Rzeszów University of Technology

Nanomaterials possess superior optical, electrical, magnetic, mechanical, and thermal properties, which have made them suitable for a multitude of applications. The present review paper deals with recent advances in the measurement and modeling of thermophysical properties at the nanoscale (from the solid state to colloids). For this purpose, first, various techniques for the measurement of the solid state properties, including thermal conductivity, thermal diffusivity, and specific heat capacity, are introduced. The main factors that affect the solid state properties are grain size, grain boundaries, surface interactions, doping, and temperature, which are discussed in detail. After that, methods for the measurement and modeling of thermophysical properties of colloids (nanofluids), including thermal conductivity, dynamic viscosity, specific heat capacity, and density, are presented. The main parameters affecting these properties, such as size, shape, and concentration of nanoparticles, aggregation, and sonication time are studied. Furthermore, the properties of not only simple nanofluids but also hybrid nanofluids (which are composed of more than one type of nanoparticles) are investigated. Finally, the main research gaps and challenges are listed.

The anomalous Hall effect (AHE) is a consequence of spin-orbit coupling in a ferromagnetic metal and related primarily to density-matrix response to an electric field that is off-diagonal in band index. For this reason disorder contributions to the AHE are difficult to treat systematically using a semiclassical Boltzmann equation approach, even when weak localization corrections are disregarded. In this article we explicitly demonstrate the equivalence of an appropriately modified semiclassical transport theory which includes anomalous velocity and side-jump contributions and microscopic Kubo-Streda perturbation theory, with particular unconventional contributions in the semiclassical theory identified with particular Feynman diagrams when calculations are carried out in a band-eigenstate representation. The equivalence we establish is verified by explicit calculations for the case of the two-dimensional Dirac model Hamiltonian relevant to graphene.

Growing water scarcity and pollution are the main challenges that scientists need to focus on currently. Fenton-like processes are promising for applications related to water and wastewater treatment. Although there have been reviews on the fundamentals and applications of Fenton oxidation, a review focusing on the limitations of Fenton oxidation and their possible solutions is still insufficient. This review summarises the features, advantages, and drawbacks of the classic Fenton process. A comprehensive literature survey was conducted to review studies conducted over the last few decades dealing with the application of Fenton processes to organic pollutant removal from water and wastewater. The present overview highlights the modifications of Fenton processes focusing on industrial applications in water and wastewater treatment, especially for micropollutant degradation. Additionally, this study reviews the possibilities and future directions of research on Fenton-like processes to enable the incorporation of Fenton-based methods into existing water and wastewater treatment technologies, including industrial wastewater. It also presents a novel technological solution and improvements to the Fenton-like process to improve the efficiency and reduce the cost.

Technologies like AI and IoT have been employed in farming for some time now, along with other forms of cutting-edge computer science. There has been a shift in recent years toward thinking about how to put this new technology to use. Agriculture has provided a large portion of humanity’s sustenance for thousands of years, with its most notable contribution being the widespread use of effective agricultural practices for several crop types. The advent of cutting-edge IoT know-how with the ability to monitor agricultural ecosystems and guarantee high-quality production is underway. Smart Sustainable Agriculture continues to face formidable hurdles due to the widespread dispersion of agricultural procedures, such as the deployment and administration of IoT and AI devices, the sharing of data and administration, interoperability, and the analysis and storage of enormous data quantities. This work initially analyses existing Internet-of-Things technologies used in Smart Sustainable Agriculture (SSA) to discover architectural components that might facilitate the development of SSA platforms. This paper examines the state of research and development in SSA, pays attention to the current form of information, and proposes an Internet of Things (IoT) and artificial intelligence (AI) framework as a starting point for SSA.

This paper proposes an Energy Management System for the optimal operation of Smart Grids and Microgrids, using Fully Connected Neuron Networks combined with Optimal Power Flow. An adaptive training algorithm based on Genetic Algorithms, Fuzzy Clustering and Neuron-by-Neuron Algorithms is used for generating new clusters and new neural networks. The proposed approach, integrating Demand Side Management and Active Management Schemes, allows significant enhancements in energy saving, customers' active participation in the open market and exploitation of renewable energy resources. The effectiveness of the proposed Energy Management System and adaptive training algorithm is verified on a 23-bus 11 kV microgrid.

The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain.

Areal 3D analysis of surface texture gives more opportunities than a study of 2D profiles. Surface topography evaluation, considered as 3D dimensional analysis in micro or nanoscales, plays an important role in many fields of science and life. Among many texture parameters, those connected with height are the most often used. However, there are many other parameters and functions that can provide additional important information regarding functional behaviour of surfaces in different applications. The knowledge about the functional importance of various surface properties is low. This review tries to fill this gap. Surface texture parameters are presented in various groups: height, spatial, hybrid, functional, feature, and others. Based on experiences of the present authors and literature review, the relationships among various surface parameters and functional properties are described. A proposal of a selection of parameters on the basis of their functional significations is provided. Considerations for future challenges are addressed.

