Biosorption processes emerge as an economical and eco-friendly alternative technology for the removal of pollutants present in the environment. One biosorption process application is found in the immobilization of microbial cells for the removal of toxic pollutants in industrial wastewater. The immobilization of microorganisms on magnetic nano-based carriers is a relevant new technique applied to obtain biocatalysts for environmental pollution control, however, it has been little described in the literature. Magnetic nanoparticles present several advantages when compared to suspended microorganism technology and conventional methods of cell immobilization, such as simple recovery of catalysts from the liquid phase. Considering the relevant application of magnetic nanoparticles as a support for microorganism immobilization, this review aims to present potential studies in the immobilization of bacteria, yeast, and fungi on magnetic nano-based carriers. Immobilization carriers and methods are highlighted and discussed based on their strengths and drawbacks. Isotherm and kinetic models are presented. This review also highlights the application of immobilized microbial nanoparticles in the removal of heavy metals from the environment. The state-of-the art of biosorption technology applying magnetically immobilized cells is presented in this review and provides insights into this research area.

In this study, a mild-temperature two-step dilute acid and alkaline pretreatment (DA-AL) process was developed to generate highly digestible cellulose pulp from sugarcane bagasse for producing fermentable sugars by novel thermophilic cellulases derived from Phomopsis stipata SC 04. First, DA pretreatment of sugarcane bagasse at 2% (w/v) H₂SO₄ and 121 °C for 71 min, followed by AL pretreatment at 2.2% (w/v) NaOH and 110 °C for 100 min led to the pulp containing 86% cellulose. The cellulose pulp was hydrolyzed by the immobilized P. stipata cellulase on Ca-alginate beads, following optimization of immobilization conditions. The results showed that mixing the cellulase extract and sodium alginate solutions at a volume ratio of 1:4 led to the highest immobilization efficiencies of 99.83% for β-glucosidase and 97.52% for endoglucanase while the enzyme leakage was the lowest. The use of the immobilized cellulases led to a cellulose digestibility of 30% in the initial batch and recycling of the immobilized cellulases reduced cellulose digestibility to 18% after s recycling for two times (a total of third rounds). Overall, this study provides useful information in the use of a mild pretreatment process to produce highly digestible cellulose pulp and in the immobilization of thermophilic cellulases to produce fermentable sugars from pretreated biomass.

Xylitol is a five-carbon sugar alcohol of industrial interest due to its applications as a food sweetener and sugar substitute. In this study, artificial neural networks combined with a genetic algorithm were evaluated as a data-driven approach for modeling and exploring xylitol production by Spathaspora boniae and Spathaspora brasiliensis during fermentation of sugarcane bagasse hemicellulosic hydrolysate. The dataset comprised 20 experimental points obtained from a face-centered central composite design, using urea, yeast extract, peptone, and ammonium sulfate as input variables. The neural network models showed high goodness-of-fit, with R² values of 0.9952 for S. boniae and 0.9930 for S. brasiliensis. Experimental validation of the optimized conditions resulted in xylitol production of 11.54 ± 0.52 g L⁻¹ for S. boniae and 9.29 ± 0.24 g L⁻¹ for S. brasiliensis. Comparison with response surface methodology showed that both approaches provided strong predictive performance, although the statistical model predicted the optimum conditions more accurately. For S. boniae, however, the ANN-GA approach identified an alternative condition associated with lower nitrogen supplementation and higher experimental xylitol production. Given the limited dataset, this study should be regarded as a proof-of-concept for the application of data-driven optimization tools to xylitol fermentation. The results indicate that ANN-GA can complement classical statistical methods by helping to identify alternative operating conditions in bioprocess optimization.