Evidence map›Paper›PMID 40160772›Full record

ArticleACS omega2025

Laser-Induced Porous Graphene-Based Materials and Its Potential Application in Lithium-Ion Batteries.

Xiao Ma, Shaoqing Wang, Zilong Fu, Hongyi Gao, Yan Shao, Xiaoguang Li, Xiaoyun Yan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. 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

7 authors.

Xiao MaSchool of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, PR China.
Shaoqing WangSchool of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, PR China.ORCID https://orcid.org/0000-0001-5158-7751
Zilong FuSchool of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, PR China.
Hongyi GaoSchool of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, PR China.
Yan ShaoSchool of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, PR China.
Xiaoguang LiInstitutional Center for Shared Technologies and Facilities, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Xiaoyun YanGeneral Prospecting Institute of China National Administration of Coal Geology, Beijing 100039, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Laser-induced coal-based graphene (C-LIG) is a promising field in energy materials research that focuses on converting abundant coal resources into high-value graphene-based materials using low-cost and environmentally friendly processes. In this study, we successfully produced few-layer graphene from low-volatility bituminous coal under vacuum conditions using carbon dioxide infrared laser equipment. The results showed that the laser-induced graphene consists predominantly of 2-5 layers, featuring multipore structures with smaller-size pores on the pore walls. It also exhibits large interlayer spacing with the layers arranged in a long-range disorder, demonstrating high crystallinity and significant expansion of the aromatic layers. Notably, the interlayer spacing increases as the number of graphene layers decreases. When used as an anode material for lithium-ion batteries, C-LIG demonstrates a high specific capacity of 330 mA h g

Identifiers

PMID40160772
PMCPMC11948149

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.