Evidence map›Paper›PMID 38779489›Full record

ArticleResearch (Washington, D.C.)2024

A General Synthesis Method for Patterning PEDOT toward Wearable Electronics and Bioelectronics.

Wanke Cheng, Zihao Zheng, Xiaona Li, Ying Zhu, Suqing Zeng, Dawei Zhao, Haipeng Yu

Abstract read
In one paragraph

Article in Research (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Cellulosic Composites in Lithium Metal Batteries.Advanced materials (Deerfield Beach, Fla.) · 2026
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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.

Wanke ChengKey Laboratory of Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin, China.
Zihao ZhengKey Laboratory of Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin, China.
Xiaona LiKey Laboratory of Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin, China.
Ying ZhuKey Laboratory of Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin, China.
Suqing ZengKey Laboratory of Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin, China.
Dawei ZhaoKey Laboratory of Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin, China.ORCID https://orcid.org/0000-0003-2813-7263
Haipeng YuKey Laboratory of Bio-based Material Science and Technology of Ministry of Education, Northeast Forestry University, Harbin, China.ORCID https://orcid.org/0000-0003-0634-7913

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The conductive polymer poly-3,4-ethylenedioxythiophene (PEDOT), recognized for its superior electrical conductivity and biocompatibility, has become an attractive material for developing wearable technologies and bioelectronics. Nevertheless, the complexities associated with PEDOT's patterning synthesis on diverse substrates persist despite recent technological progress. In this study, we introduce a novel deep eutectic solvent (DES)-induced vapor phase polymerization technique, facilitating nonrestrictive patterning polymerization of PEDOT across diverse substrates. By controlling the quantity of DES adsorbed per unit area on the substrates, PEDOT can be effectively patternized on cellulose, wood, plastic, glass, and even hydrogels. The resultant patterned PEDOT exhibits numerous benefits, such as an impressive electronic conductivity of 282 S·m

Identifiers

PMID38779489
PMCPMC11109514

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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.