Kerogen in Aquifer Material and Its Strong Sorption for Nonionic Organic Pollutants
Bibliographic record
Abstract
Sorption of organic pollutants by subsurface materials has been found to not only correlate with the total organic carbon (TOC) content, but also depend on the types of soil and sediment organic matter (SOM). Characterization of geochemically heterogeneous SOM is key to elucidating sorption mechanisms and predicting pollutant transport in ground water systems. In this study, kerogen, a nonextractable organic material, was isolated with an acid demineralization procedure from a sandy aquifer material (Borden, Ontario, Canada) having a TOC content of approximately 0.021% (w/w). Petrographical examinations reveal that the kerogen has three major types of macerals including bituminite (Kerogen Type I and II), vitrinite (Type III), and fusinite (Type IV or charred kerogen). The solid-state 13C nuclear magnetic resonance (NMR) spectrum shows two dominant peaks, aliphatic and aromatic carbons, for the isolated material. Sorption isotherms measured using phenanthrene, naphthalene, 1,3,5-trichlorobenzene (TCB), and 1,2-dichlorobenzene (DCB) as sorbates showed that both the isolated kerogen and the original sand exhibited nonlinear sorption and that the phenanthrene and TCB isotherms measured for the kerogen material are more nonlinear than the respective isotherms for the original sand. The single-point organic carbon--normalized sorption capacity measured for the isolated kerogen can be several times greater than that measured for the original sand for a given sorbate. The study suggests that kerogen plays a major role in overall sorption isotherm nonlinearity and could yield higher-than-predicted sorption capacities for the subsurface material even though the content of this organic material is very low.
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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".