Tailored l-Arginine modified Poly(piperazine-amide) nanofiltration membrane with enhanced water permeability for efficient Li+/Mg2+ separation
Bibliographic record
Abstract
In recent years, there has been a growing focus on lithium recovery from water resources using thin-film composite (TFC) nanofiltration (NF) membranes. In this study, a poly(piperazine-amide) NF membrane was fabricated via interfacial polymerization (IP) between piperazine (PIP) and trimesoyl chloride (TMC) to effectively separate Li + from Mg 2+ . However, the trade-off between water permeability and Li + /Mg 2+ selectivity presented a challenge, requiring additional surface modification to optimize performance. The surface of the poly(PIP-amide) membrane was modified with ARG amine-based hydrophilic monomer, followed by crosslinking with glutaraldehyde (GLA). The amine groups of ARG are expected to interact with the unreacted acyl chloride groups of TMC, increasing the positive surface charge and thereby improving Li + /Mg 2+ selectivity. The membrane modified with ARG and GLA (A2-G0.3) exhibited a significantly improved Li + /Mg 2+ selectivity of 17.11, compared to 5.14 for the unmodified membrane when tested in a solution containing 2000 ppm of salts (Li + /Mg 2+ of 1:20). Notably, the A2-G0.3 membrane demonstrated a Li + rejection of −45.4 and Mg 2+ rejection of 91.5 %, with a water flux of 47.0 Lm −2 h −1 at 70 psi. When tested with a simulated brine with a total salt concentration exceeding 21,000 ppm, the membrane exhibited a Li + rejection of 9.5 % and Mg 2+ rejection of 90.1 %, along with a water flux of 18.6 Lm −2 h −1 at low pressure of 70 psi. The membrane maintained consistent performance over 200 h of simulated feed filtration, demonstrating its long-term stability. Moreover, the antifouling performance of the membrane was greatly improved by grafting the ARG-GLA layer onto its surface. These findings highlight the modified membrane's potential for effective lithium recovery in high-salinity environments.
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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) | 0.001 | 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 | 0.001 | 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".