Evidence map›Paper›PMID 34669482›Full record

ArticleScience advances2021

Homeostatic growth of dynamic covalent polymer network toward ultrafast direct soft lithography.

Di Chen, Chujun Ni, Lulin Xie, Ye Li, Shihong Deng, Qian Zhao, Tao Xie

Abstract read
In one paragraph

Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

7 authors.

Di ChenNingbo Research Institute of Zhejiang University, Ningbo 315100, China.ORCID 0000-0002-9074-2555
Chujun NiState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
Lulin XieState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.ORCID 0000-0001-9201-1740
Ye LiState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
Shihong DengState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
Qian ZhaoState Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.ORCID 0000-0002-6931-9859
Tao XieNingbo Research Institute of Zhejiang University, Ningbo 315100, China.ORCID 0000-0003-0222-9717

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft lithography is a complementary extension of classical photolithography, which involves a multistep operation that is environmentally unfriendly and intrinsically limited to planar surfaces. Inspired by homeostasis processes in biology, we report a self-growth strategy toward direct soft lithography, bypassing conventional photolithography and its limitations. Our process uses a paraffin swollen light responsive dynamic polymer network. Selective light exposure activates the network locally, causing stress imbalance. This drives the internal redistribution of the paraffin liquid, yielding controllable formation of microstructures. This single-step process is completed in 10 seconds, does not involve any volatile solvents/reactants, and can be adapted to three-dimensional complex surfaces. The living nature of the network further allows sequential growth of hierarchical microstructures. The versatility and efficiency of our approach offer possibilities for future nanotechnologies beyond conventional microfabrication techniques.

Identifiers

PMID34669482
PMCPMC8528418

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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.