This paper investigates the impact of renewable energy sources (RESs), CO2 emissions, macroeconomics, and the political stability in a country on the Gross Domestic Product (GDP). The authors analyse the dynamics of RESs use, CO2 emissions, and GDP development and also test the following hypotheses: (1) The country’s economic growth is related to the energy consumption, in terms of both human resources and capital; (2) the share of the renewable energy consumption of the total energy consumption has a positive impact on the economic growth; and (3) the share of the renewable energy consumption of the total energy consumption is unrelated to the economic growth. To test the above hypotheses, the authors use the modified Cobb-Douglas production function, which also considers RES production volumes, CO2 emissions, and economic growth. The study employs data between 1995 to 2015 from the candidate and potential candidate countries for the EU membership. The data are drawn from the World Bank and Eurostat. The analyses entail panel unit root tests, Pedroni panel cointegration tests, fully modified OLS (FMOLS), dynamic OLS (DOLS) panel cointegration techniques, and the Vector Error Correction model (VECM). The findings confirm the relationship between RESs, CO2 emissions, and the GDP. For the EU countries, RESs as human resources and capital have an impact on the GDP. Moreover, the results reveal a correction retraction when the economic growth leads to an increase in renewable energy consumption. The investigation also finds that candidate and potential candidate countries for the EU membership should foster renewable energy development. The authors conclude that developing affordable and effective instruments and mechanisms to boost the RES implementation is necessary to decrease the anthropogenic impact on the environment (in particular, decreasing CO2 emissions) without any attendant reduction in the economic growth.

The main objective of the research and thinking presented in this study is to characterize the conditions and barriers related to the implementation of SDGs in supply chains. The explanation of the outlined research problem and the more detailed research issues prompted the authors to formulate the following auxiliary goals: identification and emphasis on the links between SSCM practices and UN SDGs and development of a model supporting the implementation of UN SDGs in SSCM. In summarizing our analysis of information from this field of inquiry, contributions to the literature include a new conceptual model, and a dynamic context for a three phased model for implementation of successful sustainable supply chain management initiatives. We provide a grounded approach for new theoretical insights that map supply chain practices for future research and performance measurement aligned with the United Nation's 17 Sustainable Development Goals.

Environmental degradation is a serious problem in modern civilization as it causes changes in consumer behavior towards ecologically responsible purchasing. In order to understand the green product purchasing decision process, an extensive literature review on green purchasing behavior was conducted. The current study is an exploratory research and the purpose of this paper was to investigate how sociodemographic factors explain consumers’ green purchase behavior. The differences between gender, age, education level, personal financial situation, and the number of children in the family were examined. To achieve the goal, we conducted a survey among 650 Polish consumers. Our findings suggest that all of the above-mentioned sociodemographic variables have an impact on awareness and purchasing behavior towards green products. Moreover, the results show that female consumers have more positive attitudes towards purchasing green products, than male consumers. Young consumers are skeptical about green products. A positive relationship was established between education and the acknowledgement of the dominance of one’s own needs over the needs of the environment. The better the personal financial situation, the more people expressed their intention to buy green products. The paper shows the implications for marketing. In addition, our exploratory research shows future research directions based on findings from the point of view of green consumer behavior. The study is an in-depth analysis of the factors determining green purchasing behavior. It contributes to the current literature by strengthening the existing knowledge about the factors influencing green purchase behavior in developing countries.

The designing of heat exchanger networks (HENs), mass exchanger networks (MENs), and water networks (WNs) is a major topic of process system engineering. The WN problem is most recent, and although the seminal work was published in 1980, the real development should be dated from 1994. Increasing public concern on scarce water resources, together with stringent regulations on wastewater discharge, has caused a great number of publications in recent years. Despite noticeable achievements in the topic, there is still a need for robust flexible approaches that will find wider application in practice. This review provides the reader with literature annotations on the WN problem from the year 1980 and follows the format of an earlier HEN review paper [Furman, K. C.; Sahinidis, N. V. A critical revue and annotated bibliography for heat exchanger network synthesis in the 20th century. Ind. Eng. Chem. Res. 2002, 41, 2335−2370]. An analysis of the WN problem formulation also is given, together with an overview of solution techniques. Statistics and classifications of the literature annotations are provided. Finally, thoughts on the direction of future research are presented.

