Evidence map›Paper›PMID 37853073›Full record

ArticleScientific reports2023

Study on CNT/TPU cube under the 3D printing conditions of infill patterns and density.

Imjoo Jung, Eun Joo Shin, Sunhee Lee

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Imjoo JungDepartment of Fashion and Textiles, Dong-A University, Busan, 49315, Republic of Korea.
Eun Joo ShinDepartment of Organic Materials and Polymer Engineering, Dong-A University, Busan, 49315, Republic of Korea.
Sunhee LeeDepartment of Fashion and Textiles, Dong-A University, Busan, 49315, Republic of Korea. shlee014@dau.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, to develop soft pressure sensor applicable to wearable robots using stretchable polymers and conductive fillers, 3.25 wt% carbon nanotubes/thermoplastic polyurethane filament with shore 94 A were manufactured. Three infill densities (20%, 50%, and 80%) and patterns (zigzag (ZG), triangle (TR), honeycomb (HN)) were applied to print cubes via fused filament fabrication 3D printing. Most suitable infill conditions were confirmed based on the slicing images, morphologies, compressive properties, electrical properties, and electrical heating properties. For each infill pattern, ZG and TR divided the layers into lines and figures, and the layers were stacked by rotation. For HN, the same layers were stacked in a hexagonal pattern. Consequently, TR divided layer in various directions, showed the strongest compressive properties with toughness 1.99 J for of infill density 80%. Especially, the HN became tougher with increased infill density. Also, the HN laminated with the same layer showed excellent electrical properties, with results greater than 14.7 mA. The electrical heating properties confirmed that ZG and HN had the high layer density, which exhibited excellent heating characteristics. Therefore, it was confirmed that performance varies depending on the 3D printing direction, and it was confirmed that HN is suitable for manufacturing soft sensors.

Identifiers

PMID37853073
PMCPMC10584865

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Registered trials

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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.