Bibliographic record
Abstract
Nanotechnology holds the promise of vastly expanding our ability to clean up hazardous waste sites and decontaminate polluted groundwater in situ.Polychlorinated biphenyls, organic solvents, petroleum products, arsenic, and many more contaminants are on the list that specifically engineered nanoparticles could rapidly remove from contaminated soil and water, saving billions of dollars that would have been spent on more expensive conventional remediation methods.These possibilitites are discussed in a review of field tests using nanomaterials [EHP 117:1823-1831; Karn et al.].However, the authors caution, our knowledge of the potential environmental and health hazards posed by these nanomaterials is in its infancy.In the United States alone there are hundreds of thousands of sites contaminated with hazardous wastes, with more than 1,200 requiring priority attention.Using traditional remediation technologies, such as pumping out and treating contaminated groundwater and removing contaminated soil, the job of cleaning up U.S. hazardous waste sites could take 35 years and $250 billion.The authors of this review, however, report on results showing that nanoscale zero-valent iron (nZVI) nanoparticles could dramatically reduce the time required to remediate soil and water as well as, according to one report, save 80-90% compared with conventional methods.Although tiny in diameter, the surface area of these iron-based nanoparticles reaches 20-40 m 2 /g.This relatively large surface area can greatly increase the particles' reactivity.Flowing with the groundwater they can spread out to react with pollutants, transforming them into safer compounds, including compounds that bacteria can break down.The authors provide many examples of how nZVIs, currently the nano materials most widely used for in situ remediation, produced measurable effects within days or, in some cases, hours.However, the authors also emphasize that we do not know much about the potential adverse effects of nanoparticles deployed into the environment-agents that could, for example, end up in our drinking water.Some nanomaterials have already been found to enter organisms.Might some be toxic or transport bound pollutants to places they might not other wise have gone?Can they be biomagnified?How do they affect living organisms?The authors point out that, although the environment is full of naturally occurring nanoparticles, manufactured nanoparticles may behave in unpredictable ways.They recommend that while we improve engineering applications using nanoparticles for in situ remediation, we also develop the analytical tools to enable the study of manufactured nanoparticles in the environment and increase research on the ecosystem effects of these materials. Adrian Burton is a biologist living in Spain
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.020 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".