Development of superhydrophillic tannic acid-crosslinked graphene oxide membranes for efficient treatment of oil contaminated water with enhanced stability
Bibliographic record
Abstract
In the present age of industrialization, oil contamination in the waste water has become a huge global concern due to its several negative impacts on human health and aquatic ecosystem. In order to address this problem, a novel oleophobic and super-hydrophilic graphene-based membrane has been developed using simple and cost-effective vacuum filtration methodology. Prior developing the membranes, the graphene oxide (GO) sheets were crosslinked with tannic acid (TA) molecules in order to improve their mechanical and surface properties. To obtain the structural and morphological information of the membranes and their constituents, Field Emission Scanning Electron (FE-SEM) microscopy, X-Ray Diffraction (XRD), FTIR spectroscopy and Raman spectroscopy was used. When tested with simulated oilfield effluent samples, these membranes exhibited significant reduction in the values of chemical oxygen demand (COD), total dissolved solids (TDS), total suspended solids (TSS) and turbidity demonstrating low-oil adhesion and preferable oil rejection rates. Moreover, such crosslinked membranes are highly stable which can withstand the pressure of water filtration. In such a way, TA crosslinked GO membranes present a robust and efficient way to treat oil contaminated water released from various industries which can be reused for numerous further applications.
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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".