Evidence map›Paper›PMID 33551537›Full record

ArticleJournal of manufacturing systems2021

Additively manufactured respirators: quantifying particle transmission and identifying system-level challenges for improving filtration efficiency.

Lindsey B Bezek, Jin Pan, Charbel Harb, Callie E Zawaski, Bemnet Molla, Joseph R Kubalak, Linsey C Marr, Christopher B Williams

Open access · greenAbstract read
In one paragraph

Article in Journal of manufacturing systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

7 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Masks for COVID-19.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Review
  5. Article
  6. Review
  7. Article
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

8 authors at 1 institution in 1 country.

Lindsey B BezekDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
Jin PanDepartment of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
Charbel HarbDepartment of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
Callie E ZawaskiDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
Bemnet MollaDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
Joseph R KubalakDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
Linsey C MarrDepartment of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
Christopher B WilliamsDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
Virginia Tech · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has disrupted the supply chain for personal protective equipment (PPE) for medical professionals, including N95-type respiratory protective masks. To address this shortage, many have looked to the agility and accessibility of additive manufacturing (AM) systems to provide a democratized, decentralized solution to producing respirators with equivalent protection for last-resort measures. However, there are concerns about the viability and safety in deploying this localized download, print, and wear strategy due to a lack of commensurate quality assurance processes. Many open-source respirator designs for AM indicate that they do not provide N95-equivalent protection (filtering 95% of SARS-CoV-2 particles) because they have either not passed aerosol generation tests or not been tested. Few studies have quantified particle transmission through respirator designs outside of the filter medium. This is concerning because several polymer-based AM processes produce porous parts, and inherent process variation between printers and materials also threaten the integrity of tolerances and seals within the printed respirator assembly. No study has isolated these failure mechanisms specifically for respirators. The goal of this paper is to measure particle transmission through printed respirators of different designs, materials, and AM processes. The authors compare the performance of printed respirators to N95 respirators and cloth masks. Respirators in this study printed using desktop- and industrial-scale fused filament fabrication processes and industrial-scale powder bed fusion processes were not sufficiently reliable for widespread distribution and local production of N95-type respiratory protection. Even while assuming a perfect seal between the respirator and the user's face, although a few respirators provided >90% efficiency at the 100-300 nm particle range, almost all printed respirators provided <60% filtration efficiency. Post-processing procedures including cleaning, sealing surfaces, and reinforcing the filter cap seal generally improved performance, but the printed respirators showed similar performance to various cloth masks. The authors further explore the process-driven aspects leading to low filtration efficiency. Although the design/printer/material combination dictates the AM respirator performance, the identified failure modes originate from system-level constraints and are therefore generalizable across multiple AM processes. Quantifying the limitations of AM in producing N95-type respiratory protective masks advances understanding of AM systems toward the development of better part and machine designs to meet the needs of reliable, functional, end-use parts.

Indexed as

Additive manufacturingCOVID-19Filtration efficiencyN95Particle transmissionRespirator

Identifiers

PMID33551537
PMCPMC7846466
OpenAlexW3128866810

What OpenQuestion holds

Textmetadata
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.