Evidence map›Paper›PMID 33437000›Full record

ArticleScientific reports2021

Study on performance degradation and damage modes of thin-film photovoltaic cell subjected to particle impact.

Kailu Xiao, Xianqian Wu, Xuan Song, Jianhua Yuan, Wenyu Bai, Chenwu Wu, Chenguang Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Kailu XiaoInstitute of Mechanics, Chinese Academy of Sciences, No.15 Beisihuanxi Road, Haidian District, Beijing, 100190, China.
Xianqian WuInstitute of Mechanics, Chinese Academy of Sciences, No.15 Beisihuanxi Road, Haidian District, Beijing, 100190, China. wuxianqian@imech.ac.cn.
Xuan SongInstitute of Mechanics, Chinese Academy of Sciences, No.15 Beisihuanxi Road, Haidian District, Beijing, 100190, China.
Jianhua YuanCollege of Electrical Engineering and Renewable Energy, Three Gorges University, Yichang, 443002, China.
Wenyu BaiDepartment of Medical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.
Chenwu WuInstitute of Mechanics, Chinese Academy of Sciences, No.15 Beisihuanxi Road, Haidian District, Beijing, 100190, China. chenwuwu@imech.ac.cn.
Chenguang HuangInstitute of Mechanics, Chinese Academy of Sciences, No.15 Beisihuanxi Road, Haidian District, Beijing, 100190, China.

Funding

National Natural Science Foundation of China 11572327National Natural Science Foundation of China 11672315National Natural Science Foundation of China 11772347Science Challenge Project TZ2018001Strategic Priority Research Program of Chinese Academy of Sciences XDA17030100Strategic Priority Research Program of Chinese Academy of Sciences XDA17030200Strategic Priority Research Program of Chinese Academy of Sciences XDB22040302Strategic Priority Research Program of Chinese Academy of Sciences XDB22040303
6 · The paper itself

Abstract

It has been a key issue for photovoltaic (PV) cells to survive under mechanical impacts by tiny dust. In this paper, the performance degradation and the damage behavior of PV cells subjected to massive dust impact are investigated using laser-shock driven particle impact experiments and mechanical modeling. The results show that the light-electricity conversion efficiency of the PV cells decreases with increasing the impact velocity and the particles' number density. It drops from 26.7 to 3.9% with increasing the impact velocity from 40 to 185 m/s and the particles' number densities from 35 to 150/mm

Identifiers

PMID33437000
PMCPMC7804249

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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.