A microbubble nucleated due to the absorption of a tightly focused laser at the interface of a liquid−solid substrate enables directed and irreversible self-assembly of mesoscopic particles dispersed in the liquid at the bubble base. This phenomenon has facilitated a new microlithography technique which has grown rapidly over the past decade and can now reliably pattern a vast range of soft materials and colloids, ranging from polymers to metals to proteins. In this review, we discuss the science behind this technology and the present state-of-the-art. Thus, we describe the physics of the self-assembly driven by the bubble, the techniques for generating complex mesoarchitectures, both discrete and continuous, and their properties, and the various applications demonstrated in plastic electronics, site-specific catalysis, and biosensing. Finally, we describe a roadmap for the technique to achieve its potential of successfully patterning "everything" mesoscopic and the challenges that lie therein.

Water oxidation has become very popular due to its prime role in water splitting and metal–air batteries. Thus, the development of efficient, abundant, and economical catalysts, as well as electrode design, is very demanding today. In this review, we have discussed the principles of electrocatalytic water oxidation reaction (WOR), the electrocatalyst and electrode design strategies for the most efficient results, and recent advancement in the oxygen evolution reaction (OER) catalyst design. Finally, we have discussed the use of OER in the Oxygen Maker (OM) design with the example of OM REDOX by Solaire Initiative Private Ltd. The review clearly summarizes the future directions and applications for sustainable energy utilization with the help of water splitting and the way forward to develop better cell designs with electrodes and catalysts for practical applications. We hope this review will offer a basic understanding of the OER process and WOR in general along with the standard parameters to evaluate the performance and encourage more WOR-based profound innovations to make their way from the lab to the market following the example of OM REDOX.

Anomaly detection plays a critical role in large-scale industrial systems, where real-time streaming data is generated at high volumes. This research journal presents an in-depth study on developing an efficient anomaly detection algorithm specifically designed for such industrial systems. The goal is to identify abnormal patterns and deviations from normal behavior, enabling proactive maintenance, improved operational efficiency, and reduced downtime. The proposed algorithm leverages machine learning and stream processing techniques to handle the challenges associated with real- time streaming data analysis. The research covers algorithm design, implementation, evaluation, and performance analysis using large-scale datasets from industrial domains. Keywords: anomaly detection, real-time streaming data, large-scale industrial systems, machine learning, stream processing

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