Green Cleaning of 3D-Printed Polymeric Products by Micro-/Nano-Bubbles.

Haoxiang Gao, Fenghua Zhang, Kangkang Tang, Xianyu Luo, Ziang Pu, Jiuzhou Zhao, Zhiwei Jiao, Weimin Yang

Abstract read

In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Haoxiang GaoCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Fenghua ZhangCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Kangkang TangCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Xianyu LuoCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Ziang PuCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Jiuzhou ZhaoCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Zhiwei JiaoCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Weimin YangCollege of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.

Funding

National Advanced Functional Fiber Innovation Center of China No. H2022536Shanghai Youshun Automotive Technology Co.,Ltd No. H2022201

6 · The paper itself

Abstract

3D printing technology has been used to directly produce various actual products, ranging from engines and medicines to toys, especially due to its advantage in producing items of complicated, porous structures, which are inherently difficult to clean. Here, we apply micro-/nano-bubble technology to the removal of oil contaminants from 3D-printed polymeric products. Micro-/nano-bubbles show promise in the enhancement of cleaning performance with or without ultrasound, which is attributed to their large specific surface area enhancing the adhesion sites of contaminants, and their high Zeta potential which attracts contaminant particles. Additionally, bubbles produce tiny jets and shock waves at their rupture, driven by coupled ultrasound, which can remove sticky contaminants from 3D-printed products. As an effective, efficient, and environmentally friendly cleaning method, micro-/nano-bubbles can be used in a range of applications.

Indexed as

3D printingcleaningmicro-/nano-bubblesporous polymersultrasound

Identifiers

PMID37299707

DOI10.3390/nano13111804

PMCPMC10254490

What OpenQuestion holds

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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.