Efficient Lithium Recovery from Water Using Polyamide Thin-Film Nanocomposite (TFN) Membrane Modified with Positively Charged Silica Nanoparticles
Bibliographic record
Abstract
The separation of Li + from Mg 2+ in salt-lake brines using nanofiltration (NF) has become the most popular solution to meet the rising demand for lithium, particularly driven by the extensive use of lithium-ion batteries. This study presents the fabrication of a uniquely designed polyamide (PA) thin-film nanocomposite (TFN) membranes with ultrahigh Li + /Mg 2+ selectivity and enhanced water flux by covalently incorporating mixed ligands functionalized silica nanoparticles (F-SiO 2 NPs) into the selective PA layer and covalently bonding them to the membrane surface. In this strategy, bare silica nanoparticles (SiO 2 NPs) were functionalized with mixed superhydrophilic ligands, including primary amine and quaternary ammonium groups, resulting in a highly positive surface charge primarily from the quaternary ammonium groups and enabling covalent conjugation via amine groups. Among the F-SiO 2 NP-incorporated membranes, M500 containing 500 ppm of F-SiO 2 NPs exhibited the best performance. In a solution with 2000 ppm salt concentration (Li + /Mg 2+ ratio of 1:20), the M500 membrane showed an improved Li + /Mg 2+ selectivity of 7.41 compared to the nonmodified TFC membrane, which had a selectivity of 5.05. Further surface conjugation of the M500 sample with 1500 ppm of F-SiO 2 NPs resulted in the C1500 membrane, demonstrating the best performance among all of the surface-modified membranes. C1500 showed an outstanding Li + /Mg 2+ selectivity of 37.95, with a Mg 2+ rejection of 95.7% and a Li + rejection of −63.2%, and a water flux of 56.0 L m –2 h –1 at 70 psi. Notably, a 7.5-fold improvement in Li + /Mg 2+ selectivity over the TFC membrane was achieved without compromising the water flux. This is evident from the nearly identical water flux values of the TFC, M500, and C1500 membranes, which were 57.1, 54.8, and 56.0 L m –2 h –1, respectively. Considering key factors for large-scale applications, such as cost-effectiveness, environmental impact, the abundance of synthetic precursors, and the maturity of synthesis and tailoring technologies, SiO 2 NP-based modifications outperform all other reported approaches to date.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".