Bibliographic record
Abstract
Nonionic surfactants are always good candidates for surfactant enhanced aquifer remediation (SEAR), enhanced oil recover (EOR) and insitu/ex-situ soil washing.Their wide application is based on their high solubilization capacities and their low bio-toxicity.However, surfactant loss due to adsorption and/or partitioning on adsorbents impairs their effectiveness to solubilize/mobilize and reduce the oil-water interfacial tension (IFT) which renders contaminant remediation process economically unfeasible.In this research anionic (SDS) and nonionic (TX100) surfactants and their mixtures (1:2, 1:1, 2:1; TX100:SDS mass ratios) were evaluated for their ability to reduce surfactant CMCs, partitioning losses to an organic phase and/or adsorption to shale.Result showed that all mixtures behave similarly and have superior properties than both single surfactants.Partitioning and adsorption of TX100 into organic phase (Sarapar147) and shale were decreased by mixing with anionic surfactant (SDS).The data showed a 40% reduction in surfactant losses due to adsorption to shale and around 60% reduction due to both partitioning and adsorption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.435 | 0.243 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".