Emissions and health risks from the use of 3D printers in an occupational setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of this study was to determine concentrations of particulates and volatile organic compounds (VOCs) emitted from 3D printers using polylactic acid (PLA) filaments at a university workroom to assess exposure and health risks in an occupational setting. Under typical-case (one printer) and worst-case (three printers operating simultaneously) scenarios, particulate concentration (total and respirable), VOCs and formaldehyde were measured. Air samples were collected in the printing room and adjacent hallway. Size-resolved levels of nano-diameter particles were also collected in the printing room. Total particulate levels were higher in the worst-case scenario (0.7 mg/m3) vs. typical-case scenario (0.3 mg/m3). Respirable particulate and formaldehyde concentrations were similar between the two scenarios. Size-resolved measurements showed that most particles ranged from approximately 27 to 116 nm. Total VOC levels were approximately 6-fold higher during the worst-case scenario vs. typical situation with isopropyl alcohol being the predominant VOC. Airborne concentrations in the hallway were generally lower than inside the printing room. All measurements were below their respective occupational exposure limits. In summary, emissions of particulates and VOCs increased when multiple 3D printers were operating simultaneously. Airborne levels in the adjacent hallway were similar between the two scenarios. Overall, data suggest a low risk of significant and persistent adverse health effects. Nevertheless, the health effects attributed to 3D printing are not fully known and adherence to good hygiene principles is recommended during use of this technology.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it