Iskanje po katalogu digitalne knjižnice

Opis: Superparamagnetic iron oxide nanoparticles (SPIONs) with sizes below 10 nm are biocompatible and non-toxic, making them promising for biomedical applications. To prevent their agglomeration and enhance their functionality, the nanoparticles were coated with citric acid (CA), which modifies the surface charge, improves dispersion stability, and facilitates biomedical use. In this work, a modular flow-through microreactor system was employed to synthesize and coat the nanoparticles in a single, continuous two-step process. The system enables precise control over temperature and mixing, ensuring uniform reaction conditions and minimizing hot spots. The synthesized Fe3O4 nanoparticles exhibited an average crystallite size of ~5 nm (XRD) and particle sizes of 4–6 nm (TEM). FTIR analysis confirmed the successful surface functionalization with CA, while TGA indicated a coating mass fraction of approximately 4–20 wt%, increasing with higher CA concentration. Zeta potential measurements revealed strong colloidal stability, with values around −35 mV at pH 6.5. Among the tested CA concentrations, the sample with a molar ratio of Fe3O4:CA = 1:0.25 exhibited the most favorable properties, including narrow size distribution and improved dispersion stability. These findings demonstrate that the continuous modular flow approach enables the reproducible synthesis of highly stable, sub-10 nm CA-coated SPIONs, offering promising potential for biomedical applications, particularly as magnetic resonance imaging (MRI) contrast agents.
Ključne besede: superparamagnetic iron oxide nanoparticles, citric acid, modular flow microreactor system, continuous synthesis, zeta potential measurements
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (1,54 MB)

Opis: Artificial intelligence (AI) has witnessed an exponential increase in use in various applications. Recently, the academic community started to research and inject new AI-based approaches to provide solutions to traditional software-engineering problems. However, a comprehensive and holistic understanding of the current status needs to be included. To close the above gap, synthetic knowledge synthesis was used to induce the research landscape of the contemporary research literature on the use of AI in software engineering. The synthesis resulted in 15 research categories and 5 themes—namely, natural language processing in software engineering, use of artificial intelligence in the management of the software development life cycle, use of machine learning in fault/defect prediction and effort estimation, employment of deep learning in intelligent software engineering and code management, and mining software repositories to improve software quality. The most productive country was China (n = 2042), followed by the United States (n = 1193), India (n = 934), Germany (n = 445), and Canada (n = 381). A high percentage (n = 47.4%) of papers were funded, showing the strong interest in this research topic. The convergence of AI and software engineering can significantly reduce the required resources, improve the quality, enhance the user experience, and improve the well-being of software developers.
Ključne besede: software engineering, artificial intelligence, machine learning, synthetic knowledge synthesis, AI
Objavljeno v DKUM: 30.07.2025; Ogledov: 0; Prenosov: 12
Celotno besedilo (1,39 MB)
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Opis: Background: Artificial intelligence and machine learning are playing crucial roles in digital transformation, aiming to improve the efficiency, effectiveness, equity, and responsiveness of primary health systems and their services. Method: Using synthetic knowledge synthesis and bibliometric and thematic analysis triangulation, we identified the most productive and prolific countries, institutions, funding sponsors, source titles, publications productivity trends, and principal research categories and themes. Results: The United States and the United Kingdom were the most productive countries; Plos One and BJM Open were the most prolific journals; and the National Institutes of Health, USA, and the National Natural Science Foundation of China were the most productive funding sponsors. The publication productivity trend is positive and exponential. The main themes are related to natural language processing in clinical decision-making, primary health care optimization focusing on early diagnosis and screening, improving health-based social determinants, and using chatbots to optimize communications with patients and between health professionals. Conclusions: The use of machine learning in primary health care aims to address the significant global burden of so-called “missed diagnostic opportunities” while minimizing possible adverse effects on patients.
Ključne besede: primary health care, machine learning, research landscape, synthetic knowledge synthesis
Objavljeno v DKUM: 24.07.2025; Ogledov: 0; Prenosov: 6
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Opis: Interconnected computers and software systems have become an indispensable part of people’s lives in the period of digital transformation. Consequently, software quality research is becoming more and more critical. There have been multiple attempts to synthesise knowledge gained in software quality research; however, they were focused mainly on single aspects of software quality and did not structure the knowledge holistically. To fill this gap, we harvested software quality publications indexed in the Scopus bibliographic database. We analysed them using synthetic content analysis which is a triangulation of bibliometrics and content analysis. The search resulted in 15,468 publications. The performance bibliometric analysis showed that the production of research publications relating to software quality is currently following an exponential growth trend and that the software quality research community is growing. The most productive country was the United States, followed by China. The synthetic content analysis revealed that the published knowledge could be structured into six themes, the most important being the themes regarding software quality improvement by enhancing software engineering, advanced software testing and improved defect and fault prediction with machine learning and data mining.
Ključne besede: software engineering, software quality, knowledge synthesis, bibliometrics, synthetic knowledge synthesis
Objavljeno v DKUM: 07.04.2025; Ogledov: 0; Prenosov: 6
Celotno besedilo (1,74 MB)
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Opis: E-textiles and their wearable analogues are finding applications in a myriad of sectors, ranging from sensors to health and sports applications. The development of a truly functional and reusable textile substrate presents a challenging task; its design encompasses the fabrication of optimal functional conductive particles, as well as devising strategies for their application that will ensure their functional properties (e.g. conductivity) are retained in an undiminished state for a foreseeable period. In the presented study, we tackled these two aspects in an interdependent way: i) The enhancement of the electrical conductivity of MXene-modified cotton fabric by increasing the lateral size of nanosheets during the Ti3C2TX synthesis, and ii) The improvement of washing durability of MXenes on the fabric surface by selecting suitable protective coatings. The results of Scanning Electron Microscopy (SEM), X-ray powder Diffraction (XRD), Dynamic Light Scattering (DLS) and Atomic Force Microscopy (AFM) revealed the successful synthesis of large and stable MXene nanosheets with ultrathin flakelike nanostructures, high colloidal stability and delamination yields. Using multiple application procedures of dipping and drying, the MXene nanosheets formed extensive adhesion areas on the cotton fabric and overlapped the fibre pores, thus reducing the interfacial resistance between the sheets and improving the coating uniformity and, consequently, increasing electrical conductivity. Weaker adhesion and depletion of large nanosheets were further effectively prevented by protective polymer coatings. The MXene-coated/protected fabrics had sufficient electrical conductivity, even after 20 laundering cycles. Moreover, the surface hydrophobicity was negligibly reduced, preventing water accessibility and, thus, increasing the oxidation stability of the applied MXenes.
Ključne besede: Ti3C2TX MXene, synthesis parameters, cotton fabric, electrical conductivity, protective coatings, washing durability
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 14
Celotno besedilo (8,43 MB)
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