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Advanced nanocellulose materials prepared through interfacial electrostatic complexation


Interest in producing materials from biomass to develop a sustainable future with low environmental impact is growing fast. However, more eco-friendly materials face challenges related to their intrinsic characteristics, which differ substantially from those of oil-based plastics, in terms of mechanical properties and wet resilience. Supramolecular assemblies of bio-based components in aqueous media are a promising strategy to overcome some drawbacks in preparing materials with desirable properties. This project aims to evaluate if electrostatic complexation between cellulose nanofibers (CNF) and biopolymers with opposite charges (alginate, lignin, and chitosan) in water can lead to materials that exhibit wet resilience and improved mechanical properties. The complexation will be evaluated under different experimental conditions, such as CNF degree of substitution, CNF/biopolymer mass fraction, pH, ionic strength. Further, 3D-printing methodologies, using a co-axial nozzle, will be developed to promote complexation during printing. Using a combination of viscometry, small-angle X-ray scattering (SAXS), and cryo-transmission (cryo-TEM) electron microscopy, it will be possible to investigate the electrostatic assembly of nanofibers and explore the preparation of hydrogels, underwater adhesives, and nanopapers. The results may help understand the behavior of CNFs in aqueous systems and open new avenues in the production of nanocellulose materials prepared in water, with nontoxic chemicals, and using a scalable method. (AU)

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(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
NASCIMENTO, DIEGO M.; COLOMBARI, FELIPPE M.; FOCASSIO, BRUNO; SCHLEDER, GABRIEL R.; COSTA, CARLOS A. R.; BIFFE, CLEYTON A.; LING, LIU Y.; GOUVEIA, RUBIA F.; STRAUSS, MATHIAS; ROCHA, GEORGE J. M.; et al. How lignin sticks to cellulose-insights from atomic force microscopy enhanced by machine-learning analysis and molecular dynamics simulations. NANOSCALE, v. 14, n. 47, p. 11-pg., . (14/50884-5, 19/04527-0, 20/07794-6, 17/02317-2, 16/04514-7)

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