Equilibrium Adsorption Isotherms of Anionic, Nonionic Surfactants and Their Mixtures to Shale and Sandstone
Bibliographic record
Abstract
In this paper, the adsorptive behaviour of two surfactants (Triton X100 and SDS) and their mixtures (1:2; 1:1; 2:1 SDS:TX100 by wt) with two local adsorbents, sandstone and shale was examined. Adsorption of surfactants was assessed using a surface tension technique for aqueous phase surfactant concentrations less than critical micelle concentration (CMC). SDS (anionic surfactant) adsorption was not detected to any of the adsorbent samples. In contrast Triton X100, an ethoxylated nonionic surfactant, adsorbed to both adsorbents. Surfactant adsorbed/Kg adsorbent was lower for TX100-SDS mixtures in comparison to TX100 alone particularly for shale. Adsorption data for sub-micelle concentrations were found to fit successfully both Freundlich and Langmuir isotherms. Freundlich models pretty much represent the data than the Langmuir model. Because of their ability to minimize their amounts adsorbed to different adsorbents, mixed anionic-nonionic surfactant particularly TX100-SDS may show potential advantages in surfactant enhanced aquifer remediation (SEAR) and surfactant enhanced oil recovery (EOR) applications.
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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 | 0.001 |
| 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".