Using multi-walled carbon nanotubes (MWNTs) for oilfield produced water treatment with environmentally acceptable endpoints
Why this work is in the frame
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Bibliographic record
Abstract
In this study, multi-walled carbon nanotubes (MWNTs) were employed to remove benzene, toluene, ethylbenzene, and xylenes (BTEX) from low and high salinity water pre-equilibrated with crude oil. The treatment endpoint of crude oil-contaminated water is often controlled by BTEX compounds owing to their higher aqueous solubility and human-health toxicity compared to other hydrocarbons. The MWNT sorbent was extensively characterized and the depletion of the organic sorbate from the produced water was monitored by gas chromatography-mass spectrometry (GC-MS) and total organic carbon (TOC) analyses. The equilibrium sorptive removal of BTEX followed the order: ethylbenzene/o-xylene > m-xylene > toluene > benzene in the presence of other competing organics in produced water. Sorption mechanisms were explored through the application of a variety of kinetics and equilibrium models. Pseudo 2(nd) order kinetics and Freundlich equilibrium models were the best at describing BTEX removal from produced water. Hydrophobic interactions between the MWNTs and BTEX, as well as the physical characteristics of the sorbate molecules, were regarded as primary factors responsible for regulating competitive adsorption. Salinity played a critical role in limiting sorptive removal, with BTEX and total organic carbon (TOC) removal falling by 27% and 25%, respectively, upon the introduction of saline conditions. Results suggest that MWNTs are effective in removing risk-driving BTEX compounds from low-salinity oilfield produced water.
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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 | 0.001 |
| 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 it