Ultrathin Graphene Oxide Nanoribbon Networks as Architects of Enhanced Performance in Polyamide‐Based Nanofiltration Membranes
Bibliographic record
Abstract
Abstract Customized architecture and chemistry play a pivotal role in conferring exceptional permeability and selectivity to polyamide (PA) membranes for desalination and ionic separation. Herein, a new interfacial polymerization (IP) template, the ultrathin graphene oxide nanoribbon (GONR) networks, is developed to meet the need for minimizing the funnel effect and mediating the IP reaction toward a highly permeable and selective membrane. The coated GONR template efficiently represents the gutter layer role and regulates the adsorption and transport of amine monomers at the GONR interface, which is studied by molecular simulation as well. The structure, electrostatic interaction, capillary rise, and nanoconfinement of the IP template are manipulated by different GONR loadings to optimize the membrane structure. The optimized GONR loading at 0.02 g m −2 results in a hybrid layered GONR/PA‐thin‐film‐composite nanofiltration membrane with nanostrip crumpled structure beyond the PA context, ultrathin 15 nm PA nanofilm, 80% cross‐linking degree, and narrow pore size distribution. The membrane passes the upper bound trade‐off with a permeance of 21.3 L m −2 h −1 bar −1 and a remarkable rejection of 98% for Na 2 SO 4 . This research offers a fresh perspective on comprehensively understanding the role of the IP template in creating a desired membrane for efficient desalination and ionic separation.
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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) | 0.000 | 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 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".