Interfacially Cross-Linked Polydopamine/Polybenzimidazole Composite Membranes for Organic Solvent Nanofiltration
Bibliographic record
Abstract
-xylene (DBX) was employed as a cross-linking agent to make the composite membranes chemically stable against harsh polar aprotic solvents. The interfacial cross-linking of PDA/PBI allowed for finely tuning the molecular weight cutoff (MWCO) of the membrane, resulting in a membrane with precise molecular separation capabilities for OSN. The morphology and surface properties of the membranes were characterized, and a membrane with a MWCO of 286 Da was investigated for OSN of a series of solvents. The membrane permeance was in the order of acetonitrile (MeCN) > methanol (MeOH) > acetone > toluene > dimethylformamide (DMF) > heptane > ethanol (EtOH) > isopropanol (IPA) > tetrahydrofuran (THF). The membranes displayed a sharp pore size distribution, yielding a rejection rate of over 99% for Rose Bengal (RB, MW 1020 g/mol) and Remazol brilliant blue (RBB, MW 626.5 g/mol) from DMF and EtOH solutions. When it came to methyl orange (MO, MW 327.3 g/mol) that had a molecular weight closer to the MWCO of the membrane, the membrane still displayed a high rejection rate of 95% and 99% in nanofiltrating solvents DMF and EtOH, respectively. In addition, it was demonstrated that the membrane was able to effectively fractionate mixed solutes having molecular weights appropriate for the MWCO rating of the membrane during OSN.
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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 | 0.000 | 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".