Environmental applications and risks of nanomaterials: An introduction to CREST publications during 2018–2021
Bibliographic record
Abstract
Nanomaterials (NMs) possess many unique properties that are increasingly used in environmental applications. Twenty-six articles in Critical Reviews in Environmental Science and Technology (CREST) from 2018 to 2021were identified that enhance our understanding and provide insight about future research directions with NMs in the environment. The first section focuses on environmental applications of NMs, including sensors to detect contaminants and environmental conditions, novel membrane materials to treat water and wastewater, and nano-enabled remediation of contaminants by adsorption, photocatalytic degradation, and/or disinfection. The second section reports on risks and the fate of NMs in the environment, including mechanisms and models of environmental transport, the role of nanoscale heterogeneities on particle attachment, and contaminant associations and ecotoxicity. The final section discusses research pertaining to emerging applications and ecotoxicity associated with nanosulfur and nanoplastics. This virtual article collection demonstrates that recent nanotechnology advances show great promise for addressing many critical challenges in environmental science and technology. However, many of these studies have been conducted under highly idealized laboratory conditions and still need to be upscaled. Caution is warranted and new approaches are still needed to detect and control the mobility of NMs, and to quantify potential impacts on ecosystems under realistic field conditions.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".