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In healthcare facilities, the rapid emergence of multidrug-resistant pathogenic bacteria necessitates the development of novel strategies to control or prevent their spread.Hence, current research focuses on the fabrication of pure and Ni-doped TiO₂ nanoparticles via green approach by employing aqueous extracts of A.muricata leaves.XRD, FTIR, XPS, HR-TEM, SAED, SEM, EDX, UV-Visible and TGA are used to examine structural, morphol., optical and thermal properties of the obtained nanopowder.XRD pattern reveals all samples have crystalline nature and exhibit the tetragonal anatase phase of TiO₂.XPS measurements confirm successful incorporation of Ni²⁺ions into the TiO₂ host lattice.FTIR spectra represent the presence of diverse functional groups along with their vibrational modes.The presence of Ti, O and Ni in EDAX spectra indicates sample purity.Moreover, TiO₂ nanoparticles have a significant cytotoxic effect on L929 normal fibroblast cell lines.Addnl., the DPPH radical scavenging method is used to investigate antioxidant properties.Antimicrobial activity of nanoparticles was assessed against various microbial and fungal strains.Also, photocatalytic action was evaluated for methylene blue and congo red dye degradation in the presence aqueous solution under direct irradiation of sunlight.Thus, the findings suggest A. muricata leaf extract can be utilized for the design and fabrication of pure and Ni-doped TiO₂ nanoparticles, as well as a variety of biomedical and photocatalytic applications.

04 Mar 2025·Journal of Thermal Stresses

Non-simple thermoelastic diffusion interaction in a half-space with nonlocality and memory effect

Author: Varghese, Vinod ; Chandel, Nitin ; Khalsa, Lalsingh

06 Dec 2024·Advances in Educational Technologies and Instructional Design

Exploring the Potentiality of Artificial Intelligence in the Higher Education System

Author: Ansary, Mazhar Shamsi ; Behera, Santosh Kumar ; Mahato, Indrajit ; Gorai, Nupur ; Mahanti, Jayashree

This chapter investigates undergraduate students' perceptions of the potential of artificial intelligence (AI) in higher education. Using a descriptive survey method, data were collected from 322 undergraduate students in West Bengal through random sampling and analyzed using perception scales. Additionally, unstructured interviews were conducted to explore the reasons behind low levels of perception. The study employed Mean, Standard Deviation, Kruskal-Wallis test, Mann-Whitney U test, post-hoc analysis, and percentage calculations to analyze data and test hypotheses. Findings indicate that students generally have a moderate perception of AI's potential in higher education, with significant variation observed across academic streams (Arts, Science, and Commerce) but no notable differences based on gender, caste, location, or semester. The study also uncovers factors contributing to the low perception levels among undergraduates regarding AI's role in higher education.

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