Development of a robust ion-selective membrane from sulfonated cellulose nanofibers for zinc–iodine redox flow batteries
Bibliographic record
Abstract
Electrochemical cells play a critical role across various sectors, enabling technologies in renewable energy generation and storage, environmental monitoring, and management. Among different options, redox flow batteries (RFBs) are gaining importance for large-scale energy storage applications due mainly to their decoupled power and energy outputs associated with easily scalable design. As part of most acidic and neutral RFBs, proton exchange membranes (PEMs) are essential for transport of cations, preventing the mixing of anolyte and catholyte while still allowing high ion conductivity. However, many commercial PEMs are made of poly and perfluoroalkyl substances (PFAS) and besides being expensive and petroleum feedstock-derived, they raise significant environmental concerns. In this work, we developed high-efficiency sulfonated nanocellulose membranes (S-CNF) incorporating layer-by-layer assembly silanization modification. The S-CNF membranes exhibited excellent thermal-oxidative stability (up to 250 °C), dimensional stability, and mechanical strength (Young’s modulus reaching 1.4 GPa and storage modulus exceeding 1.1 GPa). Additionally, they demonstrated high moisture-uptake (absorbing up to 90% water after 48 h). A zinc-iodine redox flow battery (ZIRFB) assembled with S-CNF membranes achieved an average coulombic, voltage and energy efficiencies of 98%, 66%, and 65%, respectively, at a current density of 20 mA cm -2 —comparable to commercially available RFBs. Overall, the S-CNF membranes developed in this study offer a bio-based, simple, cost-effective, and durable alternative to conventional PEMs, with promising scalability for energy storage applications in ZIRFBs.
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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 | 0.001 | 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".