(Cu0.4Al0.3)TaSe2: PREPARATION AND CRYSTAL STRUCTURE ANALYSIS FROM X-RAY POWDER DIFFRACTION
A new phase of the (CuAlSe2)1-x(TaSe)x alloy system was synthesized by the melt and annealingtechnique and studied by SEM, DTA, and XRPD techniques. Its structure has been refined by the Rietveld methodusing X-ray powder diffraction data. The new alloy corresponds with the stoichiometry Cu0.4Al0.3TaSe2. Thiscompound crystallizes in the hexagonal space group 𝑃6ത𝑚2 (Nº 187) with a MoS2-type structure, and unit cellparameters a = 3.455(2) Å, c = 13.423(4) Å, V = 138.7(1) Å3, Z =2. The crystal structure is based on the MoS2-type of stacking of TaSe2 layers with a partial ordering of Cu and Al cations over the tetrahedral sites. The powderpattern was composed of 63.1% of the principal phase Cu0.4Al0.3TaSe2 and 29.9% of CuAlSe2, 7.0% of TaSe3, asthe secondary phases.
Read ArticleADSORPTION STUDIES OF ZINC, COPPER, AND LEAD IONS FROM PHARMACEUTICAL WASTEWATER ONTO SILVER-MODIFIED CLAY ADSORBENT
Background: Industrial wastewater contains pollutants that are detrimental to human health in varied proportions. Among the pollutants are heavy metals, including Zn2+, Pb2+, and Cu2+ found in a characterized pharmaceutical wastewater. Several techniques have been proposed for the heavy metal sequester. However, they are with attendant challenges. The adsorption techniques using clay-metal oxide modified adsorbent/composite such as silver-clay adsorbent is considered suitable for an effective sequestering process. Aims: To develop and characterize Ag/clay adsorbent for pharmaceutical wastewater treatment. Methods: The Ag nanoparticles were synthesized using Parkia biglobossa aqueous leaves extract in an optimization study. The raw clay was beneficiated and doped with silver nanoparticles via the wet impregnation method. The silver-clay adsorbent was characterized using FTIR, XRD, SEM, and EDS characterization tools. The developed adsorbent was used for the batch adsorption process of the heavy metal ion removal from the wastewater. Results and Discussion: The phytochemical analysis and FTIR results of the P. biglobosa showed that the leaf contains phenol, tannin, and flavonoids which acts as reducing, capping, and stabilizing agent required for synthesizing the silver nanoparticles. The prepared silver nanoparticles modified clay adsorbent Ag/clay, have evenly distributed stacks of pseudo-hexagonal plates, are rich in silica, possess silver nanoparticles in the frameworks, and contain functional groups suitable for binding heavy metals. The adsorptions of Zn2+, Pb2+, and Cu2+ from pharmaceutical wastewater onto the silver-modified clay were studied as a function of adsorbent dosage and contact time. The percentage removal results obtained showed that the adsorbent had up to 99.96%, 99.5%, and 99.44% removal efficiency for Zn2+, Pb2+, and Cu2+, respectively, which are better compared with previous studies. The adsorption process was feasible, spontaneous, and exothermic, with Langmuir and Pseudo-second-order models as best fits for the process. Conclusions: The adsorption of selected heavy metal ions onto the green synthesized silver-modified clay adsorbent (Ag/clay) was feasible, spontaneous, and exothermic in the order Zn2+>Pb2+>Cu2+ with Langmuir and Pseudo-second-order model best fitted for the process. These show that the synthesized silver oxide nanoparticles supported on local clay can be used as a potentially low-cost adsorbent to remove heavy metal ions from industrial wastewater.
Read ArticleFERRAMENTA DE ANONIMIZAÇÃO DE DADOS MÉDICOS COM PRESERVAÇÃO DE PRIVACIDADE
Background: Medical institutions collect a vast amount of sensitive patient data for personalized treatments and health trend analysis. However, this raises concerns regarding the privacy of patient data, as it contains sensitive and confidential information. Aims: Develop an anonymization tool using diverse techniques to protect data while preserving its utility. Methods: A Python-based data anonymization tool for medical datasets supporting both categorical and numerical data is developed. It employs various methods, including data perturbation, binning, scaling, transformation, and differential privacy. Results: The tool was able to anonymize sensitive data, both categorical and numerical, while preserving its utility for further analysis. Discussion: The Privacy-Preserving Data Anonymization Tool advances sensitive medical data management by anonymizing both categorical and numerical data using various techniques while retaining data utility. Conclusions: The Anonymization Tool addresses patient data privacy concerns by balancing data utility with privacy, enabling secure medical data use in research.
Read Article