# Characterizing the Orientational and Network Dynamics of Polydisperse Nanofibres at the Nanoscale

## TL;DR

This paper introduces a novel microfluidic birefringence method to study the rotational and network dynamics of polydisperse nanofibre suspensions, revealing how fibre properties influence network formation and rigidity.

## Contribution

It presents a new experimental approach to analyze length-dependent rotational dynamics and orientation distributions of nanoscale fibres, addressing limitations of previous fixed-length assumptions.

## Key findings

- Characterized transition from rotational mobility to rigidity at entanglement thresholds.
- Demonstrated effects of fibre length and orientation on network dynamics.
- Revealed interplay between Brownian motion and nanoparticle alignment.

## Abstract

Polydisperse fibre networks are the basis of many natural and man-made architectures, ranging from high-performance bio-based materials to components of living cells and tissues. The formation and persistence of such networks are given by fibre properties such as length and stiffness as well as the number density and fibre-fibre interactions. Studies of fibre network behavior, such as connectivity or rigidity thresholds, typically assume fixed fibre length and isotropic fibre orientation distributions, specifically for nanoscale fibres where the methods providing time-resolved measurements are limited. Using birefringence measurements in a microfluidic flow-focusing channel combined with a flow-stop procedure, we here propose a methodology allowing investigations of length dependent rotational dynamics of nanoscale polydisperse fibre suspensions, including effects of non-isotropic orientation distributions. Transition from rotational mobility to rigidity at entanglement thresholds is specifically addressed for a number of nanocellulose suspensions, which are used as model nanofibre systems. The results show that the proposed method allows characterization of the subtle interplay between Brownian diffusion and nanoparticle alignment on network dynamics.