The move towards a new Circular Economy (CE) economic model has been advocated and supported in Portugal, however, there is limited research on this topic. To address this gap, a quantitative research based on an online survey was carried out among 99 Portuguese organizations, encompassing a wide range of sectors and sizes. The results show that CE is regarded as a strategic and relevant issue for profitability and value creation. Furthermore, the perception that it requires the adoption of new business models in addition to the classical “reduce, reuse and recycle” approach is growing. Moreover, based on the hypotheses raised, results suggest that the level of CE adoption is positively impacted by the status of the EMS (Environmental Management System) certification and the willingness to improve the environmental performance and achieve a sustainable business model. However, CE activities are still relatively modest and a friendlier context (fiscal, legal, organizational, etc.) and the stronger support from supply chain agents and consumers are required. Future research should focus on how to design and shape the transition from a linear to a CE economy and to ascertain if the positive attitude towards CE is materialized in changing the way business is done.

Nanofluids are recent nanomaterials with improved thermophysical properties that could enhance the efficiency and reliability of heat transfer systems. Relevant properties for heat transfer calculation, thin film flows, droplet impingements or microfluidic are surface tension and wettability. However, to date, the understanding of those properties in nanofluids field is at the beginning compared to transport properties. At this stage, this review focus on the effect of nanoparticles and base fluid nature, temperature, use of surfactant, nanoparticle concentration, size and shape as well on the surface tension and wettability of nanofluids. After the presentation of heat transfer processes involving the influence of surface tension and wettability, this paper is organized according to the nature of the nanoparticles dealing with oxide, carbon-based and metallic nanofluids as well as unusual or less considered nature of nanoparticles. The factors affecting the surface tension of nanofluids are relatively well identified, but concentration and surfactant effects present some inconsistent outcomes. In any case, the dispersion of nanoparticles have an effect on the surface tension of base fluid significantly lower than that on transport properties. Based on results available in the literature and existing empirical correlations, a comprehensive assessment, challenges and future works are suggested.

The synthesis and characterization of a series of molybdenum, iron, ruthenium, nickel, palladium, and platinum complexes containing new achiral and chiral PNP pincer-type ligands based on the N-heterocyclic diamines 2,6-diaminopyridine, N, N ‘-di-10-undecenyl-2,6-diaminopyridine, N, N ‘-dihexyl-2,6-diaminopyridine, and 2,6-diamino-4-phenyl-1,3,5-triazine are reported. The new PNP ligands are prepared conveniently in high yield by treatment of the respective N-heterocyclic diamines with 2 equiv of a variety of achiral and chiral R 2 PCl compounds in the presence of base. Molybdenum PNP complexes of the type [Mo(PNP)(CO) 3 PNP] are obtained by treatment of [Mo(CO) 3 (CH 3 CN) 3 ] with 1 equiv of the respective PNP ligand. They were found to react with I 2 to give novel seven-coordinate pincer complexes of the types [Mo(PNP)(CO) 3 I] + and [Mo(PNP)(CO) 2 (CH 3 CN)I] + depending of whether the reaction is carried out in CH 2 Cl 2 or CH 3 CN. With [Fe(H 2 O) 6 ](BF 4 ) 2 and 1 equiv of PNP ligand in acetonitrile dicationic complexes of the type [Fe(PNP)(CH 3 CN) 3 ](BF 4 ) 2 are obtained. The cis and trans dichloride complexes [Ru(PNP)(PPh 3 )Cl 2 ] are prepared by a ligand exchange reaction of [RuCl 2 (PPh 3 ) 3 ] with a stoichiometric amount of the respective PNP ligand. Cationic PNP complexes of Ni(II), [Ni(PNP)Br]Br, were synthesized by the reaction of [NiBr 2 (DME)] with 1 equiv of PNP ligand. In similar fashion, treatment of [M(COD)X 2 ] (M = Pd, Pt; X = Cl, Br) with 1 equiv of PNP ligand yields the cationic square-planar complexes [M(PNP)X]X. If the reaction is carried out in the presence of the halide scavenger KCF 3 SO 3, complexes of the type [M(PNP)X]CF 3 SO 3 are obtained, which are better soluble in nonpolar solvents than the analogous halide compounds. X-ray structures of representative Mo, Fe, Ru, Ni, and Pd PNP complexes have been determined. Finally, the use of the palladium complexes as catalysts for the Suzuki−Miyaura coupling of some aryl bromides and phenyl boronic acid has been examined.

Simplification of electrochemically mediated atom transfer radical polymerization was achieved efficiently under either potentiostatic or galvanostatic conditions using an aluminum wire sacrificial anode (seATRP) immersed directly into the reaction flask without separating the counter electrode. seATRP polymerizations were carried out under different applied potentials, Eapps = E1/2, Epc, Epc -40 mV, and Epc -80 mV. As the rate of polymerization (Rp) can be modulated by applying different Eapp potentials, more reducing conditions resulted in faster Rp. The polymerization results showed similar narrow molecular-weight distribution throughout the reactions, similar to results observed for n-butyl acrylate (BA) polymerization under conventional eATRP. High-molecular-weight PBA and diblock copolymers were synthesized by seATRP with more than 90% monomer conversion. Furthermore, galvanostatic conditions were developed for synthesizing PBA with the two-electrode system.

