Pore Network Modeling of Nanoparticle Dispersion in Porous Media
Bibliographic record
Abstract
Understanding the processes that give rise to hydrodynamic dispersion of nanoparticles in porous media is important not only for assessing the risk from their accidental release in subsurface environments, but also for the design of nanoremediation strategies. Pore network models offer distinct advantages over continuum models, including the ability to account for the distribution of pore-scale velocities, as well as other phenomena that occur at the pore scale and are dependent on the interaction between nanoparticles and the local geometry of the pore space (hindered diffusion, size exclusion, etc.). Adopting a Eulerian approach, we formulate here a pore network model in OpenPNM, and present simulations of nanoparticle transport in a fully-saturated column packed with spherical beads. The pore network which is extracted from a voxel image of the simulated sphere pack is found to accurately represent the permeability, tortuosity and capillary properties of a real column of glass beads. The resulting pore network model is used to investigate an aspect of nanoparticle transport that has so far received limited attention, namely the possible effect of nanoparticle size on dispersivity. To this end, the longitudinal dispersion coefficient is determined by simulating transient advection and diffusion in the pore network, introducing either a pulse or step-change injection, and then fitting analytical solutions to the resulting elution curve. It is found that nanoparticle size influences the dispersion coefficient or the effective particle velocity only when the ratio of particle to bead (solid grain) size is sufficiently high (greater than about 0.01). Under such conditions, the nanoparticles experience an earlier breakthrough due to the velocity profile exclusion. Hindered diffusion is found to play a significant role only when the Peclet number is less than 10. In the absence of such effects, the simulations provide a priori predictions of the longitudinal dispersion coefficient in agreement with a large body of literature data.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".