Surfactant sorption to soil and geologic samples with varying mineralogical and chemical properties
Bibliographic record
Abstract
Abstract The sorptive behavior of two surfactants (Triton X-1000® and Dowfax® 8390) with two surface soil samples, a subsurface soil sample, a weathered black shale containing large amounts of aged organic matter, an aeolian sand, and two clay minerals (montmorillonite and kaolinite) was examined. Dowfax 8390 (dianionic surfactant) sorption was not detected with any of the samples. In contrast, Triton X-100, an ethoxylated nonionic surfactant, sorbed to all the samples. The mole surfactant sorbed/g sorbent (mol surf g sorbent) was greater for samples containing large amounts of smectite minerals and nonlinearity of the Triton X-100 isotherm increased in samples low in organic carbon. The X-ray diffraction analysis concluded that the ethoxylate group of Triton X-100 intercalates with montmorillonite. The weathered black shale sample also has a high mol surf g sorbent value when reacted with Triton X-100 but contains less smectite clay. We suggest that Triton X-100 may be reacting via hydrophobic groups (branched alkyl chain) with the shale sample. Consequently, sorption of alcohol ethoxylate surfactants cannot be predicted solely on the basis of soil attributes such as clay or organic matter content, for it appears that the organic matter-clay organization may predominate in these types of interactions.
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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) | 0.001 | 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".