دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Prevention through design: insights from computational fluid dynamics modeling to predict exposure to ultrafine particles from 3D printing.
المؤلفون: MacCuspie RI; NanoSafe, Inc., Blacksburg, VA, USA., Hill WC; NanoSafe, Inc., Blacksburg, VA, USA., Hall DR; Chemistry & Industrial Hygiene, Inc., Wheat Ridge, CO, USA., Korchevskiy A; Chemistry & Industrial Hygiene, Inc., Wheat Ridge, CO, USA., Strode CD; Chemistry & Industrial Hygiene, Inc., Wheat Ridge, CO, USA., Kennedy AJ; Department of Environmental Laboratory, Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA., Ballentine ML; Department of Environmental Laboratory, Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA., Rycroft T; Department of Environmental Laboratory, Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA., Hull MS; NanoSafe, Inc., Blacksburg, VA, USA.; Institute for Critical Technology and Applied Science, Virginia Tech, Blacksburg, VA, USA.
المصدر: Journal of toxicology and environmental health. Part A [J Toxicol Environ Health A] 2021 Jun 03; Vol. 84 (11), pp. 458-474. Date of Electronic Publication: 2021 Feb 28.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Taylor & Francis Country of Publication: England NLM ID: 100960995 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1528-7394 (Print) Linking ISSN: 00984108 NLM ISO Abbreviation: J Toxicol Environ Health A Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Washington, D.C. ; London : Taylor & Francis, c1998-
مواضيع طبية MeSH: Computational Chemistry* , Hydrodynamics*, Air Pollutants/*analysis , Environmental Exposure/*analysis , Particulate Matter/*analysis, Computational Biology ; Humans ; Models, Theoretical ; Printing, Three-Dimensional
مستخلص: 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 2 room. Measurements of the particle concentrations in a Class 1000 clean room of identical geometry were found to pass the Hanna test for agreement between model and experimental data, validating the findings.
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معلومات مُعتمدة: R43 ES030650 United States ES NIEHS NIH HHS
فهرسة مساهمة: Keywords: 3D printer emissions; additive manufacturing; computational fluid dynamics; exposure; indoor air quality; ultrafine particles
المشرفين على المادة: 0 (Air Pollutants)
0 (Particulate Matter)
تواريخ الأحداث: Date Created: 20210301 Date Completed: 20210916 Latest Revision: 20240407
رمز التحديث: 20240407
مُعرف محوري في PubMed: PMC8044021
DOI: 10.1080/15287394.2021.1886210
PMID: 33641630
قاعدة البيانات: MEDLINE
الوصف
تدمد:1528-7394
DOI:10.1080/15287394.2021.1886210