Exploring the twin potential of nanostructured TiO2:SeO2 as a promising visible light photocatalyst and selective fluorosensing platform.

The present work describes the development of TiO2/SeO2 nanostructure as a potential candidate for visible light photocatalysis as well as selective fluorophore for the sensing of picric acid. The obtained nanostructure consists of uniform globular nanoparticles having approximate size of 170 nm and possess an optical band gap of 2.33 eV with absorption maxima at 473 nm. The photocatalyst was able to achieve 90.34% degradation efficiency for 2, 4-dichlorophenol (2,4-DCP) with rate constant of 0.0046 min-1 in the visible region. Further the nanostructure was able to serve as a selective fluorophore for sensing of Picric acid portraying more than 95% of fluorescence quenching when the concentration of PA is 10-4 M. Theoretical calculations indicate the interaction of organic pollutants with the nanostructure and reveal that both picric acid (- 66.21 kcal/mol) and 2,4-DCP (- 12.31 kcal/mol) possess more negative binding energy values demonstrating a strong interaction of both with the nanostructure, making it suitable for the degradation as well as sensing of organic pollutants. Thus this study explains the potential of prepared catalyst for waste water treatment.

A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning.

Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.

Accumulation of pollutants from fly ash in Pleurotus ostreatus and a substrate based on coffee grounds by elemental analysis using the ICP-OES method and photometric method.

The substrate mixtures were created in the study, using spent coffee grounds for Pleurotus ostreatus cultivation with the addition of straw and fluidized bed ash at 5 and 10 percent by weight relative to the total weight of coffee grounds. In order to determine the ability to accumulate heavy metals and the possibility of further waste management, analyses of micro- and macronutrients, biogenic elements, as well as the metal content of fungal fruiting bodies, mycelium and post-cultivation substrate were performed. The addition of 5 percent resulted in slower growth of mycelium and fruiting bodies, and with the addition of 10 percent, the growth of fruiting bodies was completely inhibited. The accumulation of elements such as (Cr), (Cu), (Ni), (Pb) and (Zn) was reduced in the fruiting bodies grown on the substrate with the addition of 5 percent fly ash, compared to spent coffee grounds without additives.

Impact of organizational learning on sustainable firm performance: Intervening effect of organizational networking and innovation.

This research has analyzed the role of learning in an organization while measuring and managing sustainable organizational performance. Furthermore, our research has also included the intervening role of organizational networking and organizational innovation while analyzing the relationship between organizational learning and sustainable organizational performance. Our research has adopted a quantitative approach while using the survey method to collect data from 710 owners of the manufacturing sector belonging to the Small and Medium Enterprises SMEs operating in Laos. Informed consent was obtained from all participants for your research. Structure equation modeling SEM was used through partial least square PLS software to test the collected data's reliability and validity and test the hypothesis to meet the research objectives. The study's findings reveal that organizational learning is vital to organizational performance and success. Information sources (networks) moderate the relationship between innovation and organizational performance. Our findings confirm that innovation is disruptive if it is not well-informed and well-processed. The research concludes that organizational learning is very vital for sustainable organizational performance. The current research contributes to the body of knowledge by examining sustainable organizational performance from an entirely different perspective.