University of Chemical Technology and Metallurgy

Multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were embedded into poly(vinylidene fluoride) (PVDF) electrospun fibers with varying mass fractions (0–4 wt%). In addition, the hybrid combinations at 1.5 wt.% GNP/MWCNT/PVDF were fabricated to evaluate potential synergistic effects. The electrospun fiber mats were thermally post-treated at 145°C for 3 and 6 h to assess the influence of annealing on the microstructure, crystalline phase composition, and functional performance. The morphological, structural, mechanical, and piezoelectric properties were examined in relation to both the distinct geometries of the fillers and the thermal annealing. The combination of annealing and nanofiller-induced nucleation significantly improved the crystallinity of the fibers. The tensile strength increased up to 11.1 MPa for 0.5 wt.% MWCNT/PVDF and 15.7 MPa for 1.5 wt.% GNP/PVDF after 6 h of annealing. Structural analysis revealed a pronounced α→β phase transition in the 1.5 wt. % hybrid compositions, reaching a β/α ratio up to 7.8 substantially greater than the transitions observed for mono-filler systems: 4.7 for 1.5 wt.% GNP/PVDF and 3.0 for 1.5 wt.% MWCNT/PVDF. The effect of the high β-phase content was further confirmed at the nanoscale by piezoresponse force microscopy (PFM), which showed a consistent piezoelectric response through the fibers: a coercive voltage of approximately ± 40 V, which decreased to ± 20 V, ascribed to the dispersed nanofillers within the fiber. These results demonstrate that combining hybrid carbon fillers with controlled annealing enables tunable crystalline structures and enhanced electromechanical performance in electrospun PVDF nanofibers. • hybrid GNP/MWCNT fillers drive synergetically β-phase nucleation mechanisms. • The interfacial interaction between PVDF and carbon fillers was quantified using Piezoresponse Force Microscopy (PFM) measurements. • PFM confirmed a consistent piezoelectric response with coercive voltage decreasing from ± 40 V to ± 20 V due to dispersed nanofillers • Hybrid fillers and annealing tailor PVDF crystallinity and functional performance.

The recent endeavor to establish a sustainable society, with respect to environmental protection and occupational health prevention, imposes the need for the development of environmentally friendly anodization electrolytes. In addition, these electrolytes should be composed of biocompatible organic acids derived from renewable sources. In response to these challenges, a need arises to seek environmentally conscious alternatives to the widely used sulfuric acid anodization electrolytes. Accordingly, a comparative study was performed on the anodic polarization of AA2024-T3 aircraft alloy samples for 30 min at 0 or 20 °C. The respective electrolytes were composed of 0.5 M solutions of oxalic, citric, tartaric acids, or glycine. The comparative analysis included optical metallographic microscopy (OMM), scanning electron microscopy (SEM), determination of color characteristics and wettability, chemical composition analysis by X-ray photoelectron spectroscopy (XPS), and assessment of the corrosion protective properties of the obtained layers. The latter were defined by the application of electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization scanning (PDS) after 24 h of exposure to a 0.5% NaCl solution. Among the most important conclusions is that the barrier properties of the layers obtained in citric and tartaric acid electrolytes remarkably exceed those of the film obtained in oxalic acid. The use of glycine does not result in film formation at all. The process temperature did not have as strong an effect as the electrolyte composition.

We introduce a divergence-based framework for structural normalization and constrained reconstruction in generative models for poetic translation. The central hypothesis is that a text admits a contextualized, language-independent structural representation capturing semantic, prosodic, rhetorical, cultural, and aective invariants independently of surface linguistic form. A normalization operator embeds each text into a domain-dependent structural manifold conditional on a contextual knowledge state Kt. Reconstruction in a target language is formulated as divergence-minimizing projection under explicit constraint functionals. Structural preservation is quantied through domain-dependent divergence between probability measures induced by structural representations. Cross-linguistic transfer is interpreted as analogical alignment between contextualized structural states. Because structural representation depends on the contextual knowledge state, epistemic updates modify the geometry of structural comparison and may induce time-indexed optimal realizations. The proposed formulation establishes a mathematical perspective on translation as constrained structural projection in contextualized measure spaces, separating relational invariants from surface realization and enabling controllable generative reconstruction under explicit structural validation.

In this study, sol–gel-synthesized nanoparticles were characterized by various physicochemical techniques, including scanning electron microscopy (SEM), X-ray powder diffraction (XRD), UV-Vis spectrophotometry, and thermogravimetric analysis (DTA/TG). The as-obtained powders were tested for their antimicrobial activity against the Gram-positive bacteria Staphylococcus aureus and Enterococcus faecalis, as well as the fungal strains Candida albicans and Saccharomyces cerevisiae. Additionally, the photocatalytic performance of the samples was evaluated under simulated solar light. The results are promising for possible environmental applications. The antimicrobial assessment also revealed notable effects, with varying degrees of growth inhibition observed across the tested microorganisms. The main approach in this study consists of the combination of physicochemical characterization with antibacterial and photocatalytic evaluations, resulting in promising multifunctional materials.

