Evidence map›Paper›PMID 35889560›Full record

ReviewNanomaterials (Basel, Switzerland)2022

Research Progress on the Preparation and Applications of Laser-Induced Graphene Technology.

Yani Guo, Cheng Zhang, Ye Chen, Zhengwei Nie

Abstract readReview
In one paragraph

Review in Nanomaterials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
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  6. Article
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  8. Article
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  11. Seeing Through Muddy Water: Laser-Induced Graphene for Portable Tomography Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  12. Review
  13. Article
  14. Article
  15. Application of Graphene-Based Materials.Nanomaterials (Basel, Switzerland) · 2023
    Article
  16. Review
  17. Review
  18. Article
  19. 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.

Yani GuoSchool of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China.
Cheng ZhangCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Ye ChenSchool of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China.
Zhengwei NieSchool of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China.ORCID 0000-0002-4374-8620

Funding

Natural Science Foundation of Jiangsu Province, China BK20200686
6 · The paper itself

Abstract

Graphene has been regarded as a potential application material in the field of new energy conversion and storage because of its unique two-dimensional structure and excellent physical and chemical properties. However, traditional graphene preparation methods are complicated in-process and difficult to form patterned structures. In recent years, laser-induced graphene (LIG) technology has received a large amount of attention from scholars and has a wide range of applications in supercapacitors, batteries, sensors, air filters, water treatment, etc. In this paper, we summarized a variety of preparation methods for graphene. The effects of laser processing parameters, laser type, precursor materials, and process atmosphere on the properties of the prepared LIG were reviewed. Then, two strategies for large-scale production of LIG were briefly described. We also discussed the wide applications of LIG in the fields of signal sensing, environmental protection, and energy storage. Finally, we briefly outlined the future trends of this research direction.

Indexed as

energy storageenvironmental protectionlaser-induced graphenepreparation processsignal sensing

Identifiers

PMID35889560
PMCPMC9317010

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.