Using multi-walled carbon nanotubes (MWNTs) for oilfield produced water treatment with environmentally acceptable endpoints
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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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".