Solid phase extraction of polyhalogenated pollutants from freshwater using chemically modified multi‐walled carbon nanotubes and their determination by gas chromatography
Bibliographic record
Abstract
This paper describes the application of pristine and chemically modified multi-walled carbon nanotubes (MWCNTs) as packing materials for the preconcentration and determination of various polyhalogenated organic pollutants, pentachlorophenol, 2,4,5-trichlorophenol, 3,3',4,4'-tetrachlorobiphenyl, and 2,2',5,5'-tetrabromobiphenyl from real water samples based on solid-phase extraction. MWCNTs were chemically modified by octadecyl amine and polyethylene glycol, separately, and the resulting nano materials were used as packing materials for solid phase extraction. Method development, applicability, and suitability of the above mentioned adsorbents for the solid phase extraction were studied. Method development showed great reproducibility and sensitivity, and low limits of detection within a considerable linear range. The regeneration and reusability of the SPE cartridges were studied using Rideau River (Ottawa, Canada) surface water samples and the results showed that cartridges could be used for three cycles of adsorption/desorption with no loss of efficiency. In general, the results suggested that modification of MWCNTs affords a novel class of adsorbents, which could be used for the SPE of various analytes from aqueous solutions with great efficiency, recovery, reproducibility, sensitivity, and precision, within a wide range of analyte concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".