This paper investigates the effect of the circular economy on CO2 emissions growth by considering the role of energy transition, climate policy stringency, industrialization, and supply chain pressure from 1997 to 2020 using panel quantile Autoregressive Distributed Lags (QARDL) and the panel PMG. We employ cointegration association in the long run among the variables, and the results of the two models confirm this. Findings reveal that circular economy and climate policy stringency significantly negatively impact carbon emissions. On the other hand, the energy transition, industrialization, and supply chain pressures are crucial to determining CO2 emissions in the short and long run. The finding further explores that municipal waste generation recycling is considerable at the mean and upper 90th quantiles than the lower quantile. Therefore, the empirical results of the current study provide acumens for policymakers of advanced economies and emerging markets to maintain the balance among circular economy, energy transition, environmental policy stringency, and supply chain pressure for reducing CO2 emissions without halting economic growth and sustainable development. Furthermore, practical implications are reported through the lens of carbon neutrality and structural changes.

Chrysin belongs to the group of natural polyphenols. It can be found, among others, in honey, propolis and fruits and has a wide range of biological activities, including the prevention of oxidative stress, inflammation, neurodegeneration and carcinogenesis. Being a part of the human diet, chrysin is considered to be a promising compound to be used in the prevention of many diseases, including cancers, diabetes and neurodegenerative diseases such as Alzheimer's or Parkinson's. Nevertheless, due to the low solubility of chrysin in water and under physiological conditions, its bioavailability is low. For this reason, attempts at its functionalization have been undertaken, aiming to increase its absorption and thus augment its in vivo therapeutic efficacy. The aim of this review is to summarize the most recent research on chrysin, including its sources, metabolism, pro-health effects and the effects of its functionalization on biological activity and pharmacological efficacy, evaluated both in vitro and in vivo.

Infrared spectra of some flavonoids and quercetin and morin sulfonic derivatives were recorded. The spectral data of sodium salts of quercetin and morin sulfonic acids were interpreted as indicating the near position valency band of the carbonyl group in relation to its position in quercetin and morin. The distinct shift of this band in quercetin-5'-sulfonic acid (QSA) may probably be a result of the occurrence of an intramolecular proton transfer from the sulfonic -SO3H to the oxygen of -C=O group.

PURPOSE: Prostate cancer (PCa) is a common tumor disease in western countries and a leading cause of cancer-driven mortality in men. Current methods for prostate cancer detection, like prostate-specific antigen screening, lead to significant overtreatment. The purpose of the study was to analyze circulating microRNAs in serum as non-invasive biomarkers in patients with diagnosis of prostate cancer and healthy individuals. METHODS: This preliminary study included a population of 20 patients with mean age of 68.6 years and mean PSA of 21.3 ng/ml. Eight healthy patients were used as control. MiRNAs were quantified in the total RNA fraction extracted from serum and levels of five microRNAs (miR-106b, miR-141, miR-21, mir-34a, and miR-375) were quantified by RT-qPCR. Statistical analyses evaluated correlation between clinicopathological data and miRNAs expression levels. RESULTS: Relative expression ratios of miR-106b, miR-141-3p, miR-21, and miR-375 were significantly increased (1.8-, 1.9-, 2.4-, and 2.6-fold, respectively) in the PCa group compared to healthy control. Using receiver operating characteristics, the highest area under the curve equal to 0.906 was obtained for miR-357 and indicates a very good diagnostic properties of this biomarker. We found expression level of mir-34a not related with PCa. CONCLUSIONS: Our results support previous findings on the possibility of discriminating prostate cancer patients from healthy controls by detecting miRNA (miR-141-3p, miR-21, and miR-375). Further insights into miRNA abundance and characteristics are necessary to validate the panel of miRNA as surrogate markers in diagnosis of prostate cancer.

The main objective of the present research is to combine the effect of scale thickness on the flow pattern and characteristics of two-phase flow that is used in oil industry. In this regard, an intelligent nondestructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline’s scale layer thickness. The proposed system includes a dual energy gamma source, composed of Barium-133 and Cesium-137 radioisotopes, and two sodium iodide detectors for recording the transmitted and scattered photons. Support Vector Machine was implemented for regime identification and Multi-Layer Perceptron with Levenberg Marquardt algorithm was utilized for void fraction prediction. Total count in the scattering detector and counts under photo peaks of Barium-133 and Cesium-137 were assigned as the inputs of networks. The results show the ability of presented system to identify the annular regime and measure the void fraction independent of petroleum pipeline’s scale layer thickness.