From Cradle-to-Grave at the Nanoscale: Gaps in U.S. Regulatory Oversight along the Nanomaterial Life Cycle
Bibliographic record
Abstract
Engineered nanomaterials (ENMs) promise great benefits for society, yet our knowledge of potential risks and best practices for regulation are still in their infancy. Toward the end of better practices, this paper analyzes U.S. federal environmental, health, and safety (EHS) regulations using a life cycle framework. It evaluates their adequacy as applied to ENMs to identify gaps through which emerging nanomaterials may escape regulation from initial production to end-of-life. High scientific uncertainty, a lack of EHS and product data, inappropriately designed exemptions and thresholds, and limited agency resources are a challenge to both the applicability and adequacy of current regulations. The result is that some forms of engineered nanomaterials may escape federal oversight and rigorous risk review at one or more stages along their life cycle, with the largest gaps occurring at the postmarket stages, and at points of ENM release to the environment. Oversight can be improved through pending regulatory reforms, increased research and development for the monitoring, control, and analysis of environmental and end-of-life releases, introduction of periodic re-evaluation of ENM risks, and fostering a "bottom-up" stewardship approach to the responsible management of risks from engineered nanomaterials.
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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 | 0.047 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".