Regulatory developments and their impacts to the nano-industry: A case study for nano-additives in 3D printing
Bibliographic record
Abstract
Nanotechnology has increasing applications in numerous markets, particularly in additive processing (3D printing) and manufacturing, which is important for consumer products, medical devices, construction, and general research and development across many other industries. Nanomaterials are desirable in many products due to their unique properties, but those same properties have made evaluating the risk and regulation of these materials challenging. For risk-based regulations, new applications and nanomaterials should be assessed for both human and environmental hazards and exposure to ensure protection. In general, many risk assessments to date have focused on the non-nano versions of chemicals. The lack of guidance on assessing the hazard and exposure of nanomaterials in 3D printing is apparent, and these areas of assessment are actively being evaluated. Industry in most cases will now need to provide specific additional information for assessing the risk of nanomaterials in 3D printing. This review paper focuses on the use of nanomaterials in 3D printing for industrial and manufacturing applications, summarizes the current literature on human health and safety related to 3D printing and inhalation exposure, and the regulations relating to 3D printing in the U.S., Canada, and Europe for this industry.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".