The impact of immobile zones on the transport and retention of nanoparticles in porous media
Bibliographic record
Abstract
Abstract Nanoparticle transport and retention within porous media is treated by conceptualizing the porous media as a series of independent collectors (e.g., Colloid Filtration Theory). This conceptualization assumes that flow phenomena near grain‐grain contacts, such as immobile zones (areas of low flow), exert a negligible influence on nanoparticle transport and assumes that retention and release of particles depends only on surface chemistry. This study investigated the impact of immobile zones on nanoparticle transport and retention by employing synchrotron X‐ray computed microtomography (SXCMT) to examine pore‐scale silver nanoparticle distributions during transport through three sand columns: uniform iron oxide, uniform quartz, and well‐graded quartz. Extended tailing was observed during the elution phase of all experiments suggesting that hydraulic retention in immobile zones, not detachment from grains, was the source of tailing. A numerical simulation of fluid flow through an SXCMT data set predicted the presence of immobile zones near grain‐grain contacts. SXCMT‐determined silver nanoparticle concentrations observed that significantly lower nanoparticle concentrations existed near grain‐grain contacts throughout the duration of all experiments. In addition, the SXCMT‐determined pore‐scale concentration gradients were found to be independent of surface chemistry and grain size distribution, suggesting that immobile zones limit the diffusive transport of nanoparticles toward the collectors. These results suggest that the well‐known overprediction of nanoparticle retention by traditional CFT may be due to ignoring the influences of grain‐grain contacts and immobile zones. As such, accurate prediction of nanoparticle transport requires consideration of immobile zones and their influence on both hydraulic and surface retention.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| 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 teacher head, 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".