Evidence map›Paper›PMID 36679373›Full record

ArticleSensors (Basel, Switzerland)2023

Toward Large-Scale Energy Harvesting by a UV-Curable Organic-Coating-Based Triboelectric Nanogenerator.

Jian Chen, Ning Tang, Li Cheng, Youbin Zheng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Jian ChenYangjiang Nuclear Power Company Ltd., Yangjiang 529941, China.
Ning TangSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Li ChengSchool of Materials and Energy, Lanzhou University, Lanzhou 730000, China.ORCID 0000-0001-6824-7139
Youbin ZhengDepartment of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa 3200003, Israel.ORCID 0000-0003-3905-8552

Funding

National Natural Science Foundation of China 52172203Startup Fund at SJTU, China WH220503026the Natural Science Foundation of Gansu Province 22JR5RA486
6 · The paper itself

Abstract

Triboelectric nanogenerators (TENGs) stand out as an attractive form of technology for the efficient harvest of mechanical energy and the powering of wearable devices due to their light weight, simplicity, high power density, and efficient vibration energy scavenging capabilities. However, the requirement for micro/nanostructures and/or complex and expensive instruments hinders their cheap mass production, thus limiting their practical applications. By using a simple, cost-effective, fast spray-coating process, we develop high-performance UV-curable triboelectric coatings for large-scale energy harvesting. The effect of different formulations and coating compositions on the triboelectric output is investigated to design triboelectric coatings with high output performance. The TENG based on a hybrid coating exhibits high output performance of 54.5 μA current, 1228.9 V voltage, 163.6 nC transferred charge and 3.51 mW output power. Moreover, the hybrid coatings show good long-term output stability. All the results indicate that the designed triboelectric coatings show great potential for large-scale energy harvesting with the advantages of cost-effectiveness, fast fabrication, easy mass production and long-term stability.

Indexed as

NanostructuresOrganic ChemicalsPhysical PhenomenaTechnologyVibrationOrganic Chemicalsenergy harvestingorganic coatingtriboelectric nanogeneratorwearable devices

Identifiers

PMID36679373
PMCPMC9866600

What OpenQuestion holds

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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.