Evidence map›Paper›PMID 33641630›Full record

ArticleJournal of toxicology and environmental health. Part A2021

Prevention through design: insights from computational fluid dynamics modeling to predict exposure to ultrafine particles from 3D printing.

Robert I MacCuspie, W Cary Hill, Daniel R Hall, Andrey Korchevskiy, Cassidy D Strode, Alan J Kennedy, Mark L Ballentine, Taylor Rycroft, Matthew S Hull

Open access · greenAbstract read
In one paragraph

Article in Journal of toxicology and environmental health. Part A, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Identification of effective control technologies for additive manufacturing.Journal of toxicology and environmental health. Part B, Critical reviews · 2022
    Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Robert I MacCuspieNanoSafe, Inc., Blacksburg, VA, USA.ORCID 0000-0002-6618-6499
W Cary HillNanoSafe, Inc., Blacksburg, VA, USA.
Daniel R HallChemistry & Industrial Hygiene, Inc., Wheat Ridge, CO, USA.
Andrey KorchevskiyChemistry & Industrial Hygiene, Inc., Wheat Ridge, CO, USA.
Cassidy D StrodeChemistry & Industrial Hygiene, Inc., Wheat Ridge, CO, USA.
Alan J KennedyDepartment of Environmental Laboratory, Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA.
Mark L BallentineDepartment of Environmental Laboratory, Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA.
Taylor RycroftDepartment of Environmental Laboratory, Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA.
Matthew S HullNanoSafe, Inc., Blacksburg, VA, USA.
United States Army · USVirginia Tech · US

Funding

NanoSafe Tested™ Third Party Verification Process for Characterizing Exposures to Products Containing Engineered NanomaterialsR43ES030650 · NIEHS · NANOSAFE, INC. · PI HILL, WILLIAM CARY · 2019 to 2019
$150k
NIEHS NIH HHS R43 ES030650
6 · The paper itself

Abstract

Fused filament fabrication (FFF) 3D printers are increasingly used in industrial, academic, military, and residential sectors, yet their emissions and associated user exposure scenarios are not fully described. Characterization of potential user exposure and environmental releases requires robust investigation. During operation, common FFF 3D printers emit varying amounts of ultrafine particles (UFPs) depending upon feedstock material and operation procedures. Volatile organic compounds associated with these emissions exhibit distinct odors; however, the UFP portion is largely imperceptible by humans. This investigation presents straightforward computational modeling as well as experimental validation to provide actionable insights for the proactive design of lower exposure spaces where 3D printers may be used. Specifically, data suggest that forced clean airflows may create lower exposure spaces, and that computational modeling might be employed to predict these spaces with reasonable accuracy to assist with room design. The configuration and positioning of room air ventilation diffusers may be a key factor in identifying lower exposure spaces. A workflow of measuring emissions during a printing process in an ANSI/CAN/UL 2904 environmental chamber was used to provide data for computational fluid dynamics (CFD) modeling of a 6 m

Indexed as

Computational ChemistryHydrodynamicsAir PollutantsComputational BiologyEnvironmental ExposureHumansModels, TheoreticalParticulate MatterPrinting, Three-DimensionalAir PollutantsParticulate Matter3D printer emissionsadditive manufacturingcomputational fluid dynamicsexposureindoor air qualityultrafine particles

Identifiers

PMID33641630
PMCPMC8044021
OpenAlexW3133938748

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.