Evidence map›Paper›PMID 36245693›Full record

ArticleInternational journal of energy research2022

Discarded COVID-19 masks-derived-doped porous carbon for lithium-sulfur batteries.

Qian Rong, Chao Yuwen, Peng Liu, Feixiang Cheng, Shubiao Xia

Open access · greenAbstract read
In one paragraph

Article in International journal of energy research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 36% of its field
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

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. 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

5 authors at 2 institutions in 1 country.

Qian RongCollege of Chemistry and Environmental Science Qujing Normal University Qujing China.
Chao YuwenFaculty of Metallurgical and Energy Engineering Kunming University of Science and Technology Kunming China.
Peng LiuCollege of Chemistry and Environmental Science Qujing Normal University Qujing China.
Feixiang ChengCollege of Chemistry and Environmental Science Qujing Normal University Qujing China.
Shubiao XiaCollege of Chemistry and Environmental Science Qujing Normal University Qujing China.
Qujing Normal University · CNKunming University of Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the high theoretical capacity and energy density of lithium-sulfur (Li-S) batteries, the development of Li-S batteries has been slow due to the poor electrical conductivity and the shuttle effect of the electrode materials, resulting in low sulfur utilization and fast long-term cycling capacity decay. The modified carbon materials are often used as sulfur hosts to significantly improve the cycling performance of the materials, but also bring high-cost issues. Here, the porous carbon materials are synthesized quickly and conveniently by the microwave cross-linking method using discarded medical masks as carbon sources and concentrated sulfuric acid as solvent. However, poor surface and structural properties limit the application of materials. The porous carbon material is modified with p-toluene disulfide and urea as the sulfur and nitrogen sources by the microwave cross-linking method, which not only improves the porosity and specific surface area of the porous carbon material, but also improved the electrical conductivity and interlayer spacing of the material. As synthesized SN-doped porous carbon is employed as the sulfur host, which exhibits a high discharge capacity (1349.3 mAh g

Indexed as

discharge capacityLi‐S batteriesmedical masksporous carbon

Identifiers

PMID36245693
PMCPMC9538013
OpenAlexW4296312398

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.