Phenanthrene Sorption to Structurally Modified Humic Acids
Bibliographic record
Abstract
Several studies emphasize the importance of soil organic matter characteristics in hydrophobic contaminant sorption and outline the strong dependence of sorption on organic matter aromaticity. In this study, the role of organic matter aromaticity in phenanthrene sorption was investigated using humic acids (HAs) from compost, peat, and soil that were structurally modified by bleaching, hydrolysis, oximation, and subcritical water extraction. The HAs were characterized with cross polarization magic angle spinning carbon-13 nuclear magnetic resonance (CPMAS 13C NMR) spectroscopy and used in batch equilibrations with phenanthrene. Bleaching substantially reduced the aromaticity of the samples whereas the other treatments increased the relative aromaticity. Phenanthrene sorption increased, even though there was a substantial reduction in sorbent aromaticity with some samples. The HAs that exhibited comparable CPMAS 13C NMR spectra and aromaticity did not behave similarly with respect to phenanthrene sorption. When the sorption data (K(oc) values) were correlated to sample aromaticity, the correlation coefficients (r2) did not exceed 0.39. Comparisons with the atomic H to C ratio provided slightly better r2 values (up to 0.54). This study demonstrates that macroscopic sorbent characteristics could not explain the observed phenanthrene sorption coefficients, aliphatic structural components of HAs can contribute appreciably to phenanthrene sorption, and organic matter physical conformation may regulate access to organic matter structures. Therefore, the use of only macroscopic sorbent properties, such as aromaticity, to predict and rationalize sorption values cannot solely be used to explain the behavior of organic contaminants in soil environments.
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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".