Polyaromatic hydrocarbons (PAHs) sorption behavior unaffected by the presence of multi-walled carbon nanotubes (MWNTs) in a natural soil system
Bibliographic record
Abstract
The batch equilibrium approach was used to examine the influence of multi-walled carbon nanotubes (MWNTs) on the sorption behaviors of polyaromatic hydrocarbons (PAHs) in soil. To the knowledge of the authors, this is the first study of PAH sorption to MWNTs in real natural soil systems. The sorption behavior of three PAHs (naphthalene, fluorene, and phenanthrene) in the presence of commercially available MWNTs in two natural soils (a sandy loam and a silt loam) and Ottawa sand was evaluated. Adsorption of PAHs by MWNTs in this study was three orders of magnitude higher than that of natural soils. Sorption coefficients of PAHs (Kd and Koc) were unchanged in the presence of 2 mg g(-1) MWNTs in soil (p > 0.05). A micro-mechanics approach, termed 'the rule of mixtures' was used for predicting PAH sorption behaviors in mixtures based on sorption coefficients derived from single sorbents. The equation, KT = KMα + KN(1 - α) (K, sorption coefficients, Kd or Koc), predicted sorption coefficients in a mixture based on mixture component sorption coefficients and mass fractions. Data presented in this study could be used to fill data gaps related to the environmental fate of carbon nanotubes in soil.
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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".