## 6 Summary and Outlook  
This review provides an overview of the electrocatalytic nitrate reduction (eNO3R) to nitrogen and ammonia for application to brackish groundwater. A comprehensive analysis of the eNO3R mechanism and the theoretical simulations by DFT were presentedto predict the reaction pathway of electrocatalysts for NH\({}_{3}\) production and N\({}_{2}\) selectivity. An overview of copper-based materials for eNO\({}_{3}\)R to ammonia as efficient transition metal-based catalysts is given in terms of single-atom copper catalysts, facet engineering, bimetallic, Cu oxide, Cu transition metal oxide composite, Cu-based inorganic/organic material, and Cu-based nanomaterials. Strategies to convert nitrate to nitrogen with iron-based catalysts specifically nanoscale zero-valent iron (nZVI) and strategies to convert ammonia generated with chlorine radical and active chlorine are discussed. A comprehensive analysis of eNO\({}_{3}\)R for brackish groundwater as a case study with the various operational parameters that influence the process with the challenge of moving towards large-scale application alongside an economic analysis was reported. This comprehensive analysis is essential for researchers and practitioners, as it provides critical information for the design and optimization of nitrate removal systems for this kind of natural water. eNO\({}_{3}\)R holds great promise for removing nitrate from contaminated water sources, including brackish groundwater, but it should be noted that this technology is still in its infancy and several challenges must be addressed. The following recommendations and information could be considered for the design and optimization of an efficient electrochemical cell unit and the selection of the preferred pathway for this type of water:  
* The mechanism for the eNO\({}_{3}\)R process is not well understood, which could be a reason to continue using DFT calculations. Farther, the complexity of the nitrate reduction process may make it difficult to accurately model using DFT. On the other hand, the DFT calculations require significant computational resources, which can be time-consuming and expensive. Additionally, DFT calculations rely on several approximations and assumptions, which may not always accurately reflect the real-world system. Despite these challenges, the use of DFT and microkinetic modeling can still provide valuable insights into the mechanism of electrocatalytic nitrate reduction and guide the tailored design of more efficient and selective electrode materials. * Machine learning (ML) and artificial intelligence (AI) play an increasingly important role in optimizing catalyst design, by enhancing the efficiency of DFT. These tools reduce the complexity of high-dimensional catalytic activity maps by identifying key descriptors, such as adsorption energies of critical species. This approach allows for more efficient screening of potential catalysts across vast chemical spaces, accelerating material discovery while significantly lowering the computational cost of DFT calculations. * Developing more efficient electrocatalysts for eNO\({}_{3}\)R is needed to find non-precious metal electrode materials that are highly efficient and durable, as the scarcity and cost of noble metals like Pd make them unsuitable for practical use. This requires extensive research and testing of various materials and modifications to improve their stability and durability, as well as their selectivity and activity for electrochemical nitrate reduction. Furthermore, advanced research in nanomaterials for Cu-nanomaterials or Fe-nanomaterials is required due to their operational low potential which reduces energy consumption for the process. * Brackish groundwater with high chloride concentration can be advantageous for nitrate conversion to nitrogen; an efficient anode of Ti/Ru-Ir is widely used for this purpose. However, by-product formation such as chlorine-containing byproducts (CBPs) can be the primary drawback, especially with water-containing organic compounds.