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Corrosion is a major issue in every industrial system. As a result of its widespread application, aluminum suffers enormous annual losses due to corrosion. Scientists are continually on the lookout for effective anti-corrosion strategies. Corrosion may be reduced in a number of ways, but many of them are harmful to the environment, so it's important to find a green alternative. Corrosion inhibitors in aluminum alloys can be found in green tea and tulsi extract. In this research, we found that aluminum alloy 1100 (Al-1100) ina 10% NaOH solution was inhibited by both green tea and Tulsi extract. Samples of AL alloy are submerged in 10% NaOH solutions with and without an inhibitor for a total of 25 days. The weight-loss technique is used to determine the effectiveness of an inhibitor, with tulsi extract far outperforming green tea with the best efficiency of 83.93% compared to the greatest efficiency of 14.29% for green tea. After being submerged in an inhibitory solution, an aluminum alloy surface developed an adsorbed protective layer, which is chemical adsorption, as seen by FTIR (Fourier-Transform Infrared Spectroscopy) spectroscopy. Green inhibitors those are present on the surface of the aluminum alloys are less corrosive confirmed by the SEM (Scanning Electron Microscopy) analysis. The chemical particles were found to be present as a coating over AL alloy surfaces, as determined by EDS (Energy Dispersion Spectroscopy) testing. In a10% NaOH solution, Al-1100 is inhibited more effectively by tulsi extracts than by green tea extracts.

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.