Projects per year
Abstract
Nitrate contamination in water sources is a growing environmental concern, threatening both human health and ecosystems. However, its combination with sodium forms NaNO3, 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 NaNO3. Molecular dynamics simulations are conducted to systematically investigate the effects of polymer block ratio and concentration, as well as salt concentration, on NO3− 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.
Original language | English |
---|---|
Article number | 162346 |
Pages (from-to) | 1-9 |
Number of pages | 9 |
Journal | Chemical Engineering Journal |
Volume | 513 |
DOIs | |
Publication status | Published - 1 Jun 2025 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Membrane
- Molecular dynamics simulation
- Polyacrylamide
- Reverse osmosis
- Sodium nitrite
- Water purification
Fingerprint
Dive into the research topics of 'Block ratio optimized cationic polyacrylamides for enhanced nitrate rejection under applied potential'. Together they form a unique fingerprint.-
GreenDigi/Ala-Nissilä: Experimental and Artificial-Intellience-Based Modeling of Optimal Effiency for Renewable Long-Term Heat Storages
Ala-Nissilä, T. (Principal investigator)
EU The Recovery and Resilience Facility (RRF)
01/01/2023 → 31/12/2025
Project: RCF Academy Project targeted call
-
Finnish Centre of Excellence in Quantum Technology
Ala-Nissilä, T. (Principal investigator)
01/01/2018 → 31/12/2020
Project: Academy of Finland: Other research funding