Highly Conductive Ultrafiltration Membrane via Vacuum Filtration Assisted Layer-by-Layer Deposition of Functionalized Carbon Nanotubes
Bibliographic record
Abstract
Conductive membranes can offer innovative solutions for membrane fouling control while maintaining enhanced filtration performance. Here, an emerging technique, vacuum filtration assisted layer-by-layer deposition of functionalized multiwalled carbon nanotubes (MWNTs), was used to prepare conductive surfaces on polysulfone (PSf) ultrafiltration membranes. PSf membranes were functionalized with oxygen-containing negatively charged functional groups through oxygen plasma treatment. MWNT-PSf membranes were prepared with 5, 10, 15, and 20 bilayers with amine- and carboxylic-functionalized MWNTs. The prepared membranes were characterized by the thickness, contact angle, and conductivity of the membranes. Scanning electron microscopy images of the membranes confirmed uniform MWNT distribution across the membrane surface. MWNT-PSf membranes exhibited slightly reduced permeability, improved selectivity, and greater conductivity with increasing number of MWNT bilayers and demonstrated almost complete inactivation of Escherichia coli at low applied DC potential (1–3 V). Furthermore, significant (around 99%) degradation of methyl orange during electrofiltration was observed, supporting an expected reduction in organic fouling of the membrane.
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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 | 0.001 |
| 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".