Investigating the effect of SPS‎ parameters on densification ‎and fracture toughness of ZrB2-SiC nanocomposite‎

In this study, the effect of sintering parameters on densification and fracture toughness of spark plasma sintering ZrB₂-SiC nanocomposites was evaluated. For this purpose, ZrB₂-??30?vol% SiC nanocomposites in the conditions of ?1600?°C-4?min, 1700?°C-4?min, 1800?°C-4?min, 1800?°C-8?min, 1800?°C-12?min? were sintered.? Scanning Electron Microscopy (SEM) was used in order to investigate the ?microstructural variations. The bulk density was measured accoring to ASTM C 373–88. Single edge notch beam (SENB) method was used to ?determine the fracture toughness of samples. Microstructural observations showed that ?an increase in sintering temperature led to slight ?increase in SiC grains size but no sensitive variation in ZrB₂. However, increasing the sintering time resulted to increase both ZrB₂ and SiC grain size. Also, it was found, temperature and time ascent always increases the relative density. In addition, it was concluded that optimal temperature and time to reach the highest fracture toughness are 1800?°C and 8?min, respectively. Investigation of SEM images of the Vickers indent and their path propagation showed that the deviation and branching of crack are the most important toughening ?mechanisms in ZrB₂-SiC nanocomposites.?     

Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country

Augmented reality (AR) has gained increased recognition in varying fields, in particular educational contexts. In the wake of the Covid-19 pandemic, home-based learning becomes a reality and is already in place across the globe, and learning via augmented reality technology will help learners comprehend learning content in a more creative frame of mind than ever before. Very little research has examined the adoption behavior of augmented reality in developing country perspectives. Therefore, there is a pressing necessity to understand the dynamics of augmented reality adoption for the benefit of motivating and inspiring students to adopt this highly innovative and impactful type of technology in the learning process. Against this background, the authors proposed and tested a model based on integrating Task-Technology Fit (TTF) and UTUAT2 theories. The results reveal the positive effect of task technology fit, performance expectancy, effort expectancy, social influence, facilitating condition, and hedonic motivation on behavioral intention (BI) in the adoption process of augmented reality in educational settings, where price value is found to exert little influence on behavioral intention. This model explains 49% of the variance in intentional behavior to adopt AR technology in the educational context. The conclusions of this study will add to the literature more informative knowledge leading to increased awareness of the dynamics and behaviors of AR adoption in a developing country perspective. We present and discuss the theoretical contributions and practical implications of our findings.     

A dinuclear iron complex as a precatalyst for water oxidation under alkaline conditions

Water oxidation is a key reaction for water splitting. The decomposition of Fe-based-molecular structures toward Fe-based oxides is a promising method for oxygen-evolution reaction (OER) through water oxidation. The decomposition of Fe-based-molecular structures method results in a slow decomposition of precatalysts and forms Fe oxide-based catalysts. In this study, the Fe species formed through the decomposition of a dinuclear Fe(III) complex under OER is investigated by X-ray photoelectron spectroscopy, scanning electron microscopy, energy dispersive spectrometry, X-ray diffraction, and the electrochemical method. In addition, using Ni(OH)₂, a new approach is reported for detecting trace Fe species on the electrode surface. The resulting Fe oxide-based catalyst shows a catalytic current with an onset of 621 mV overpotential and the Tafel slope of 113.7 mV/decade at pH 11 in a buffer phosphate.     

Ambient electrosynthesis of NH3 from N2 using Bi-doped CeO2 cube as electrocatalyst

The synthesis of ammonia (NH₃) from electrochemical nitrogen reduction reaction (NRR) under environmental conditions is a promising technology. Compared with the traditional artificial nitrogen fixation process by the Haber-Bosch process, electrochemical nitrogen reduction reaction (NRR) requires no harsh reaction conditions. In this work, we report that Bi-doped CeO₂ nanocubes show high NRR activity as electrocatalysts. The NH₃ yield of 17.83 μgh⁻¹ mg⁻¹_cat. and the Faradaic Efficiency (FE) of 1.61% at −0.9 V are achieved in 0.1 M Na₂SO₄. The performance is much higher than that for the traditional CeO₂ nanoparticles. The detailed analysis indicates that both the Bi doping and the cube morphology are critical for this encouraging NRR performance. The mechanism for improving NRR is further explored with first-principle calculations, demonstrating the importance of Bi-doping for performance enhancement.     

Insights into discrepancy in power generation among glucose and malate grown Rubrivivax benzoatilyticus JA2 microbial fuel cells

In this study, Rubrivivax benzoatilyticus JA2^T (=ATCC BAA-35^T = JCM 13220^T = MTCC 7087^T), an anoxygenic photosynthetic bacterium, was subjected to altered conditions and observed for changes in power outcome in the two chambered microbial fuel cells (MFCs), the basis of which was established using metabolomic studies. This is an extension to our previous studies, which showed that, under photo heterotrophic conditions, glucose in the form of a solitary carbon resource in minimal media, caused the strain JA2 to exhibit altered growth rates, progressive loss of pigmentation and reduced cell size (3–4 μm), compared to malate grown cells (6–7 μm). When R. benzoatilyticus JA2 cells were grown in malate bio-anodes, they presented higher potentials (289.22 ± 4.6 mV or 436.22 OCV per mg dry weight) compared to glucose bioanodes (163 ± 5.5 mv or 188.98 OCV per mg dry weight). Insights from the metabolomic footprints and fingerprints have revealed differential regulation of key components in the central metabolic pathway such as fumarate, citrate and succinate, which are significantly increased in malate grown bio anodes. Strain JA2 cells when grown with malate as substrate are densely grown on the electrodes and exhibited reduced size, when observed under SEM, which contrasts with control cells grown on malate broth. The artificial selection pressure of the MFC and the different metabolic pathways followed by these bacteria are the reasons for such discrepancy in the power production by the strain JA2. These adaptations may indicate survival advantage during the electron transfer and growth in bio anodes. The study throws light on what types of effluents would be more suitable as substrates for R. benzoatilyticus JA2 microbial fuel cells.     

A new synthetic metamodel methodology for liquid‐propellant engine's cooling system optimization

The present paper strives for optimization of the cooling system of a liquid‐propellant engine (LPE). To this end, the new synthetic metamodel methodology utilizing the design of experiment method and the response surface method was developed and implemented as two effective means of designing, analyzing, and optimizing. The input variables, constraints, objective functions, and their surfaces were identified. Hence, the design and development strategy of combustion chamber and nozzle was clarified, and 64 different experiments were carried out on the RD‐161 propulsion system, of which 47 experiments were approved and compatible with the problem constraints. This engine used all three modes of cooling: the radiation cooling, the regenerative cooling, and the film cooling. The response surface curves were drawn and the related objective function equations were obtained. The analysis of variance results indicate that the developed synthetic model is capable to predict the responses adequately within the limits of input parameters. The three‐dimensional response surface curves and contour plots have been developed to find out the combined effects of input parameters on responses. In addition, the precision of the models was assessed and the output was interpreted and analyzed, which showed high accuracy. Therefore, the desirability function analysis has been applied to LPE's cooling system for multiobjective optimization to maximize the total heat transfer and minimize the cooling system pressure loss simultaneously. Finally, confirmatory tests have been conducted with the optimum parametric conditions to validate the optimization techniques. In conclusion, this methodology optimizes the LPE's cooling system, a 2% increase in the total heat transfer, and a 38% decrease in the pressure loss of the cooling system. These values are considerably large for the LPE design.