Cellulose Nanofibrils (CNFs), highly present in nature, can be used as building blocks for future sustainable materials, including strong and stiff filaments. A rheo-optical flow-stop technique is used to conduct experiments to characterize the CNFs by studying Brownian dynamics through the CNFs' birefringence decay after stop. As the experiments produce large quantities of data, we reduce their dimensionality using Principal Component Analysis (PCA) and exploit the possibility of visualizing the reduced data in two ways. First, we plot the principal components (PCs) as time series, and by training LSTM networks assigned for each PC time series with the data before the flow stop, we predict the behavior after the flow stop (Bragone et al., 2024). Second, we plot the first PCs against each other to create clusters that give information about the different CNF materials and concentrations. Our approach aims at classifying the CNF materials to varying concentrations by applying unsupervised machine learning algorithms, such as k-means and Gaussian Mixture Models (GMMs). Finally, we analyze the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) of the first principal component, detecting seasonality in lower concentrations.

Hypothesis: Charge-stabilized colloidal cellulose nanocrystals (CNCs) can self-assemble into higher-ordered chiral nematic structures by varying the volume fraction. The assembly process exhibits distinct dynamics during the isotropic to liquid crystal phase transition, which can be elucidated using X-ray photon correlation spectroscopy (XPCS). Experiments: Anionic CNCs were dispersed in propylene glycol (PG) and water spanning a range of volume fractions, encompassing several phase transitions. Coupled with traditional characterization techniques, XPCS was conducted to monitor the dynamic evolution of the different phases. Additionally, simulated XPCS results were obtained using colloidal rods and compared with the experimental data, offering additional insights into the dynamic behavior of the system. Findings: The results indicate that the particle dynamics of CNCs undergo a stepped decay in three stages during the self-assembly process in PG, coinciding with the observed phases. The phase transitions are associated with a total drop of Brownian diffusion rates by four orders of magnitude, a decrease of more than a thousand times slower than expected in an ideal system of repulsive Brownian rods. Given the similarity in the phase behaviors in CNCs dispersed in PG and in water, we hypothesize that these dynamic behaviors can be extrapolated to other polar solvent environments. Importantly, these findings represent the direct measurement of CNC dynamics using XPCS, underscoring the feasibility of directly assessing the dynamic behavior of other rod-like colloidal suspensions.

The ForMAX beamline at the MAX IV Laboratory provides multiscale and multimodal structural characterization of hierarchical materials in the nanometre to millimetre range by combining small- and wide-angle X-ray scattering with full-field microtomography. The modular design of the beamline is optimized for easy switching between different experimental modalities. The beamline has a special focus on the development of novel fibrous materials from forest resources, but it is also well suited for studies within, for example, food science and biomedical research.

In many industrial applications, nonwoven fibre networks are facilitated to operate under partly saturated conditions, allowing for filtration, liquid absorption and liquid transport. Resolving the governing liquid distribution in loaded polyamide-6 (PA6) fibre networks using X-ray computed micro-tomography is a challenge due to the similar X-ray attenuation coefficients of water and PA6 and limitations in using background subtraction techniques if the network is deformed, which will be the case if subjected to compression. In this work, we developed a method using a potassium iodide solution in water to enhance the liquid’s attenuation coefficient without modifying the water’s rheological properties. Therefore, we studied the evolving liquid distribution in loaded and partly saturated PA6 fibre networks on the microscale. Increasing the external load applied to the network, we observed an exponential decrease in air content while the liquid content was constant, increasing the overall saturation with increasing network strain. Furthermore, the microstructural properties created by the punch-needle process in the manufacturing of the network significantly influenced the out-of-plane liquid distribution. The method has been proven helpful in understanding the results of adaptions in both the fibre network design and manufacturing process, allowing for investigating the resulting liquid distribution on a microscale.

