In the paper by Asiri et al. [Acta Cryst. (2012), E68, o1154], the title and the chemical name of one of the reagents used in the synthesis are corrected.[This corrects the article DOI: 10.1107/S1600536812011579.].
In the current study, a variety of sulfonated polyethersulfone (SPES)-based ion-exchange membranes were prepared and utilized as efficient and selective solid adsorbents for the detection of Co(II) ions in aquatic solutions. SPES membranes were treated with a variety of cations at a 2:1 ratio overnight. The produced materials were assessed via XRD, FT-IR, SEM, and TGA analyses. The structure of these materials was confirmed by FT-IR and XRD, which also confirmed the inclusion of Na+, NH4+, and amberlite on the SPES surface successfully. TGA analysis showed that the thermal stabilities of these materials were enhanced, and the order of stability was NH4-SPES > SPES > Na-SPES > A-SPES. Furthermore, the efficiency of these modified membranes for the determination and adsorption of a variety of metal ions was also examined by the ICP-OES analytical technique. A-SPES expressed a powerful efficiency of adsorption, and it showed an efficient as well as quantitative adsorption at pH = 6. Moreover, A-SPES displayed the highest adsorption capacity of 90.13 mg/g for Co(II) through the Langmuir adsorption isotherm.
Quick setting and poor injectability due to liquid-solid phase separation have limited the clinical use of brushite and monetite cements. The presence of certain ions in the cement during the setting reaction moderate the setting time and properties of the cement. This study reports the preparation of injectable bone cement by using biphasic calcium phosphate (BCP) extracted from femur lamb bone by calcination at 1450 °C. EDX analysis infers the presence of Mg and Na ions as trace elements in BCP. X-ray diffraction patterns of the prepared cement confirmed the formation of brushite (DCPD) along with monetite (DCPA) as a minor phase. DCPA phase diminished gradually with a decrease in powder to liquid ratio (PLR). Initial and final setting time of 5.3 ± 0.5 and 14.67 ± 0.5 min respectively are obtained and within the acceptable recommended range for orthopedic applications. Exceptional injectability of ≈90% is achieved for all prepared bone cement samples. A decrease in compressive strength was observed with increase in the liquid phase of the cement, which is attributed to the higher degree of porosity in the set cement. Immersion of bone cement in simulated body fluid (SBF) for up to 7 days resulted in the formation of apatite layer on the surface of cement with Ca/P ratio 1.71, which enhanced the compressive strength from 2.88 to 9.15 MPa. The results demonstrate that bone cement produced from BCP extracted from femur lamb bone can be considered as potential bone substitute for regeneration and repair of bone defects.
In recent years, users' privacy concerns and reluctance to use have posed a challenge for the social media and wellbeing of its users. There is a paucity of research on elderly users' negative connotations of social media and the way these connotations contribute to developing passive behaviour towards social media use, which, in turn, affects subjective wellbeing. To address this research vacuum we employed the stressor-strain-outcome (SSO) approach to describe the evolution of passive social media use behaviour from the perspective of communication overload, complexity, and privacy. We conceptualized subjective wellbeing as a combination of three components-negative feelings, positive feelings, and life satisfaction. Negative and positive feelings were used to derive an overall affect balance score that fluctuates between 'unhappiest possible' and 'happiest possible'. The proposed research framework was empirically validated through 399 valid responses from elderly social media users. Our findings reveal that communication overload and complexity raise privacy concerns among social media users, which leads to passive usage of social media. This passive social media use improved the subjective wellbeing favourably by lowering negative feelings and raising positive feelings and life satisfaction. The findings also revealed that respondents' overall affect balance leans towards positive feelings as a consequence of passive social media use. This study contributes to the field of technostress by illuminating how the SSO perspective aid the comprehension of the way passive social media use influences the subjective wellbeing of its users.
This paper describes a dataset collected from a survey carried out in the United Kingdom, Malaysia, and Pakistan, to understand the variables that impact political trust. The data was collected from September to November 2021 via an online survey on Google Forms, and 472 valid responses were obtained. Drawing on relevant literature, the survey instrument was designed to cover the respondents' opinions concerning partisanship, social media utilization, online social capital, voluntary online and offline political participation, and political trust. The dataset offers useful insights for institutional practitioners and policymakers working in the domains of democracy and political communication, facilitating policy formulation to bolster political trust through collaborative crowdsourcing.
This study investigates the thermal decomposition, thermodynamic and kinetic behavior of rice-husk (R), sewage sludge (S) and their blends during co-pyrolysis using thermogravimetric analysis at a constant heating rate of 20 °C/min. Coats-Redfern integral method is applied to mass loss data by employing seventeen models of five major reaction mechanisms to calculate the kinetics and thermodynamic parameters. Two temperature regions: I (200-400 °C) and II (400-600 °C) are identified and best fitted with different models. Among all models, diffusion models show high activation energy with higher R2(0.99) of rice husk (66.27-82.77 kJ/mol), sewage sludge (52.01-68.01 kJ/mol) and subsequent blends (45.10-65.81 kJ/mol) for region I and for rice husk (7.31-25.84 kJ/mol), sewage sludge (1.85-16.23 kJ/mol) and blends (4.95-16.32 kJ/mol) for region II, respectively. Thermodynamic parameters are calculated using kinetics data to assess the co-pyrolysis process enthalpy, Gibbs-free energy, and change in entropy. Artificial neural network (ANN) models are developed and employed on co-pyrolysis thermal decomposition data to study the reaction mechanism by calculating Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R2). The co-pyrolysis results from a thermal behavior and kinetics perspective are promising and the process is viable to recover organic materials more efficiently.