Mobilization of DNAPL lenses in heterogeneous aquifers using shear-thinning PEO polymers: Experimental and numerical study
Bibliographic record
Abstract
and Newtonian behavior above. PEO solutions in porous media exhibit Newtonian behavior at low-to-moderate shear rates for all MWs, likely due to confinement-limited entanglement. Adsorption studies found non-significant PEO adsorption on soil surfaces, likely due to its large molecular size. Post-flushing of PEO-saturated columns with water led to notable permeability reductions attributed to viscous fingering. Column tests indicated a decrease of the residual DNAPL saturation with the capillary number (Ca), more sharply in low permeability soils. 2D cell tests identified three stages of DNAPL mobilization: initial stabilization, sharp recovery increase upon PEO arrival, and a final stabilization at residual saturation. The duration of each transition was found to be influenced by concentration. Numerical simulations accurately mirrored these stages and provided additional insights into PEO viscosity distribution and DNAPL mobilization patterns in heterogeneous media. The results highlighted that higher injection rates promote mobilization from the two low permeability layers surrounding the DNAPL bank from both sides and the upper zone, while lower rates mainly drive mobilization from the upper side. Using numerical simulations the performance of PEO injection on displacement of DNAPL in multiple lenses and various position of recovery points was evaluated.
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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".