Evidence map›Paper›PMID 36844511›Full record

ArticleACS omega2023

Biowaste Eggshell Membranes for Bio-triboelectric Nanogenerators and Smart Sensors.

Meng-Fang Lin, Po-Yen Chang, Chia-Hsien Lee, Xin-Xian Wu, Ru-Jong Jeng, Chih-Ping Chen

Abstract read
In one paragraph

Article in ACS omega, 2023. 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. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Biowaste-Derived Triboelectric Nanogenerators for Emerging Bioelectronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Review
  8. Article
  9. 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

6 authors.

Meng-Fang LinDepartment of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan.ORCID https://orcid.org/0000-0002-9286-6527
Po-Yen ChangDepartment of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan.
Chia-Hsien LeeDepartment of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan.
Xin-Xian WuDepartment of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan.
Ru-Jong JengInstitute of Polymer Science and Engineering, National Taiwan University, Taipei 106, Taiwan.ORCID https://orcid.org/0000-0002-0913-4975
Chih-Ping ChenDepartment of Materials Engineering, Ming Chi University of Technology, New Taipei City 24301, Taiwan.ORCID https://orcid.org/0000-0002-0281-7554

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we used a simple and cost-effective method to fabricate triboelectric nanogenerators (TENGs) based on biowaste eggshell membranes (EMs). We prepared stretchable electrodes with various types of EMs (hen, duck, goose, and ostrich) and employed them as positive friction materials for bio-TENGs. A comparison of the electrical properties of the hen, duck, goose, and ostrich EMs revealed that the output voltage of the ostrich EM could reach up to 300 V, due to its abundant functional groups, natural fiber structure, high surface roughness, high surface charge, and high dielectric constant. The output power of the resulting device reached 0.18 mW, sufficient to power 250 red light-emitting diodes simultaneously, as well as a digital watch. This device also displayed good durability when subjected to 9000 cycles at 30 N at a frequency of 3 Hz. Furthermore, we designed an ostrich EM-TENG as a smart sensor for the detection of body motion, including leg movement and the pressing of different numbers of fingers.

Identifiers

PMID36844511
PMCPMC9948195

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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.