Evidence map›Paper›PMID 36267469›Full record

ArticleMaterials today. Proceedings2023

Significance of additive manufacturing amidst the pandemic.

M Nishal, K Ram Prasad, M Salman Dasthageer, A G Ragunath

Open access · greenAbstract read
In one paragraph

Article in Materials today. Proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 27% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

4 authors at 1 institution in 1 country.

M NishalDepartment of mechanical engineering, Sri Venkateswara College of Engineering, Sriperumbudur Tk-602117, Tamil Nadu, India.
K Ram PrasadDepartment of mechanical engineering, Sri Venkateswara College of Engineering, Sriperumbudur Tk-602117, Tamil Nadu, India.
M Salman DasthageerDepartment of mechanical engineering, Sri Venkateswara College of Engineering, Sriperumbudur Tk-602117, Tamil Nadu, India.
A G RagunathKTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden.
KTH Royal Institute of Technology · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the light of COVID-19 pandemic, a global shortage for Personnel Protective Equipment (PPE) led to the search for an alternative to fill the gap where additive manufacturing made necessary development of rapid design and adaptive filtering masks for local manufacturing using 3D printing to help the frontline workers. The review focuses on the utilization of antimicrobial materials in additive manufacturing with the use of bespoke design to facilitate and respond to the disruptions in the medical supply chain. Previous studies confirmed the age-old theory of copper as an antimicrobial material with contact killing properties. The antimicrobial properties of copper have been registered at the U.S. Environmental Protection Agency as the first solid antimicrobial material. Combining the properties of copper in a PLA (Polylactic Acid) filament as a nano composite, Copper-3D facilitates the antimicrobial properties to any 3D printed object. Provided this flexibility of 3D printing, the use of masks designed distinctively based on the 3D scan of an individual's facial structures as an efficient Personnel Protective Equipment is also addressed. Additive manufacturing as a support to the shortage of medical devices and a responsive method to the disruption in the supply chain is discussed.

Indexed as

Additive ManufacturingAntimicrobial maskCOVID-19Protective mask

Identifiers

PMID36267469
PMCPMC9556958
OpenAlexW4304893256

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.