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This study presents a novel approach in which a dual network (DN) composite, comprising polyvinyl alcohol (PVA) and ribbon-like nanocellulose (RC), was synthesized in one step using the volume exclusion effect involved in enzyme-catalyzed cellulose synthesis. Additionally, the impact of PVA as a crowding reagent during enzymatic catalysis on the in situ formation of nanocellulose and its resulting aspect ratio was explored. In contrast, the other two composites were created by incorporating enzyme-catalyzed synthetic block cellulose (BC) and its acid-hydrolyzed regenerated disc-shaped cellulose (DC) into the PVA. Subsequently, the mechanism by which three distinct types of nanocellulose, varying in morphology and size, was explored to elucidate their contributions to enhancing the properties of PVA. The results demonstrated that PVA/RC outperformed PVA/BC and PVA/DC. The elevated aspect ratio and intricate network structure of RCs not only significantly bolster the mechanical robustness of PVA/RC, leading in an 86.40 % surge in tensile strength and a remarkable 277.03 % rise in tensile modulus in comparison to pure PVA, but also induce a slight enhancement in elongation at break. Moreover, the thermal stability and biodegradability of PVA/RC was enhanced. Collectively, this study introduces an innovative strategy for the efficient fabrication of biodegradable composites with enhanced properties. [Display omitted] [ABSTRACT FROM AUTHOR]

Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R2 of 0.912 and rRMSE of 15.387%, and the validation set performance had an R2 of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R2 of 0.901 and rRMSE of 16.318%, and the validation set performance had an R2 of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture. [ABSTRACT FROM AUTHOR]