The present study explores the potential of black cumin seed meal (Nigella sativa L.) (a by-product of industrial oil extraction) to produce activated carbon. The meal was carbonized at 500°C and chemically activated with 85 % H₃PO₄ at 500°C for 1 h. The obtained materials were characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and elemental analysis (C, H and N). FT-IR spectra revealed the formation of a carbonized structure with surface oxygenated and phosphate functional groups. SEM micrographs provided a visual indication of a developed surface morphology with finely distributed pores; however, this observation does not substitute for a quantitative pore structure analysis such as N₂-sorption/BET. The obtained results confirm that black cumin seed meal can be used as a promising material for activated carbon production under phosphoric acid activation.

The present study explores the possibility of combining membrane concentration, spray drying, and low-temperature precipitation into a single process for the valorization of spent lavender biomass as a source of ingredients rich in antioxidants. Lavender spent plant material was subjected to solid-liquid extraction, and the obtained hydroalcoholic extracts were further concentrated using a dead-end membrane filtration cell (METcell) with a polyamide-urea thin-film composite X201 membrane. The feed and the obtained retentate were subsequently spray dried using a Nano Spray Dryer B-90 (BÜCHI) under different temperature conditions (120 °C and 85 °C). Low-temperature precipitation was further applied for the retentate. An eight-fold concentration of the extracts was achieved, with membrane rejection coefficients of 100% for antioxidant activity and 98.5% for dry solids content. The permeate flux ranged from 2.25 to 0.201 L·m-2·h-1. Spray drying at a lower inlet temperature resulted in minimal losses for antioxidant activity (below 6%). The low-temperature storage of the membrane concentrate led to clear phase separation, allowing for the recovery of a precipitated fraction. The obtained results demonstrate that the integrated approach may support the sustainable and scalable valorization of lavender by-products.

In the context of global warming mitigation through energy conservation and pollution control, integrating green waste into treatment processes has become more popular. This study evaluated the potential of raw wood processing residues generated from furniture manufacturing as renewable sorbents for water treatment. Comparative studies assessed the Mn(II) removal efficiency of raw walnut (WW) and cherry (CW) wood shavings and the derived biochars (BChWW, BChCW) produced by hydropyrolysis. SEM, BET, FTIR, and TGA analyses characterized their surface and structural properties. CW demonstrated a higher adsorption capacity compared to WW. Physical activation enhanced the surface properties and Mn(II) adsorption affinity of the materials. Maximum adsorption capacities ranged from 2.1 to 2.2 mg/g for CW and WW, and 2.4 to 2.5 mg/g for BChCW and BChWW. The Freundlich model best fits to the data obtained using CW (R2 = 0.997) and BChCW (R2 = 0.984), while the RALF isotherm almost perfectly describes the mechanism of the Mn(II) adsorption onto WW (R2 = 0.999) and BChWW (R2 = 1.000). The pseudo second-order kinetic model shows strong agreement with experimental data, which suggests chemisorption on a heterogeneous surface. The results underscore the potential of wood industry byproducts as efficient and low-cost adsorbents for water treatment, supporting the circular economy and sustainable environmental management.

Large language models (LLMs) now form a regular part of scientific research practice, where they are used to assist with hypothesis formulation, literature synthesis, and various forms of formal reasoning. Their use builds on earlier ideas of \emph{delegated cognition} and brings into sharper focus questions about how epistemic agency and moral responsibility are distributed across human--machine arrangements. This paper develops a conceptual and formal framework for examining these hybrid modes of reasoning, drawing on an analogy with familiar academic hierarchies in which a principal investigator (PI) coordinates and supervises junior collaborators. Within this framework, three related operators are distinguished: \emph{verification} $V(g)$, which concerns logical consistency and empirical adequacy; \emph{responsibility mapping} $R(g)$, which assigns epistemic and moral accountability to human agents; and \emph{epistemic value} $E(g)$, which characterizes the justificatory status and cognitive standing of a result, regardless of whether it is produced by a human or an artificial system. Verification and moral authorship are treated as closely connected aspects of epistemic responsibility, in the sense that verifying a claim amounts to accepting responsibility for its truth. On this view, the ethical boundary in scientific research is not drawn between human and machine reasoning, but between \emph{responsible} and \emph{negligent} forms of delegation within a distributed cognitive system. The paper also introduces the notion of an \emph{epistemic audit} as an institutional mechanism, comparable to established quality-assurance practices, for documenting transparency, reproducibility, and coherence in AI-assisted research. The analysis contributes to ongoing discussions in cognitive epistemology and the philosophy of AI concerning authorship, verification, and responsibility in extended systems of scientific reasoning.

Fractional calculus, the study of derivatives and integrals of arbitrary order [...]

This article proposes an approach based on the combined use of information standards and model transformations with the aim of facilitating interoperability in enterprises. The implementation of the approach employs visual modeling through a software environment for model-driven software development. Real application cases of the proposed approach have been developed based on specific data models.