The study objective was to model and predict the bioethanol production process from lignocellulosic biomass based on an example of empirical study results. Two types of algorithms were used in machine learning: artificial neural network (ANN) and random forest algorithm (RF). Data for the model included results of studying bioethanol production with the use of ionic liquids (ILs) and different enzymatic preparations from the following biomass types: buckwheat straw and biomass from four wastelands, including a mixture of various plants: stems of giant miscanthus, common nettle, goldenrod, common broom, fireweed, and hay (a mix of grasses). The input variables consisted of different ionic liquids (imidazolium and ammonium), enzymatic preparations, enzyme doses, time and temperature of pretreatment, and type of yeast for alcoholic fermentation. The output value was the bioethanol concentration. The multilayer perceptron (MLP) was used in the artificial neural networks. Two model types were created; the training dataset comprised 120 vectors (14 elements for Model 1 and 11 elements for Model 2). Assessment of the optimum random forest was carried out using the same division of experimental points (two random datasets, containing 2/3 for training and 1/3 for testing) and the same criteria used for the artificial neural network models. Data for mugwort and hemp were used for validation. In both models, the coefficient of determination for neural networks was <0.9, while for RF it oscillated around 0.95. Considering the fairly large spread of the determination coefficient, two hybrid models were generated. The use of the hybrid approach in creating models describing the present bioethanol production process resulted in an increase in the fit of the model to R² = 0.961. The hybrid model can be used for the initial classification of plants without the necessity to perform lengthy and expensive research related to IL-based pretreatment and further hydrolysis; only their lignocellulosic composition results are needed.

Polydopamine (PDA)-coated magnetic nanoparticles functionalized with mono-6-thio-β-cyclodextrin (SH-βCD) were obtained and characterized by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), Nuclear and Magnetic Resonance Imaging (NMR and MRI), and doxorubicin (DOXO)-loading experiments. The liver cancer cellular internalization of DOXO-loaded nanoparticles was investigated by confocal imaging microscopy. Synthesized nanomaterials bearing a chemotherapeutic drug and a layer of polydopamine capable of absorbing near-infrared light show high performance in the combined chemo- and photothermal therapy (CT-PTT) of liver cancer due to the synergistic effect of both modalities as demonstrated in vitro. Moreover, our material exhibits improved T₂ contrast properties, which have been verified using Carr-Purcell-Meiboom-Gill pulse sequence and MRI Spin-Echo imaging of the nanoparticles dispersed in the agarose gel phantoms. Therefore, the presented results cast new light on the preparation of polydopamine-based magnetic theranostic nanomaterials, as well as on the proper methodology for investigation of magnetic nanoparticles in high field MRI experiments. The prepared material is a robust theranostic nanoasystem with great potential in nanomedicine.

Magnetic nanoparticles (MNP) coated with bioinspired polydopamine (PDA) were obtained via a co-precipitation method and oxidative polymerization of dopamine. Nanoparticles were investigated by FTIR, TEM and SQUID. Loading capacity of anticancer drug doxorubicin was determined by UV-Vis spectroscopy. The nanocomposites exhibit a high drug loading capacity of 0.46 mg mg⁻¹. Anticancer activity of the nanocomposites was proved in profound in vitro tests on HeLa cells. Cytotoxicity and internalization of nanoparticles were checked using various method, i.e. proliferation assay (WST-1), a two-colour fluorescence cell viability assay, and fluorescent and confocal microscopy.

This article describes how crystalline or fibrous nanocellulose influences the mechanical properties of paper substrate. In this context, we used commercially available cellulose nanocrystals, mechanically prepared cellulose nanofibers dispersed in water or ethanol, and carboxy cellulose nanofibers. Selective reinforcement of the paper treated with the nanocellulose samples mentioned above was observed. The change in the fibre structure was assessed using scanning electron microscopy, roentgenography, and spectroscopy techniques. In addition, the effect of nanocellulose coating on physical properties was evaluated, specifically tensile index, elongation coefficient, Elmendorf tear resistance, Bendtsen surface roughness, Bendtsen air permeability, and bending strength. It can be concluded that the observed decrease in the strength properties of the paper after applying some NC compositions is due to the loss of potential disturbances in hydrogen bonds between the nanocellulose dispersed in ethanol and the paper substrate. On the other hand, significantly increased strength was observed in the case of paper reinforced with nanocellulose functionalized with carboxyl groups.

The application of ionic liquids (ILs) has grown enormously, from their use as simple solvents, catalysts, media in separation science, or electrolytes to that as task-specific, tunable molecular machines with appropriate properties. A thorough understanding of these properties and structure–property relationships is needed to fully exploit their potential, open new directions in IL-based research and, finally, properly implement the appropriate applications. In this work, we investigated the structure–properties relationships of a series of alkyltriethylammonium bis(trifluoromethanesulfonyl)imide [TEA-R][TFSI] ionic liquids in relation to their thermal behavior, structure organization, and self-diffusion coefficients in the bulk state using DSC, FT-IR, SAXS, and NMR diffusometry techniques. The phase transition temperatures were determined, indicating alkyl chain dependency. Fourier-transformed infrared spectroscopy studies revealed the structuration of the ionic liquids along with alkyl chain elongation. SAXS experiments clearly demonstrated the existence of polar/non-polar domains. The alkyl chain length influenced the expansion of the non-polar domains, leading to the expansion between cation heads in polar regions of the structured IL. ¹H NMR self-diffusion coefficients indicated that alkyl chain elongation generally caused the lowering of the self-diffusion coefficients. Moreover, we show that the diffusion of anions and cations of ILs is similar, even though they vary in their size.