Unsupervised Learning Analysis of Flow-Induced Birefringence in Nanocellulose: Differentiating Materials and Concentrations

• Cellulose nanofibrils (CNFs) can be used as building blocks for future sustainable materials including strong and stiff filaments. The goal of this paper is to introduce a data analysis of flow-induced birefringence experiments by means of unsupervised learning techniques. By reducing the dimensionality of the data with Principal Component Analysis (PCA) we are able to exploit information for the different cellulose materials at several concentrations and compare them to each other. Our approach aims at classifying the CNF materials at different concentrations by applying unsupervised machine learning algorithms, like 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. The focus is given to the initial relaxation of birefringence after the flow is stopped to have a better understanding of the Brownian dynamics for the given materials and concentrations.Our method can be used to distinguish the different materials at specific concentrations and could help to identify possible advantages and drawbacks of one material over the other. 

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Keyword

Unsupervised Learning

Cellulose Nanofibrils

k-means

Gaussian Mixture Models

Principal Component Analysis

Publication and Content Type

vet (subject category)

ovr (subject category)