Evidence map›Paper›PMID 36655197›Full record

Article3D printing and additive manufacturing2020

Scalable Printing of Bionic Multiscale Channel Networks Through Digital Light Processing-Based Three-Dimensional Printing Process.

Yue Wang, Yancheng Wang, Deqing Mei

Open access · greenAbstract read
In one paragraph

Article in 3D printing and additive manufacturing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 36% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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 at 1 institution in 2 countries.

Yue WangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Yancheng WangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
Deqing MeiState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, China.
George Washington University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital light processing (DLP)-based printing process has been used to print microfeature-sized constructs and architectures for biomedical applications; the key challenge is to achieve both large printing size and high accuracy at the same time. Here we reported a scalable DLP-based three-dimensional (3D) printing system with scalable resolution and building size, which was used for printing of multiscale hydrogel fractal bionic channels. Scalable printing was achieved by moving the convex lens of the printing system, and thus, each single micromirror of the digital micromirror device chip corresponded to the single-pixel size scaling from 6 to12 μm. Using this system, we were able to use poly (ethylene glycol) diacrylate to fabricate a variety of multiscale architectures, such as regular fractal Y-shaped channels, and more irregular and intricate geometries, such as biomimetic capillary vascular networks. Blue and red food dye solutions were able to freely fill all these channels in the scaffolds, from the trunk (>1500 μm in width) to small branch (∼30 μm in width) by capillarity. Cell experiments were carried out to certify the biocompatibility of printed multiscale biomimetic channel networks. This work reveals significant progress in printing multiscale constructs with both large printing size and high precision in scalable DLP-based 3D printing.

Indexed as

3D printingcell culturingdigital light processing (DLP)fractal and bionic channelsmultiscale fabricationscalable

Identifiers

PMID36655197
PMCPMC9586228
OpenAlexW3032880562

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.