posted on 2025-10-30, 11:28authored byHossein Vahid, Arsalan Hashemi, Mohammad Khavani, Abhinav Sharma, Mohammad RK Mofrad, Tapio Ala-NissilaTapio Ala-Nissila
<p dir="ltr">Nitrate contamination in water sources is a growing environmental concern, threatening both human health and ecosystems. However, its combination with sodium forms NaNO<sub>3</sub> , a compound essential for various industrial applications. The integration of charged polymers and reverse osmosis (RO) membranes presents a promising approach to capture undesired ions and enhance water purification efficiency. In this paper, di-block cationic polyacrylamides (DCPAMs) as charged polymers are evaluated separately in bulk solution and in combination with the RO process to capture nitrite ions introduced by NaNO<sub>3</sub>. Molecular dynamics simulations are conducted to systematically investigate the effects of polymer block ratio and concentration, as well as salt concentration, on NO<sub>3</sub><sup>-</sup> capturing. Our results in bulk solution indicate that an optimal block ratio of 8:12 yields the highest performance, with polymers adopting a stretched conformation. When an electric potential is applied, anions are strongly attracted to the positively charged electrode, and nitrate ions remain closer to the electrode surface than other ions. Our findings reveal that a 12:8 ratio outperforms all other ratios. The simultaneous application of RO membranes and DCPAMs achieves salt rejection efficiencies ranging from 78% to 100%, depending on DCPAM type and salt concentration. These findings pave the way for further computational studies on combined processes to advance water purification technologies.</p>
Funding
The Finnish Cultural Foundation, Finland supported this work under grant no. 00241182
The Academy of Finland, Finland through its QTF Center of Excellence program (Project No. 312298)
The European Union – NextGenerationEU instrument by the Academy of Finland grant 353298
The Technology Industries of Finland Centennial Foundation, Finland TT2020 grant