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

Swelling-based gelation of wet cellulose nanopaper evaluated by single particle tracking

Nanopapers fabricated from cellulose nanofibers (CNFs) drastically swell to form hydrogels when they are in contact with water. This gelation makes contrast with conventional papers that simply deform without drastic volume increase. While the volume increase is qualitatively obvious from the macroscopic visual inspection, its quantitative understanding is nontrivial because of the difficulty in the detection of the boundary between the nanopaper hydrogel and the residual or extra water. In this study, we use single particle tracking (SPT) to reveal the swelling-based gelation phenomenon of cellulose nanopapers. The diffusive dynamics of probe particles uncovers the transient process of swelling, and equilibrium analysis reveals the dependence of volume increase fundamentally dependent on the amount of water to be in contact with the nanopapers. Comparison with the aqueous CNF dispersion without drying reveals the difference in the texture of the nanopaper hydrogels from them.

Statistical genetics concepts in biomass-based materials engineering

With the rise of biomass-based materials such as nanocellulose, there is a growing need to develop statistical methods capable of leveraging inter-dependent experimental data to improve material design, product development, and process optimisation. Statistical approaches are essential given the multifaceted nature of variability in lignocellulosic biomass, which includes a range of different biomass feedstock types, a combinative arrangement of different biomass processing routes, and an array of different product formats depending on the focal application. To account for this large degree of variability and to extract meaningful patterns from research studies, there is a requirement to generate larger datasets of biomass-derived material properties through well-designed experimental systems that enable statistical analysis. To drive this trend, this article proposes the cross-disciplinary utilisation of statistical modelling approaches commonly applied within the field of statistical genetics to evaluate data generated in the field of biomass-based material research and development. The concepts of variance partitioning, heritability, hierarchical clustering, and selection gradients have been explained in their native context of statistical genetics and subsequently applied across the disciplinary boundary to evaluate relationships within a model experimental study involving the production of sorghum-derived cellulose nanofibres and their subsequent fabrication into nanopaper material. Variance partitioning and heritability calculates the relative influence of biomass vs. processing factors on material performance, while hierarchical clustering highlights the obscured similarity between experimental samples or characterisation metrics, and selection gradients elucidate the relationships between characterisation metrics and material quality. Ultimately, these statistical modelling approaches provide more depth to the investigation of biomass-processing-structure-property-performance relationships through outlining a framework for product characterisation, quality evaluation, and data visualisation, not only applicable to nanocellulose production but for all biomass-based materials and products.

Partial structural order of gel-forming material detected as multimodal subdiffusion by logarithmic measure

Fibrous nanomaterials suspended in liquid form gel structures when the binding sites between the components reach sufficient number densities. Cellulose nanofibers (CNFs) are one of such nanomaterials, and transparent papers are fabricated by drying their aqueous dispersions. It is therefore important to characterize the wet state, but the specific fluorescent marker molecules are not available for arbitrary CNFs. We report an approach based on the single particle tracking of Brownian probe particles. We focus on the nonuniformity in the Brownian motion to detect the partial structural order between sol and gel, which is nontrivial to characterize. The simple logarithmic measure of diffusive behavior reveals the multimodal nature of Brownian motion depending on the CNF concentration. The subdiffusive behavior by the overall mean squared displacements alone does not tell whether it is caused by confinement in the local environment by CNFs, or binding to single CNFs possibly diffusing in the dispersion. However, the particle-size dependence clarifies that it is not caused by binding but the confinement effect. Furthermore, the logarithmic measure approach enables the detection of overlapping distributions through their heads rather than tails. The detection of partial structural order by rheological non-uniformity of the system with a simple approach will contribute to the further understanding of gel forming materials in general.

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

Sessile droplets of colloidal dispersions tend to exhibit the coffee-ring phenomenon in the drying process. The suspended particles are transported especially at the final stage of the drying process, which is called the rush hour. Conventional inkjet printers require the ink liquid to have a sufficiently low viscosity for inkjet discharge, but such liquids tend to be subject to the coffee-ring effect. The coffee-ring effect is an issue for conventional printing applications and drawing wires in printed electronics. We show by microscopy movie data analysis based on single particle tracking that the addition of a small amount of cellulose nanofibers (CNFs) to the colloidal dispersion works in such a way that the initial low concentration satisfies the low viscosity requirement, and the three-dimensional structural order of the CNFs formed during the final stage of droplet drying owing to the high concentration hinders the transport of particles to the periphery, suppressing the coffee-ring effect. This is a spatio-temporally controlled process that makes use of the inherent process of ordinary ink printing situations by the simple protocol. This is also an approach to seamlessly link the ink and substrate since CNFs are regarded as a promising substrate material for flexible devices in printed electronics because of their fine texture that keeps conductive nanoparticles on the surface.

Fundamentals of the logarithmic measure for revealing multimodal diffusion

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.

Spatiotemporal Dynamics of Laser-Induced Molecular Crystal Precursors Visualized by Particle Image Diffusometry

We have succeeded in label-free visualization of spatiotemporal dynamics of laser-induced crystal precursors in aqueous solutions. The tracking-free evaluation of the diffusion-coefficient field for the observation domain with tens of micrometers on a side from microscopy movie data is realized by particle image diffusometry (PID). PID revealed the time fluctuation of coverage composition with the nonuniform space distribution of diffusion coefficients by the prenucleation clusters. Furthermore, the results indicate the existence of a loose aggregation domain of prenucleation clusters where the order of viscosity corresponds to that of honey.

Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels

Native mucus is polymer-based soft-matter material of paramount biological importance. How non-Gaussian and non-ergodic is the diffusive spreading of pathogens in mucus? We study the passive, thermally driven motion of micron-sized tracers in hydrogels of mucins, the main polymeric component of mucus. We report the results of the Bayesian analysis for ranking several diffusion models for a set of tracer trajectories [C. E. Wagner et al., Biomacromolecules, 2017, 18, 3654]. The models with "diffusing diffusivity", fractional and standard Brownian motion are used. The likelihood functions and evidences of each model are computed, ranking the significance of each model for individual traces. We find that viscoelastic anomalous diffusion is often most probable, followed by Brownian motion, while the model with a diffusing diffusion coefficient is only realised rarely. Our analysis also clarifies the distribution of time-averaged displacements, correlations of scaling exponents and diffusion coefficients, and the degree of non-Gaussianity of displacements at varying pH levels. Weak ergodicity breaking is also quantified. We conclude that-consistent with the original study-diffusion of tracers in the mucin gels is most non-Gaussian and non-ergodic at low pH that corresponds to the most heterogeneous networks. Using the Bayesian approach with the nested-sampling algorithm, together with the quantitative analysis of multiple statistical measures, we report new insights into possible physical mechanisms of diffusion in mucin gels.