Superhydrophilicity and underwater superoleophobicity graphene oxide-micro crystalline cellulose complex-based mesh applied for efficient oil/water separation
Bibliographic record
Abstract
ABSTRACT With the aggravation of the pollution in our environment, the novel interface materials have gained more and more attention. In this work, we reported an approach to prepare graphene oxide-microcrystalline cellulose complex-based mesh with superhydrophilicity and underwater superoleophobicity. Hydrophilic groups were grafted to enhance the superhydrophilicity and improve the superoleophobicity at the same time. The modified mesh exhibited under water superoleophobicity with oil contact angle more than 150°, while water can quickly permeate the wire mesh surface with a contact angle value of 0°. In the process of oil/water separation tests, water molecules permeate the wire mesh under the force of gravity, and the oil was retained on the mesh. Thus, the oil/water mixtures with different proportions can be separated in a high efficiency up to 99.1%. In addition, our superhydrophilic meshes are robust in harsh water conditions and can be used as an efficient filtration membrane. And our superhydrophilic meshes still have high efficiency separation in poor conditions, such as alkaline,acidic or saline solutions. Thus, this modified mesh could be an ideal choice for practical oil/water separation which can satisfy the need for water body restoration.
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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) | 0.001 | 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".