Ionomer‐coated filtration membranes as an alternative to ion‐exchange membranes for demineralization by electrodialysis
Bibliographic record
Abstract
Abstract Industrial application of ion‐exchange membranes, the key component of many electro‐membrane processes, is often hindered due to their high cost—result of complex fabrication methods and use of highly engineered precursors. As an alternative to conventional anion‐exchange membrane (AMX), this study focuses on the fabrication of cation‐coated filtration membranes (CCFM‐Type 1 and CCFM‐Type 2) by depositing a thin layer of crosslinked branched polyethyleneimine (PEI)‐based ionomer on top of the porous substrate. The use of commodity precursors (PEI) in combination with energy efficient fabrication processes ensures a low end‐cost for CCFMs. The properties and the performances of these membranes are evaluated and compared with AMX. Though ionic conductance of both CCFMs is found to be lower than AMX, similar electrochemical behavior is observed for all three membranes. In terms of performance, the highest NaCl demineralization rate is given by CCFM‐Type 2, while whey demineralization rate for all three membranes is similar. Energy consumption during both processes is increased for CCFMs probably due to noticeably higher global resistance which the ED system reaches during the process. These promising results suggest the possible application of these cost‐effective CCFMs in electrodialysis demineralization processes as an alternative to commercial AMX at an industrial level.
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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) | 0.001 | 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".