Impact of Porous Media Grain Size on the Transport of Multi-walled Carbon Nanotubes
Bibliographic record
Abstract
Nanoparticles possess unique physical, electrical, and chemical properties which make them attractive for use in a wide range of consumer products. Through their manufacturing, usage, and eventual disposal, nanoparticles are expected to ultimately be released to the environment after which point they may pose environmental and human health risks. One critical component of understanding and modeling those potential risks is their transport in the subsurface environment. This study investigates the mobility of one important nanoparticle (multi-walled carbon nanotubes or MWCNTs) through porous media, and makes the first measurements on the impact of mean collector grain size (d(50)) on MWCNT retention. Results from one-dimensional column experiments conducted under various physical and chemical conditions coupled with results of numerical modeling assessed the suitability of traditional transport models to predict MWCNT mobility. Findings suggest that a dual deposition model coupled with site blocking greatly improves model fits compared to traditional colloid filtration theory. Of particular note is that the MWCNTs traveled through porous media ranging in size from fine sand to silt resulting in normalized concentrations of MWCNTs in the effluent in excess of 60% of the influent concentration.
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) | 0.000 | 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 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".