Influence of a Nonionic Surfactant on the Water Retention Properties of Unsaturated Soils
Bibliographic record
Abstract
Surfactants are widely used in household products, industrial processes and as adjuvants to improve the delivery and effectiveness of agrochemicals. Due to their amphiphilic nature, surfactants tend to accumulate at gas‐liquid and solid‐liquid interfaces, and thus, have the potential to influence water flow and retention in unsaturated soils. The objective of this study was to investigate the effects of a nonionic surfactant, Triton X‐100, on the interfacial properties and capillary pressure‐water content relationships of F‐70 Ottawa sand and Appling soil. In the presence of surfactant, soil water contents decreased incrementally as the surfactant concentration was increased from 0 g L −1 up to the critical micelle concentration (CMC) of Triton X‐100 (0.15 g L −1 ). Over the same surfactant concentration range, the surface tension of water decreased from 7.2 × 10 −2 J m −2 to 3.2 × 10 −2 J m −2 while solid‐liquid contact angle decreased from 40° to 10°. No further changes in interfacial properties or soil water characteristics were observed at surfactant concentrations above the CMC. The experimental results were used to develop and evaluate alternative scaling approaches to describe concentration dependent changes in soil water characteristics based on the van Genuchten model. A scaling factor that incorporated both surface tension and content angle relationships provided accurate predictions of soil water retention curves over a range of surfactant concentrations. A simplified form of the scaling factor also was developed, on the basis of a single fitting parameter without the need for surface tension and contact angle data. Although further validation of the simplified scaling factor will be required, this approach offers an efficient means to describe the effects of concentration dependent changes in interfacial properties on soil water characteristics.
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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) | 0.000 | 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".