Enhanced Morphological Characterization of Cellulose Nano/Microfibers through Image Skeleton Analysis.

The present paper proposes a novel approach for the morphological characterization of cellulose nano and microfibers suspensions (CMF/CNFs) based on the analysis of eroded CMF/CNF microscopy images. This approach offers a detailed morphological characterization and quantification of the micro and nanofibers networks present in the product, which allows the mode of fibrillation associated to the different CMF/CNF extraction conditions to be discerned. This information is needed to control CMF/CNF quality during industrial production. Five cellulose raw materials, from wood and non-wood sources, were subjected to mechanical, enzymatic, and (2,2,6,6-Tetramethylpiperidin-1-yl)oxyl (TEMPO)-mediated oxidative pre-treatments followed by different homogenization sequences to obtain products of different morphologies. Skeleton analysis of microscopy images provided in-depth morphological information of CMF/CNFs that, complemented with aspect ratio information, estimated from gel point data, allowed the quantification of: (i) fibers peeling after mechanical pretreatment; (ii) fibers shortening induced by enzymes, and (iii) CMF/CNF entanglement from TEMPO-mediated oxidation. Being mostly based on optical microscopy and image analysis, the present method is easy to implement at industrial scale as a tool to monitor and control CMF/CNF quality and homogeneity.

In-depth characterization of the aggregation state of cellulose nanocrystals through analysis of transmission electron microscopy images.

Dispersion of cellulose nanocrystals (CNCs) is of utmost importance to guarantee their reliable application. Nevertheless, there is still no consensual method to characterize CNC aggregation. The hypothesis of this paper is that dispersion could be quantified through the classification of aggregates detected in transmission electron microscopy images. k-Means was used to classify image particulate elements of five CNC samples into groups according to their geometric features. Particles were classified into five groups according to their maximum Feret diameter, elongation, circularity and area. Two groups encompassed the most application-critical aggregates: one integrated aggregates of high complexity and low compactness while the other included elongated aggregates. In addition, the characterization of CNC dispersion after different levels of sonication was achieved by assessing the change in the number of elements belonging to each cluster after sonication. This approach could be used as a standard for the characterization of the aggregation state of CNCs.

Microalgae harvesting with the novel flocculant hairy cationic nanocrystalline cellulose.

This paper investigates the flocculation of Chlorella sorokiniana suspensions with a novel cellulose derivative, namely hairy cationic nanocrystalline cellulose (CNCC). CNCC are a brand new family of nanocellulose characterized by having two positively charged amorphous ends joint through a common crystalline shaft. Flocculation was monitored through laser reflectance and its mechanism was studied by means of zeta potential, fractal dimension and turbidity removal. CNCC dosage and shear rate were varied and their effect on floc morphology and filterability were assessed. CNCC effectively flocculated the cultures at dosages well below and over the isoelectric point, being the flocculation mechanisms and floc strength highly dependent on the doses applied. The filtration propensity of flocculated suspensions proved highly sensitive to small differences in flocs' geometry. The aggregation process entailed two phases, a first one in which the CNCC adsorbed on the surface of microalgal cells according to a flat random deposition up to reaching a maximal cell coverage, and a second one in which the free spots left were progressively covered with orthogonally deposited CNCC, being this later configuration the main responsible for intercellular attachment. The present work demonstrates that CNCC is an effective flocculant of microalgal cell suspensions and constitutes an alternative worth exploring for the aggregation of other cells' suspensions.

Hairy cationic nanocrystalline cellulose as a novel flocculant of clay.

HYPOTHESIS: The present paper investigates, for the first time, the potential of cationic hairy cellulose nanocrystals (CNCC) to induce the flocculation of a model suspension of kaolinite. CNCC belong to a brand new family of nanocelluloses characterized for presenting a crystalline rod-like body and functionalized amorphous chains at both ends. Given that these chains can be easily tuned, these nanocelluloses present a high potential as fit-to-purpose flocculants. EXPERIMENTS: CNCC were produced through periodate oxidation, cationization and thermal treatment of cellulose. Flocculation was monitored by both photometric dispersion analysis and laser reflectance. Flocs were characterized by the determination of zeta potential, supernatant turbidity removal and optical microscopy. A recently developed machine learning random forest regression model was used to estimate fractal dimension (Df) from chord length distribution data. FINDINGS: Although a high efficiency was achieved for CNCC dosages between 7.5 and 75 mg/g, the maximum floc size and the fastest flocculation were found near the isoelectric point (10-30 mg/g). Thus, CNCC acted through charge neutralization mechanism. The model used to estimate flocs Df was found very successful to describe the flocculation process. The clay/CNCC flocs Df values suggest a relation between floc conformation and CNCC dosage, presenting an opener structure when closer to the isoelectric point.

Laser reflectance measurement for the online monitoring of Chlorella sorokiniana biomass concentration.

Fast and reliable methods to determine biomass concentration are necessary to facilitate the large scale production of microalgae. A method for the rapid estimation of Chlorella sorokiniana biomass concentration was developed. The method translates the suspension particle size spectrum gathered though laser reflectance into biomass concentration by means of two machine learning modelling techniques. In each case, the model hyper-parameters were selected applying a simulated annealing algorithm. The results show that dry biomass concentration can be estimated with a very good accuracy (R2=0.87). The presented method seems to be suited to perform fast estimations of biomass concentration in suspensions of microalgae cultivated in moderately turbid media with tendency to aggregate.

Estimation of Chlamydomonas reinhardtii biomass concentration from chord length distribution data.

A novel method to estimate the concentration of Chlamydomonas reinhardtii biomass was developed. The method employs the chord length distribution information gathered by means of a focused beam reflectance probe immersed in the culture sample and processes the data through a feedforward multilayer perceptron. The multilayer perceptron architecture was systematically optimised through the application of a simulated annealing algorithm. The method developed can predict the concentration of microalgae with acceptable accuracy and, with further development, it could be implemented online to monitor the aggregation status and biomass concentration of microalgal cultures.