Production of Stable Electrically Conductive PVDF Membranes Based on Polydopamine-Polyethyleneimine—Assisted Deposition of Carbon Nanotubes
Bibliographic record
Abstract
Electrically conductive membranes (ECMs) have emerged as a multifunctional separation technology that integrates membrane filtration with electrochemical reactions. Physical stability remains a critical challenge for ECMs synthesized by coating polymer membranes with conductive materials. In this article, polydopamine (PDA) and polyethyleneimine (PEI) were used to facilitate the synthesis of significantly more stable ECMs using poly(vinylidene fluoride) (PVDF) ultrafiltration membranes and carbon nanotubes (CNTs). Four different synthesis methods were compared in terms of the final surface stability and separation properties: (1) CNTs deposited on PEI-crosslinked PDA-coated PVDF membranes, (2) PEI-crosslinked CNTs deposited on PDA-coated PVDF, (3) PDA, PEI and CNTs sequentially deposited layer-by-layer on PVDF, and (4) PEI-crosslinked PDA deposited on CNT-coated PVDF. The results revealed that method 1 generated ECMs with the greatest physical stability, highest electrical conductivity (18,518 S/m), and sufficient permeability (395.2 L/(m2·h·bar). In comparison, method 2 resulted in membranes with the highest permeability (2128.5 L/(m2·h·bar), but with low surface conductivity (502 S/m) and poor physical stability (i.e., 53–75% lower peel-off forces compared to other methods). Overall, methods 1, 3, and 4 can be used to make highly conductive membranes with a 97–99% removal of methyl orange by electrochemical degradation at −3 V.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".