractive index from their scattering pattern was proposed by [52] and has been subsequently demonstrated experimentally on colloidal spherical particles [53][54][55][56], showing that NNs can bypass the need to develop complex modelling [57]. Moreover, the ability to update a NN [58], for example to monitor additional particles without the need to physically change a sensor, makes such an approach particularly desirable, especially when implemented on a micro-computer, such as a Raspberry Pi [59,60].…”

Section: Introductionmentioning

confidence: 99%

Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi

Grant-Jacob

Xie

MacKay

et al. 2019

Environ. Res. Commun.

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The identification of mixtures of particles in a solution via analysis of scattered light can be a complex task, due to the multiple scattering effects between different sizes and types of particles. Deep learning offers the capability for solving complex problems without the need for a physical understanding of the underlying system, and hence offers an elegant solution. Here, we demonstrate the application of convolutional neural networks for the identification of the concentration of microparticles (silicon dioxide and melamine resin) and the solution salinity, directly from the scattered light. The measurements were carried out in real-time using a Raspberry Pi, light source, camera, and neural network computation, hence demonstrating a portable and low-cost environmental marine sensor.

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Holographic characterization of colloidal fractal aggregates

Cited by 33 publications

References 42 publications

Computational assessment of an effective-sphere model for characterizing colloidal fractal aggregates with holographic microscopy

Computational assessment of an effective-sphere model for characterizing colloidal fractal aggregates with holographic microscopy

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi

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