Investigation for Synergies of Ionic Strength and Flow Velocity on Colloidal-Sized Microplastic Transport and Deposition in Porous Media Using the Colloidal–AFM Probe
Bibliographic record
Abstract
Studies that explore the transport and retention behavior of colloidal-sized microplastic (MP) with focusing on the governing mechanisms for their attachment and detachment process using colloidal-atomic force microscopy (C-AFM) were still limited. In the present study, multiscale investigations ranging from pore-scale column test to microscale visualization and eventually to nanoscale interfacial and adhesive force measurement were conducted. Pore- and microscale tests were conducted at various flow velocity and over a broad range of IS values and found that IS and flow velocity could synergically impact the deposition of MPs during filtration, in particular under unfavorable condition at small flow velocity. The net difference between the highest and lowest deposition conditions became smaller while flow velocity was decreasing in porous media. However, the net difference between the high and low IS conditions in parallel plate chamber were not sensitive to the change of flow velocity. The measurement from C-AFM suggested that not only the interfacial force but also the adhesive forces changed while MP was approaching/retracting to the collector surface. Information related to the magnitude, location, and occurrence of interfacial/adhesive forces were analyzed. Comparisons of the interaction energy determined from the measured force and ones derived from surface energy components using DLVO theory were conducted to explain the synergies of IS and flow velocity on pathogenic size MPs transport and deposition during filtration.
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 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) | 0.001 | 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".