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Electrochemical nitrate reduction reaction (NO3RR) has garnered increasing attention as a pathway for converting a harmful pollutant (nitrate) into a value‐added product (ammonia). However, high selectivity toward ammonia (NH3) is imperative for process viability. Optimizing proton availability near the catalyst is important for achieving selective NH3 production. Here, the aim is to systematically examine the impacts of proton availability on NO3RR selectivity in a bipolar membrane (BPM)‐based membrane electrode assembly (MEA) system. The BPM generates a proton flux from the membrane toward the catalyst during electrolysis. Thus, the BPM‐MEA system can modulate the proton flux during operation. The impact of interposer layers, proton scavenging electrolytes (CO32−), and catalyst configurations are also examined to identify which local microenvironments favor ammonia formation. It is found that a moderate proton supply allows for an increase in ammonia yield by 576% when compared to the standard MEA setup. This also results in a high selectivity of 26 (NH3 over NO2−) at an applied current density of 200 mA cm−2. [ABSTRACT FROM AUTHOR]

Water electrolyzers powered by renewable energy are emerging as clean and sustainable technology for producing hydrogen without carbon emissions. Specifically, anion exchange membrane (AEM) electrolyzers utilizing non‐platinum group metal (non‐PGM) catalysts have garnered attention as a cost‐effective method for hydrogen production, especially when integrated with solar cells. Nonetheless, the progress of such integrated systems is hindered by inadequate water electrolysis efficiency, primarily caused by poor oxygen evolution reaction (OER) electrodes. To address this issue, a NiFeCo─OOH has developed as an OER electrocatalyst and successfully demonstrated its efficacy in an AEM electrolyzer, which is powered by renewable electricity and integrated with a silicon solar cell. [ABSTRACT FROM AUTHOR]

This study compares choice factors between online education suppliers and consumers. While research on online education's effectiveness in public institutions is extensive, there is a scarcity of studies comparing choice factors in the online education market. To bridge this gap, a survey was conducted among paid online learners to understand their intentions, preferred areas of study, and engagement levels. Data were analysed using SPSS 25.0, including frequency analysis, validity assessments, and multiple regression analysis to examine relationships between choice factors and satisfaction. Supplier choice factors included education costs and location, while consumer choice factors encompassed instructor expertise, content quality, and education effectiveness. This study's results offer valuable insights into the online education market, highlighting distinct priorities for suppliers and consumers. This research contributes to a better understanding of the online education landscape, providing critical insights for suppliers and policymakers to tailor offerings, improve quality, and optimize online education's effectiveness. Acknowledging consumer preferences and continuously enhancing services are crucial in meeting learners' evolving needs in this dynamic sector. [ABSTRACT FROM AUTHOR]

Fat-soluble vitamins (vitamin A, D, E, and K) assume a pivotal role in maintaining human homeostasis by virtue of their enzymatic functions. The daily inclusion of these vitamins is imperative to the upkeep of various physiological processes including vision, bone health, immunity, and protection against oxidative stress. Current research highlights fat-soluble vitamins as potential therapeutics for human diseases, especially cancer. Fat-soluble vitamins exert their therapeutic effects through multiple pathways, including regulation of matrix metalloproteinases' (MMPs) expression and enzymatic activity. As MMPs have been reported to be involved in the pathology of various diseases, such as cancers, cardiovascular diseases, and neurological disorders, regulating the expression and/or activity of MMPs could be considered as a potent therapeutic strategy. Here, we summarize the properties of fat-soluble vitamins and their potential as promising candidates capable of effectively modulating MMPs through multiple pathways to treat human diseases. [ABSTRACT FROM AUTHOR]

The development of highly active and durable Ir‐based electrocatalysts for the acidic oxygen evolution reaction (OER) is challenging because of the corrosive anodic conditions. Herein, IrOx/Zr2ON2 electrocatalyst is demonstrated, employing Zr2ON2 as a support material, to overcome the trade‐off between the activity and stability in the OER. Zr2ON2 is selected due to its excellent electrical conductivity and chemical stability, and the fact that it induces strong interactions with IrOx catalysts. As a result, IrOx/Zr2ON2 electrocatalysts exhibit outstanding OER performances, reaching an overpotential of 255 mV at 10 mA cm−2 and a mass activity of 849 mA mgIr−1 at 1.55 V (vs the reversible hydrogen electrode). The activity of IrOx/Zr2ON2 is maintained at 10 mA cm−2 for 5 h, while in contrast, IrOx/ZrN and an unsupported IrOx catalyst undergo drastic degradation. Combined experimental X‐ray analyses and theoretical interpretations reveal that the reduced oxidation state of Ir and the extended IrO bond distance in IrOx/Zr2ON2 effectively increase the activity and stability of IrOx by altering reaction pathway from a conventional adsorbate evolution mechanism to a lattice oxygen‐participating mechanism. These results demonstrate that it is possible to effectively reduce the Ir content in OER catalysts through interface engineering without sacrificing the catalytic performance. [ABSTRACT FROM AUTHOR]

Identifying the patterns of defective chips in wafer bin maps (WBMs) in semiconductor manufacturing processes is crucial because different defect patterns correspond to different root causes of process failures. Recently, mixed-type defect patterns (i.e., multiple defect patterns in a single wafer) have become increasingly common owing to the increased complexity of semiconductor manufacturing processes. Previous methods for classifying mixed-type defect patterns in WBMs focused on outputting only the class labels of the defect patterns and not their locations, although location information of the defect patterns is useful for tracking the root causes of failure and improving processes. Moreover, most previous methods used only labeled WBM data, although a larger quantity of unlabeled WBM data are more accessible because of the costly process of label annotation. Therefore, in this paper, we propose a semi-supervised learning method for classifying mixed-type defect patterns and detecting their locations simultaneously using both labeled and unlabeled WBM data. The proposed method extends a recent unsupervised object detection method called Attend-Infer-Repeat in a semi-supervised manner to perform object detection and classification simultaneously. The performance of the proposed method is verified using WBM datasets of different sizes. The results demonstrate the effectiveness of the proposed method for classification and location detection. [ABSTRACT FROM AUTHOR]