Maximizing production of cellulose nanocrystals and nanofibers from pre-extracted loblolly pine kraft pulp: a response surface approach

Abstract

AbstractThis study aims to optimize strong acid hydrolysis-based production of cellulose nanocrystals (CNCs) and cellulose nanofibers (CNFs) from pre-extracted and fully bleached kraft pulp of loblolly pinewood, the most abundant and commercially significant softwood species in southeastern United States. The effect of four parameters, including acid concentration, temperature, duration and pulp particle size, on the yield and properties of CNCs was investigated using the central composite design (CCD) of response surface methodology (RSM) for process optimization. While CNC yield was significantly affected by acid concentration and hydrolysis temperature and was adequately explained by an empirical model, none of the characteristic properties of CNCs, including crystallinity index, surface charge and particle size, displayed any strong correlation to the process parameters within the experimental ranges tested. At different hydrolysis severities, we not only analyzed the waste streams to determine the extent of holocellulose degradation, but also evaluated the properties of leftover partially hydrolyzed pulp, called cellulosic solid residues (CSR), to gauge its potential for CNF production via mechanical fibrillation. Conditions that maximized CNC yields (60% w/w) were 60% acid concentration, 58 °C, 60 min and 40 mesh particle size. Twenty percent (w/w) of the pulp was degraded under these conditions. On the other hand, conditions that maximized CSR yields (60% w/w) were 54% acid, 45 °C, 90 min and 20 mesh particle size, which also produced 15% CNCs, caused minimal pulp degradation (< 5%) and imparted sufficient surface charge such that CSR was easily microfluidized into CNFs. Therefore, the strong acid hydrolysis process could be tuned to maximize the production of cellulose nanocrystals and nanofibers and obtain two products with different properties and applications through the process optimization.