Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning.

Multiple linear regression models were trained to predict the degree of substitution (DS) of cellulose acetate based on raw infrared (IR) spectroscopic data. A repeated k-fold cross validation ensured unbiased assessment of model accuracy. Using the DS obtained from H NMR data as reference, the machine learning model achieved a mean absolute error (MAE) of 0.

Structure-Property Relationships of Short Chain (Mixed) Cellulose Esters Synthesized in a DMSO/TMG/CO Switchable Solvent System.

Increasing environmental pollution and petroleum resource depletion are important indicators for the necessary and inevitable replacement of fossil-based polymeric materials with more sustainable counterparts. Hence, the development of bio-based materials from renewable resources, such as cellulose, is of great importance. Herein, we introduce a rapid and homogeneous microwave assisted synthesis of high molecular weight (59 kDa ≤ ≤ 116 kDa) short chain (mixed) cellulose esters (CEs) with variable acyl side chain length (2 ≤ ≤ 8) by using a DMSO/TMG/CO switchable solvent system.