Flow-assisted alignment of anisotropic nanoparticles is a promising route for the bottom-up assembly of advanced materials with tunable properties. While aligning processes could be optimized by controlling factors such as solvent viscosity, flow deformation, and the structure of the particles themselves, it is necessary to understand the relationship between these factors and their effect on the final orientation. In this study, we investigated the flow of surface-charged cellulose nanocrystals (CNCs) with the shape of a rigid rod dispersed in water and propylene glycol (PG) in an isotropic tactoid state. In situ scanning small-angle X-ray scattering (SAXS) and rheo-optical flow-stop experiments were used to quantify the dynamics, orientation, and structure of the assigned system at the nanometer scale. The effects of both shear and extensional flow fields were revealed in a single experiment by using a flow-focusing channel geometry, which was used as a model flow for nanomaterial assembly. Due to the higher solvent viscosity, CNCs in PG showed much slower Brownian dynamics than CNCs in water and thus could be aligned at lower deformation rates. Moreover, CNCs in PG also formed a characteristic tactoid structure but with less ordering than CNCs in water owing to weaker electrostatic interactions. The results indicate that CNCs in water stay assembled in the mesoscale structure at moderate deformation rates but are broken up at higher flow rates, enhancing rotary diffusion and leading to lower overall alignment. Albeit being a study of cellulose nanoparticles, the fundamental interplay between imposed flow fields, Brownian motion, and electrostatic interactions likely apply to many other anisotropic colloidal systems.

As the quest for more sustainable and environmentally friendly materials has increased in the last decades, cellulose nanofibrils (CNFs), abundant in nature, have proven their capabilities as building blocks to create strong and stiff filaments. Experiments have been conducted to characterize CNFs with a rheo-optical flow-stop technique to study the Brownian dynamics through the CNFs’ birefringence decay after stop. This paper aims to predict the initial relaxation of birefringence using Principal Component Analysis (PCA) and Long Short-Term Memory (LSTM) networks. By reducing the dimensionality of the data frame features, we can plot the principal components (PCs) that retain most of the information and treat them as time series. We employ LSTM by training with the data before the flow stops and predicting the behavior afterward. Consequently, we reconstruct the data frames from the obtained predictions and compare them to the original data.

Biopolymer network dynamics play a significant role in both biological and materials science. This study focuses on the dynamics of cellulose nanofibers as a model system given their relevance to biology and nanotechnology applications. Using large-scale coarse-grained simulations with a lattice Boltzmann fluid coupling, we investigated the reptation behavior of individual nanofibers within entangled networks. Our analysis yields essential insights, proposing a scaling law for rotational diffusion, quantifying effective tube diameter, and revealing release mechanisms during reptation, spanning from rigid to semiflexible nanofibers. Additionally, we examine the onset of entanglement in relation to the nanofiber flexibility within the network. Microrheology analysis is conducted to assess macroscopic viscoelastic behavior. Importantly, our results align closely with previous experiments, validating the proposed scaling laws, effective tube diameters, and onset of entanglement. The findings provide an improved fundamental understanding of biopolymer network dynamics and guide the design of processes for advanced biobased materials. 

We demonstrate a framework of interpreting data from x-ray photon correlation spectroscopy experiments with the aid of numerical simulations to describe nanoscale dynamics in soft matter. This is exemplified with the transport of passive tracer gold nanoparticles in networks of charge-stabilized cellulose nanofibers. The main structure of dynamic modes in reciprocal space could be replicated with a simulated system of confined Brownian motion, a digital twin, allowing for a direct measurement of important effective material properties describing the local environment of the tracers. 

The nanostructure, primarily particle orientation, controls mechanical and functional (e.g., mouthfeel, cell compatibility, optical, morphing) properties when macroscopic materials are assembled from nanofibrils. Understanding and controlling the nanostructure is therefore an important key for the continued development of nanotechnology. We merge recent developments in the assembly of biological nanofibrils, X-ray diffraction orientation measurements, and computational fluid dynamics of complex flows. The result is a digital twin, which reveals the complete particle orientation in complex and transient flow situations, in particular the local alignment and spatial variation of the orientation distributions of different length fractions, both along the process and over a specific cross section. The methodology forms a necessary foundation for analysis and optimization of assembly involving anisotropic particles. Furthermore, it provides a bridge between advanced in operandi measurements of nanostructures and phenomena such as transitions between liquid crystal states and in silico studies of particle interactions and agglomeration.