Selective protein transport through ultra-thin suspended reduced graphene oxide nanopores
Bibliographic record
Abstract
The nanoporous free-standing graphene membrane is of great interest in high performance separation technology. In particular, the separation of biological molecules with similar sizes is one of the key challenges in the purification of biomaterials. Here, we report a reliable, cost-effective, and facile method for the fabrication of a graphene-based nanosieve and its application in the separation of similar-size proteins. A suspended reduced graphene oxide (rGO) nanosieve with ultra-thin, large-area, well-ordered, and dense 15 nm-sized pores was fabricated using block copolymer (BCP) lithography. The fabricated 5 nm-ultrathin nanosieve with an area of 200 μm × 200 μm (an ultra-high aspect ratio of ∼40 000) endured pressure up to 1 atm, and effectively separated hemoglobin (Hb) from a mixture of hemoglobin and immunoglobulin G (IgG), the common proteins in human blood, in a highly selective and rapid manner. The use of the suspended rGO nanosieve is expected to provide a simple and manufacturable platform for practical biomolecule separation offering high selectivity and a large throughput.
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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".