Enhanced Proton-Selective Hybrid Polybenzimidazole/Perfluorosulfonic Acid Membranes for Acid Recovery from Lithium Battery Leachate Using Electrodialysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Proton-selective membranes present a promising solution for improving the efficiency and sustainability of acid recovery in the hydrometallurgical recycling of lithium-ion batteries (LIBs). This study introduces a hybrid cation exchange membrane developed by in situ modification of a commercial perfluorosulfonic acid (PFSA) membrane with polybenzimidazole (PBI) for efficient acid recovery using electrodialysis (ED). The modified membranes demonstrated exceptional proton selectivity and stability, achieving selectivity ratios of 770 (H + /Li + ) and 606 (H + /Co 2+ ), surpassing reported values in the literature. In 150 min of electrodialysis, the optimum membrane composite (PFSA-113_PBI-3%) achieved up to 80% acid recovery from synthetic leachates containing H +, Li +, Mn 2+, Co 2+, and Ni 2+ . Outstanding separation factors of up to 86 for H + /Li + and 10 4 for H + / d -Metal 2+, alongside a current efficiency of 95%, were also obtained with the optimum membrane. The enhanced proton selectivity was attributed to the hydrogen-bond networks and ionic interactions resulting from salt bridges between PBI and polymer acidic groups from the formation of a PBI/PFSA interpolymer complex. This was confirmed through membrane structural analysis using Raman and FTIR spectroscopy, electron microscopy, and small-angle X-ray scattering. The separation mechanism of the modified membrane was found to resemble that of biological membranes, as confirmed through carefully designed methylation tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".