Polyamide thin film nanocomposite membranes modified with cationic nanogel for efficient Li+/Mg2+ separation
Why this work is in the frame
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Bibliographic record
Abstract
Lithium-ion batteries are central to the energy transition; however, the limited and unstable supply of lithium remains a significant bottleneck for scaling electric vehicles and energy storage systems. To meet the growing demand for lithium, developing efficient and environmentally sustainable technologies for extracting Li + from salt-lake brines is essential. In this study, nanofiltration (NF) membranes with ultra-high Li + /Mg 2+ selectivity and enhanced water permeability were fabricated by coating the surface of polyamide (PA) thin-film co mposite (TFC) membranes with a cationic nanogel, poly(N-isopropylacrylamide-co-N-(3-aminopropyl) methacrylamide hydrochloride) [p(NIPAM-co-APMAH)]. The nanogel was synthesized via free radical polymerization using N-isopropylacrylamide (NIPAm) and N-(3-aminopropyl) methacrylamide hydrochloride (APMAH) as monomers, and N , N ′-methylenebis(acrylamide) (BIS) as the crosslinker. Density functional theory (DFT) and zeta potential results indicated that modifying the membrane with a cationic nanogel reduced the surface negativity of the membrane. In a 2000 ppm salt solution with a Li + /Mg 2+ ratio of 1:20, the optimized membrane (M500 containing 500 ppm nanogel) achieved a Li + /Mg 2+ selectivity of 31 and a water flux of 54.3 L m −2 h −1 , representing a fivefold increase in selectivity and a 42 % improvement in water flux over the unmodified membrane. These findings demonstrate the successful decoupling of the conventional selectivity-permeability trade-off in NF membranes, highlighting a promising pathway for high-performance Li + extraction from salt-lake brines.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it