2,859 publications from this institution
The proposed chemical sensor based on Ag<sub>2</sub>O/CB nanocomposites is developed by electrochemical approach for the detection of hazardous selective 3-methoxyphenol chemical sensor for the safety of the environment sector in a broad scale.
Chromatographic separation of the extract of the aerial parts of Dodonaea viscosa L. (family Sapindaceae) afforded beta-sitosterol, stigmasterol, the flavone acacetin-7-methyl ether, the flavonol-3-methyl ethers 4',5,7-trihydroxy-3,6-dimethoxyflavone and penduletin, as well as a new clerodane diterpenoid which was identified by spectral means as 15,16-epoxy-5,9-diepicleroda-3,13(16),14-trien-20,19-olide.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
In this investigation, a melamine electrochemical sensor has been developed by using wet-chemically synthesized low-dimensional aggregated nanoparticles (NPs) of ZnO-doped Co<sub>3</sub> O<sub>4</sub> as sensing substrate that were decorated onto flat glassy carbon electrode (GCE). The characterization of NPs such as UV-Vis, FTIR, XRD, XPS, EDS, and FESEM was done for detailed investigations in optical, functional, structural, elemental, and morphological analyses. The ZnO-doped Co<sub>3</sub> O<sub>4</sub> NPs decorated GCE was used as a sensing probe to analyze the target chemical melamine in a phosphate buffer at pH 5.7 by applying differential pulse voltammetry (DPV). It exhibited good performances in terms of sensor analytical parameters such as large linear dynamic range (LDR; 0.15-1.35 mM) of melamine detection, high sensitivity (80.6 μA mM<sup>-1</sup> cm<sup>-2</sup> ), low limit of detection (LOD; 0.118±0.005 mM), low limit of quantification (LOQ; 0.393 mM), and fast response time (30 s). Besides this, the good reproducibility (in several hours) and repeatability were investigated under identical conditions. Moreover, it was implemented to measure the long-time stability, electron mobility, less charge-transfer resistance, and analyzed diffusion-controlled process for the oxidation reaction of the NPs assembled working GCE electrode, which showed outstanding chemical sensor performances. For validation, real environmental samples were collected from various water sources and investigated successfully with regard to the reliability of the selective melamine detection with prepared NPs coated sensor probe. Therefore, this approach might be introduced as an alternative route in the sensor technology to detect selectively unsafe chemicals by an electrochemical method with nanostructure-doped materials for the safety of environmental, ecological, healthcare fields in a broad scale.
Background: Color effluents generated from the production industry of dyes and pigments and their use in different applications, such as textile, paper, leather tanning, and food industries, are high in color and contaminants that damage the aquatic life. It is estimated that about 10 5 of various commercial dyes and pigments amounted to 7×10 5 tons are produced annually worldwide. Ultimately, about 10-15% is wasted into the effluents of the textile industry. Chitin is abundant in nature, and it is a linear biopolymer containing acetamido and hydroxyl groups amenable to render it atmospheric by introducing amino and carboxyl groups, hence able to remove different classes of toxic organic dyes from colored effluents. Methods: Chitin was chemically modified to render it amphoteric via the introduction of carboxyl and amino groups. The amphoteric chitin has fully been characterized by FTIR, TGA-DTG, elemental analysis, SEM, and point of zero charges. Adsorption optimization for both anionic and cationic dyes was made by batch adsorption method, and the conditions obtained were used for studying the kinetics and thermodynamics of adsorption. Results: The results of dye removal proved that the adsorbent was proven effective in removing both anionic and cationic dyes (Acid Red 1 and methylene blue (MB)), at their respective optimum pHs (2 for acid and 8 for cationic dye). The equilibrium isotherm at room temperature fitted the Freundlich model for MB, and the maximum adsorption capacity was 98.2 mg/g using 50 mg/l of MB, whereas the equilibrium isotherm fitted the Freundlich and Langmuir model for AR1 and the maximum adsorption capacity was 128.2 mg/g. Kinetic results indicate that the adsorption is a two-step diffusion process for both dyes as indicated by the values of the initial adsorption factor (Ri) and follows the pseudo-second-order kinetics. Also, thermodynamic calculations suggest that the adsorption of AR1 on the amphoteric chitin is an endothermic process from 294 to 303 K. The result indicated that the mechanism of adsorption is chemisorption via an ion-exchange process. Also, recycling of the adsorbent was easy, and its reuse for dye removal was effective. Conclusion: New amphoteric chitin has successfully been synthesized and characterized. This resin material, which contains amino and carboxyl groups, is novel as such chemical modification of chitin hasn’t been reported. The amphoteric chitin has proven effective in decolorizing aqueous solution from anionic and cationic dyes. The adsorption behavior of amphoteric chitin is believed to follow chemical adsorption with an ion-exchange process. The recycling process for few cycles indicated that the loaded adsorbent could be regenerated by simple treatment and retested for removing anionic and cationic dyes without any loss in the adsorbability. Therefore, the study introduces a new and easy approach for the development of amphoteric adsorbent for application in the removal of different dyes from aqueous solutions.
Background: Infection control is a critical component of patient safety in healthcare settings, requiring coordinated efforts among medical staff. Effective collaboration and synergy among healthcare professionals, including physicians, nurses, infection control specialists, and support staff, are essential to reducing healthcare-associated infections (HAIs). However, barriers such as hierarchical structures, lack of communication, and inadequate training often hinder teamwork, impacting infection prevention outcomes. Objective: This systematic review aims to analyze the role of team synergy and interdisciplinary collaboration in improving infection control practices within healthcare institutions. The study identifies key factors that enhance or hinder effective teamwork in infection prevention and explores evidence-based strategies for fostering collaboration. Methods: A comprehensive literature search was conducted across databases including PubMed, Scopus, Web of Science, and Cochrane Library. Studies published between 2016 and 2024 focusing on teamwork, interdisciplinary collaboration, and infection control in healthcare settings were included. The review follows the PRISMA guidelines for systematic reviews, with data extracted on study design, intervention strategies, and impact on infection control outcomes. Results: The findings indicate that structured teamwork interventions, such as shared decision-making, cross-disciplinary training, and standardized communication protocols, significantly reduce infection rates. Hospitals with well-defined team roles and interprofessional coordination demonstrate higher compliance with infection prevention protocols. However, challenges such as resistance to collaborative change and lack of institutional support remain obstacles to effective teamwork in infection control. Conclusion: Healthcare institutions must prioritize fostering a culture of teamwork and synergy among medical staff to enhance infection control measures. Implementing structured teamwork training, leadership engagement, and communication strategies can improve patient safety and reduce HAIs. Future research should explore innovative technologies, such as AI-driven collaboration tools, to optimize infection control strategies