During the last decade, the demand for lithium has been growing exponentially with its widespread uses in manufacturing, especially with the worldwide deployment of electric vehicles. Thus, lithium is considered integral to the U.S. economy, technological advancement, and the pursuit of sustainable and clean energy solutions. The produced water is known to be rich in several minerals and valuable elements, such as potassium and magnesium, and a trace amount of precious elements including lithium. However, the existence of metals in the produced water is one of the challenges to extract lithium, especially magnesium which is located in diagonal positions within the periodic table that exhibit similarities. The produced water was first purified, resulting in the complete removal of magnesium, and then lithium was precipitated by phosphate. The effects of operating conditions on the Li₃PO₄ precipitation behaviors were evaluated. The effect of different precipitating reagents was evaluated on the percentage of lithium extracted, Na₃PO4 (TSP) was found to be a promising Li precipitating reagent. The highest percentage of lithium extracted was reached when the Li concentration of produced water was enriched and increased up to 350 mg/l, then Li removal was 53% at 7 grams of TSP and 200 rpm for 2 hours. The results indicated that temperature is a more important factor than stirring speed. The novelty of the current work is not only by the results obtained that may create additional financial benefits to oil-producing areas but on that the sustainable disposal of produced water may encourage the recycling and reuse practice, ultimately reducing the use of freshwater for hydraulic fracturing.

Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

This paper addresses the pressing concern of corporate tax evasion recognized by several nations, employing insights from public choice theory and tax compliance theory. The research aims to evaluate the ramifications of corporate tax evasion on public revenue, public expenditures, tax rates, and the prevailing budget deficit. Methodologically, the study utilizes questionnaire data and employs structural equation modeling for analysis. The findings indicate that tax evasion is significantly influenced by multiple factors, encompassing tax income, public expenditures, tax rates, and the budget deficit. The article offers a comprehensive exploration of both theoretical frameworks and practical implications, contributing valuable insights to the ongoing discourse on corporate tax evasion and its multifaceted impact on fiscal dynamics.