A special molecular association is π-π-stacking, driven by weak interactions within aromatic compounds. The π-π-stacking interactions can occur in either a sandwich-like or T-shaped manner. In this study, a method to recognise π-π-stacking from classical molecular dynamics trajectories is developed. By applying three criteria, the method is tested for simple lignin dimer, tetramer and octamer systems, with all G units and β-O4′ linkages. The criteria are geometric and based on distance between ring centroids, the angle between the planes of the two rings and the lateral displacement of the rings. In addition, a wide-angle X-ray scattering (WAXS) profile was calculated from a tetramer system, in agreement with previous experimental results. However, when the WAXS peak assigned to sandwich-shaped stacking was analysed in-depth, it was found to mainly be caused by other intramolecular structural motifs involving e.g. the α-carbon and ring carbons, rather than π-π-stacking. This finding is important for future analyses of WAXS profiles originating from lignin-based materials and shows the strength of combining X-ray scattering methods with molecular modelling.

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.

Properties and functions of materials assembled from nanofibrils critically depend on alignment. A material with aligned nanofibrils is typically stiffer compared with a material with a less anisotropic orientation distribution. In this work, we investigate nanofibril alignment during flow focusing, a flow case used for spinning of filaments from nanofibril dispersions. In particular, we combine experimental measurements and simulations of the flow and fibril alignment to demonstrate how a numerical model can be used to investigate how the flow geometry affects and can be used to tailor the nanofibril alignment and filament shape. The confluence angle between sheath flow and core flow, the aspect ratio of the channel and the contractions in the sheath and/or core flow channels are varied. Successful spinning of stiff filaments requires: (i) detachment of the core flow from the top and bottom channel walls and (ii) a high and homogeneous fibril alignment. Somewhat expected, the results show that the confluence angle has a relatively small effect on alignment compared with contractions. Contractions in the sheath flow channels are seen to be beneficial for detachment, and contractions in the core flow channel are found to be an efficient way to increase and homogenise the degree of alignment.

The complex architecture of wood motivates studies of bioinspired materials that combine strength, toughness, and mechanical integrity. We explore the interplay between nanofiber alignment and molecular interactions in composite filaments formed from cellulose nanofibers (CNFs) and a dendritic polyampholyte, Helux. Helux enhances strength by 60% and increases toughness 5-fold through ionic bonding and thermal covalent cross-linking. However, wide-angle X-ray scattering (WAXS) reveals reduced nanofiber alignment in Helux-containing samples, resulting in a 25% decrease in stiffness-highlighting a trade-off between structural order and cohesion. Polarized optical microscopy (POM) and in situ small-angle X-ray scattering (SAXS) attribute this reduced alignment to enhanced rotary diffusion, driven by carboxylate groups of the Helux. With Helux, multivalent links across the nanofibers give a denser and tougher network with fewer voids. This behavior resembles lignin and hemicellulose interactions in wood, where flexibility and cohesion govern the performance.

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.

The diffusion and interaction dynamics of charged nanoparticles (NPs) within charged polymer networks are crucial for understanding various biological and biomedical applications. Using a combination of coarse-grained molecular dynamics simulations and experimental diffusion studies, we investigate the effects of the NP size, relative surface charge density (ζ), and concentration on the NP permeation length and time. We propose a scaling law for the relative diffusion of NPs with respect to concentration and ζ, highlighting how these factors influence the NP movement within the network. The analyses reveal that concentration and ζ significantly affect NP permeation length and time, with ζ being critical, as critical as concentration. This finding is corroborated by controlled release experiments. Further, we categorize NP dynamics into sticking, sliding, and bouncing regimes, demonstrating how variations in ζ, concentration, and NP size control these behaviors. Through normalized attachment time (NAT) analyses, we elucidate the roles of electrostatic interactions, steric hindrance, and hydrodynamic forces in governing NP dynamics. These insights provide guidance for optimizing NP design in targeted drug delivery and advanced material applications, enhancing our understanding of NP behavior in complex environments.