Self-Cleaning Nanoscale Coating for the Separation of Oil–Water Mixture
Bibliographic record
Abstract
The current study pertains to the self-cleaning nanoscale coating of graphene/polymer nanohybrid system for the separation of oil–water mixture. Using a single nanohybrid system, we have developed a two-stage process for the selective removal of the hydrocarbons. Total petroleum hydrocarbons (TPHs) and separation efficiency are determined by gas chromatography-flame ionization detector (GC-FID). Analysis of the polycyclic aromatic hydrocarbons (PAHs) in the two stage samples is carried out by using gas chromatography coupled to mass spectrometry (GC-MS). The analysis shows the reduction in TPHs from 290 ppm to less than 1 ppm and more than 99.5% separation efficiency (oil removal from oil–water mixture) is achieved. No polycyclic aromatic hydrocarbons (PAHs) were detected in the treated water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".