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.     

Laser beam melting of aluminum alloys with hull-core build strategy by using an initial meso-scale simulation

Laser beam melting (LBM) of aluminum alloys is gaining a wide popularity in different industrial applications as an alternative technology for the production of individual and complex parts. A long build time and the high amount of experimental work for optimizing or finding new process parameters are two of the current challenges for reaching an industrial maturity. This paper proposes an efficient way to determine new process parameters for aluminum alloy aluminum-silicon10-magnesium with highest build-up rates by using a 3D finite element model on the mesoscopic level. High laser power in combination with the hull-core build strategy was used to increase the build-up rate without impairing the part accuracy. The influences of high laser power, laser diameter and scan speed on the melt pool were studied by using a thermal simulation of single laser tracks. Based on the simulation results the process window could be derived and was tested on a laser beam melting (LBM) system. The achieved reduction of the build time of up to 31 % without loss in part accuracy proved the novel approach for the prediction of the required process window as an efficient method to reduce costly and time-consuming experimental work.     

Reliability-based fault analysis models with industrial applications: A systematic literature review

Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets-intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state-of-the-art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer-reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators.  A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co-citation network analysis. Later, a detailed content analysis of the top-100 most-cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability-based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset-intensive industry, researchers, and policymakers.     

BCLA CLEAR - Evidence-based contact lens practice

Evidence-based contact lens practice involves finding, appraising and applying research findings as the basis for patient management decisions. These decisions should be informed by the strength of the research study designs that address the question, as well as by the experience of the practitioner and the preferences and environment of the patient. This reports reviews and summarises the published research evidence that is available to inform soft and rigid contact lens history and symptoms taking, anterior eye health examination (including the optimised use of ophthalmic dyes, grading scales, imaging techniques and lid eversion), considerations for contact lens selection (including the ocular surface measurements required to select the most appropriate lens parameter, lens modality and material selection), evaluation of lens fit, prescribing (teaching self-application and removal, adaptation, care regimen and cleaning instructions, as well as minimising risks of lens wear through encouraging compliance) and an aftercare routine.