Electrochemical Poly(vinylidene fluoride) (PVDF) Membranes Using Polyethylenimine Cross-Linked Polydopamine-Bound Carbon Nanotubes
Bibliographic record
Abstract
Electrochemical membranes (ECMs) are an emerging multifunctional separation technology that enables simultaneous contaminant filtration and reaction due to their electrically conductive porous surface coatings. Physical coating stability remains a technical challenge for ECMs, which are largely based on carbonaceous nanomaterials or metallic thin films. In this research, binding chemistry based on polydopamine (PDA) and polyethylenimine (PEI) was developed to prepare physically stable ECMs. Poly(vinylidene fluoride) (PVDF) ultrafiltration membranes were coated with PEI cross-linked PDA followed by the deposition of carboxyl-functionalized single/double-walled carbon nanotubes (SW/DWCNTs-COOH). Fabricated membranes were characterized for their structural, physicochemical, electrochemical, and separation properties. In a batch electrochemical system, the membranes achieved >99.2% electrochemical reduction of methyl orange (MO) in 120 min. Results revealed that the PDA/PEI intermediate layer can significantly enhance the adhesion between the SW/DWCNTs and the underlying polymer membrane without a substantial reduction (<10%) in water permeability. Response surface methodology (RSM) was employed to optimize the permeability and surface electrical conductivity of ECMs by studying the influence of PDA concentration, PEI concentration, and the branched-amine content of PEI in the coating solution. RSM analysis demonstrated two factorial interactions between PDA and PEI concentrations, as well as PEI concentration and PEI branch molecular weight. Our optimization study revealed that the use of a 1:1 ratio of PDA/PEI at low concentrations (∼2 mg/mL) and high PEI branch M w (∼1200) was the ideal preparation condition within the tested design space to maximize both the water permeability (∼895 L/m 2 /h/bar) and electrical conductivity (∼29,761 S/m). This optimized cross-linking chemistry demonstrates the ability to make practical and physically stable ECMs on chemically inert PVDF membranes, expanding the range of membranes that can be used to create electrochemical membranes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".