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Microbial resistance to drugs is a public health problem; therefore, there is a search for alternatives to replace conventional products with natural agents. One of the potential antimicrobial agents is wood vinegar derived from the carbonization of lignocellulosic raw materials. The objectives of the present work were to evaluate the antibacterial and antifungal action of two kinds of wood vinegar (WV), one of Eucalyptus urograndis wood and another of Bambusa vulgaris biomass, and determine their chemical profile. The antimicrobial effect was assessed against Staphylococcus aureus, Pseudomonas aeruginosa, Salmonella enteritidis, Escherichia coli, Streptococcus agalactiae, and Candida albicans. The minimum inhibitory concentration and the minimum bactericidal and fungicidal concentrations were determined. Micrographs of the microorganisms before and after exposure to both kinds of wood vinegar were obtained by scanning electron microscopy. The chemical profile of the eucalyptus and bamboo vinegar was carried out by gas chromatography and mass spectrometry (GC/MS). Both types of WV presented significant antimicrobial activity, with the bamboo one having a higher efficiency. Both studied pyroligneous extracts seem promising for developing natural antimicrobials due to their efficiency against pathogens. GC/MS analyses demonstrated that the chemical profiles of both kinds of WV were similar but with some significant differences. The major component of the eucalyptus vinegar was furfural (17.2%), while the bamboo WV was phenol (15.3%). Several compounds in both WVs have proven antimicrobial activity, such as acetic acid, furfural, phenol, cresols, guaiacol, and xylenols. Together, they are the major in the chemical composition of the organic fraction of both WVs. Bamboo vinegar had a more expressive content of organic acids. Micrographs of microorganisms taken after exposure to both kinds of wood vinegar displayed several cell modifications. The potential of both types of wood vinegar as a basis for natural antimicrobial products seems feasible due to their proven effect on inhibiting the microorganisms' growth assessed in this experiment., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

The present work evaluates the production of wood–cement particleboards (WCBs) with addition of coconut-husk fibers. Trees of a Eucalyptus urophylla × Eucalyptus grandis hybrid were used as wood source. Boards were produced with addition of five coconut-fiber levels (0%, 10%, 20%, 30%, 40% and 50%) to replace the original wood, in a completely randomized design. From each board, specimens were taken to perform physical–mechanical tests (density, water absorption, thickness swelling, static bending, compression strength, and internal bond strength). The chemical characterization of the coconut fiber – in particular the total amount of extractives – in cold and hot water and an organic solvent, along with determination of lignin, holocellulose and ash amounts. The results showed that the addition of coconut fibers significantly affected the physical–mechanical properties of the boards. For the physical properties, the addition of coconut fibers improved the dimensional stability with the respective decrease in water absorption and thickness swelling. For mechanical properties, a decrease in the MOR and MOE was observed as the percentage of coconut fibers increased, most likely due to a poor interaction between the particles and the cementitious matrix. The resulting WCBs were suitable for nonstructural and indoor uses. [ABSTRACT FROM AUTHOR]

The purpose of this research was to evaluate the potential of Artificial Neural Networks in estimating the properties of wood resistance. In order to do so, a hybrid of eucalyptus (Eucalyptus urograndis) planted in the Northern Region of the State of Mato Grosso was selected and ten trees were collected. Then, four samples of each tree were removed, totaling 40 samples, which were later subjected to non-destructive testing of apparent density, ultrasonic wave propagation velocity, dynamic modulus of elasticity obtained by ultrasound, and Janka hardness. These properties were used as estimators of resistance and compressive strength parallel to fibers, and hardness. Multilayer Perceptron networks were also employed, training 100 of them for each of the evaluated parameters. The obtained results indicated that the use of Artificial Neural Networks is an efficient tool for predicting wood resistance. [ABSTRACT FROM AUTHOR]