Characterization of aqueous cellulose nanofiber dispersions from microscopy movie data of Brownian particles by trajectory analysis

Motohashi, Reiji; Hanasaki, Itsuo

doi:10.1039/c8na00214b

Cited by 15 publications

(21 citation statements)

References 64 publications

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“…Still other works show similar behaviour of the diffusion coefficient, e.g. nanoparticle diffusion in electrolytic solutions for batteries [73,74], protein diffusion in membranes [75], nanoparticle diffusion in volatile liquids [76] and aqueous cellulose nanofibers diffusion [77]. This substantiates the use of the chemical potential for nanoparticle/colloidal suspensions/dispersions proposed in this work.…”

Section: Application To Mass Diffusion Of a Nanoparticle Suspensionsupporting

confidence: 87%

On the chemical potential of nanoparticle dispersion

Machrafi

2020

Physics Letters A

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Section: Application To Mass Diffusion Of a Nanoparticle Suspensionsupporting

confidence: 87%

On the chemical potential of nanoparticle dispersion

Machrafi

2020

Physics Letters A

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“…We have already established the approach for revealing the basic characteristics of the surrounding media to the particles through trajectories obtained by single particle tracking (SPT). 29 The stark contrast with our previous study, 27 originating from this difference of approach, is that this study reveals the mesoscopic state of CNF dispersion without flow. In this study, we intermittently capture the movie data in the droplet drying process, and uncover the spatio-temporally controlled process that is highly advantageous for implementation in inkjet printing technologies.…”

Section: Introductioncontrasting

confidence: 88%

“…The first step of the data analysis was single particle tracking (SPT) [29][30][31][32] to obtain the trajectory data sets. The tracking algorithm was that of Sbalzarini and Koumoutsakos.…”

Section: Discussionmentioning

confidence: 99%

“…The specific values of the parameters are as follows: radius, 4 px; intensity percentile, 0.1%; cutoff score, 0; link range, 1; and displacement threshold, 2 px. We have shown in our previous study 29 that the SPT approach reveals the microscopic characteristics of the CNF dispersion beyond the effective viscosity and provides information including the confinement effect by CNFs. The generalized diffusion coefficient 34 is useful for the elucidation of superdiffusive and subdiffusive characteristics: 35 h|r(t) À r(0…”

Section: Discussionmentioning

confidence: 99%

“…The scaling exponent a = 1 indicates normal diffusion, and a o 1 and a 4 1 indicate the subdiffusive and superdiffusive behaviors, respectively. The generalized diffusion coefficient D a and the scaling exponent a were evaluated from the finite amount of data in the frame-based (FB) manner: 29 1…”

Section: Discussionmentioning

confidence: 99%

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Spatio-temporally controlled suppression of the coffee-ring phenomenon by cellulose nanofibers

Koyama

Hanasaki

2021

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Fundamentals of the logarithmic measure for revealing multimodal diffusion

Dalton

Sbalzarini

Hanasaki

2021

Biophysical Journal

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We develop a theoretical foundation for a time-series analysis method suitable for revealing the spectrum of diffusion coefficients in mixed Brownian systems, for which no prior knowledge of particle distinction is required. This method is directly relevant for particle tracking in biological systems, in which diffusion processes are often nonuniform. We transform Brownian data onto the logarithmic domain, in which the coefficients for individual modes of diffusion appear as distinct spectral peaks in the probability density. We refer to the method as the logarithmic measure of diffusion, or simply as the logarithmic measure. We provide a general protocol for deriving analytical expressions for the probability densities on the logarithmic domain. The protocol is applicable for any number of spatial dimensions with any number of diffusive states. The analytical form can be fitted to data to reveal multiple diffusive modes. We validate the theoretical distributions and benchmark the accuracy and sensitivity of the method by extracting multimodal diffusion coefficients from two-dimensional Brownian simulations of polydisperse filament bundles. Bundling the filaments allows us to control the system nonuniformity and hence quantify the sensitivity of the method. By exploiting the anisotropy of the simulated filaments, we generalize the logarithmic measure to rotational diffusion. By fitting the analytical forms to simulation data, we confirm the method's theoretical foundation. An error analysis in the single-mode regime shows that the proposed method is comparable in accuracy to the standard mean-squared displacement approach for evaluating diffusion coefficients. For the case of multimodal diffusion, we compare the logarithmic measure against other, more sophisticated methods, showing that both model selectivity and extraction accuracy are comparable for small data sets. Therefore, we suggest that the logarithmic measure, as a method for multimodal diffusion coefficient extraction, is ideally suited for small data sets, a condition often confronted in the experimental context. Finally, we critically discuss the proposed benefits of the method and its information content.

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Characterization of aqueous cellulose nanofiber dispersions from microscopy movie data of Brownian particles by trajectory analysis

Abstract: Cellulose nanofibers (CNFs) are not directly visible by microscopes when dispersed in water, but the dynamical characteristics of suspended colloidal particles tells us the texture as well as the rheological properties of CNF dispersions beyond the viscosity.

Cited by 15 publications

References 64 publications

On the chemical potential of nanoparticle dispersion

On the chemical potential of nanoparticle dispersion

Spatio-temporally controlled suppression of the coffee-ring phenomenon by cellulose nanofibers

Fundamentals of the logarithmic measure for revealing multimodal diffusion

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