Ultrafiltration Membranes Functionalized with Polydopamine with Enhanced Contaminant Removal by Adsorption
Bibliographic record
Abstract
The performance of polymeric ultrafiltration membranes functionalized with polydopamine to couple depth adsorption of contaminants with the typical surface rejection characteristics of the membrane is investigated. Two approaches are deployed to achieve this functionalization: in a two‐step method, the ultrafiltration membranes are initially fabricated by phase inversion, then followed by coating with polydopamine; in a more facile and advantageous one‐step method, the membranes are subjected to phase inversion in a water solution containing dopamine, so that polymer precipitation and polydopamine functionalization occur at the same time. Methylene blue is used as the representative target contaminant to study the enhancement of membrane adsorption behavior, and its removal is investigated both in batch and under filtration conditions. The sorption capacity increases with increasing polydopamine coating time and is higher for the membranes fabricated via the one‐step protocol. The amount of methylene blue adsorbed per unit of membrane mass is large (roughly 5–10 mg g−1) and the kinetics of adsorption is fast. These characteristics allow these materials to get operated effectively and for long times in membrane filtration processes before stopping the system for regeneration. The saturated membranes are completely regenerated and reused without loss of performance by cycling in acidic and alkaline solutions. image
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".