A review on mobility of engineered carbon-based nanoparticles in porous media
Bibliographic record
Abstract
Engineered nanoparticles have generated significant public and scientific excitement due to their unique physical, chemical and electrical properties, which have led to their application in a wide variety of industries. Among all these, carbon nanoparticles (CNPs) are widely manufactured nanoparticles which are utilised in a significant quantity of consumer products, such as reinforced concrete, plastics, sporting goods, electronics and biomedical applications. Due to their fast-track use, CNPs constitute a potential risk if they are released to soil and groundwater systems. Toxic effects of CNPs have been observed on the human body as well as the environment; therefore, their release and distribution into the environment has become an important topic of concern. Hence, it is essential to improve the current understanding of CNP transportation and retention into porous media. Several studies have investigated CNP mobility in packed sand columns under water-saturated conditions. This study reviews a significant number of studies which have found that CNP mobility is sensitive to a diversity of experimental conditions, including physical conditions (collector grain size, pore water velocity) and solution chemistry (ionic strength, pH). Further work should be done to understand the pattern of CNP mobility into subsurface environments considering realistic scenarios at field scale.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".