Impact of Flow Velocity on Transport of Graphene Oxide Nanoparticles in Saturated Porous Media
Bibliographic record
Abstract
Core Ideas We examined the influence of flow velocity on transport of graphene oxide nanoparticles (GONPs). Attachment efficiency ( α ) increased with increasing flow velocity for GONPs. Hydrodynamic shear cannot release the attached GONPs, even from nanoscale asperities. Increasing flow velocity increased attachment of GONPs at concave surfaces. Saturated column experiments were conducted to systematically examine transport of graphene oxide nanoparticles (GONPs) in sand porous media at different solution ionic strengths (ISs) with different flow velocities. Results show that deposition of GONPs increase with decreasing IS. However, the Derjaguin–Landau–Verwey–Overbeek (DLVO) interaction energy calculations using a surface element integration technique show that the energy barrier increases with increasing IS for the sheet‐shaped colloids interacting with a planar sand surface. In contrast, the presence of nanoscale protruding asperities (NPAs) can cause a decrease in the energy barrier with increasing IS, which facilitates the attachment in primary minima at higher IS. At a given IS, increasing flow velocity increases attachment efficiency on the rough sand surfaces. Torque analysis shows that the maximum hydrodynamic torques are smaller than the adhesive torques, even for GONPs located on the NPAs where the adhesions are the lowest. Consequently, the attachment efficiency cannot be reduced by increasing flow velocity. Additional column experiments confirm that deposited GONPs cannot be detached by increasing flow velocity. Conversely, increase of flow velocity may enhance approaching of GONPs in low‐flow regions such as concave areas of sand surfaces and subsequent attachment. Although it has been recognized in the literature that the presence of NPAs can cause decrease of attachment efficiency with increasing flow velocity for spherical colloids under both unfavorable and favorable chemical conditions, our results highlight a different role of surface roughness in attachment and detachment of sheet‐shaped colloids under unfavorable conditions.
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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 | 0.000 | 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 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".