Bioinspired Polypeptide Dendrimer‐Modified Thin‐Film Composite Membranes for Selective Lithium‐Magnesium Separation with DFT Insights
Bibliographic record
Abstract
ABSTRACT Selective ion transport in nanofiltration (NF) enables sustainable lithium (Li + ) recovery. While many membranes rely on strong positive charge for Li⁺/Mg 2 ⁺ separation, we show that negatively charged membranes can also excel using a biomimetic approach. Inspired by biological ion channels that achieve cation selectivity via specific binding sites despite their negative charge, we designed a nitrogen‐rich polypeptide dendrimer (amino acid–based) bearing carboxylate coordination sites with higher affinity for Mg 2 ⁺ than Li⁺, while moderating the membrane's net negative charge. This biomimetic design enhanced Li + recovery by inhibiting Mg 2+ transport through stronger interactions, thereby allowing for preferential Li + permeation. This process occurred through a combination of electrostatic modulation and ligand‐assisted coordination. Density functional theory (DFT) calculations indicated strong oxygen‐donor coordination: lysine motifs bind hydrated Mg 2+ (E ≈ −170 kcal.mol −1 ) far more strongly than Li + (E ≈ −50.2 kcal.mol −1 ). The optimized membrane achieved Li + /Mg 2+ selectivity of 15.6 at neutral pH with 23 LMH flux, and 136 at pH 4, highlighting strong performance in acidic feeds. Long‐term tests showed ∼0.4% leaching over 10 days with stable rejection and enrichment of Li⁺ (feed Li⁺/Mg 2 ⁺ increased from 0.05 to 0.20). Antifouling tests showed a twofold lower flux‐decline ratio and higher flux‐recovery than the unmodified TFC.
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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) | 0.002 | 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".