Pressure‐Driven Solvent Transport and Complex Ion Permeation through Graphene Oxide Membranes
Bibliographic record
Abstract
Abstract In this paper, an in‐depth investigation of three graphene oxide (GO) based membranes—pure GO, Al3+ intercalated GO (Al‐GO), and poly(ethylene glycol) (PEG) modified GO (PEG‐GO)—is presented. Both Al‐GO and PEG‐GO membranes have wider interlayer d‐spacing compared to pure GO, and the d‐spacing size correlates well to the cross‐membrane water flux with JPEG‐GO > JAl‐GO > JGO. Pressure‐driven transport of water/ethanol mixtures across all three types of GO membranes is dominated by solvent viscosity—not solvent polarity showing distinctively semi‐hydrophilic membrane characteristics. Interestingly, the results suggest that both ethanol cluster size and molecular geometry contribute to preferential ethanol rejection, indicating that both GO and Al‐GO membranes possess superior size sieving capability. Further, the lower permeation of tris(1,10‐phenanthroline)ruthenium(II) (Ru(phen)32+) compared to the charge‐equivalent smaller‐sized tris(bipyridine)ruthenium(II) (Ru(bpy)32+) demonstrates the excellent steric selectivity of GO membranes. Compared to pure GO, the widened d‐spacing in PEG‐GO allows ≈100% higher ion permeation while ion flux through Al‐GO is an order of magnitude lower, suggesting the significant role of electrostatic interaction in ion transport. In conclusion, these findings ought to enrich the understanding of the GO‐based membranes and enable future rational designs for a wide range of applications, including water purification and solvent separation.
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 | 0.000 | 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".