Evidence map›Paper›PMID 37763939›Full record

ArticleMicromachines2023

Super-Low Friction Electrification Achieved on Polytetrafluoroethylene Films-Based Triboelectric Nanogenerators Lubricated by Graphene-Doped Silicone Oil.

Junzhao Chen, Yu Zhao, Ruirui Wang, Pengfei Wang

Abstract read
In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

4 authors.

Junzhao ChenInstitute of Nanosurface Science and Engineering (INSE), State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Yu ZhaoInstitute of Nanosurface Science and Engineering (INSE), State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Ruirui WangInstitute of Nanosurface Science and Engineering (INSE), State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Pengfei WangInstitute of Nanosurface Science and Engineering (INSE), State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-6747-1574

Funding

National Natural Science Foundation of China 52011540005Natural Science Foundation of Guangdong Province 2021A1515011749
6 · The paper itself

Abstract

The novel proposal of Wang's triboelectric nanogenerator (TENG) has inspired extensive efforts to explore energy harvesting devices from the living environment for the upcoming low-carbon society. The inevitable friction and wear problems of the tribolayer materials become one of the biggest obstacles for attaining high-performance TENGs. To achieve super-low friction electrification of the TENGs, the tribological and electrical behaviors of the sliding-mode TENGs based on polytetrafluoroethylene (PTFE) films and metallic balls under both dry friction and liquid lubrication conditions were investigated by using a customized testing platform with a ball-on-flat configuration. Most interestingly, a super-low friction coefficient of 0.008 was achieved under graphene-doped silicone oil lubrication. The corresponding wear rate of the PTFE film was drastically decreased to 8.19 × 10

Indexed as

graphene nanosheetsoil lubricationPTFE filmsuper-low frictiontriboelectrical performancetriboelectric nanogenerator

Identifiers

PMID37763939
PMCPMC10536030

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.