Evidence map›Paper›PMID 38618484›Full record

ArticleFrontiers in microbiology2024

Study on the effectiveness of sulfate-reducing bacteria to remove Pb(II) and Zn(II) in tailings and acid mine drainage.

Yanrong Dong, Ziqing Gao, Junzhen Di, Dong Wang, Zhenhua Yang, Xuying Guo, Xiaotong Zhu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
6.8field-weighted citation impact, top 2% 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

7 citing papers in PubMed, 27 citations in OpenAlex.

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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 at 1 institution in 1 country.

Yanrong DongCollege of Civil Engineering, Liaoning Technical University, Fuxin, China.
Ziqing GaoCollege of Civil Engineering, Liaoning Technical University, Fuxin, China.
Junzhen DiCollege of Civil Engineering, Liaoning Technical University, Fuxin, China.
Dong WangSchool of Mining, Liaoning Technical University, Fuxin, China.
Zhenhua YangSchool of Mining, Liaoning Technical University, Fuxin, China.
Xuying GuoCollege of Science, Liaoning Technical University, Fuxin, China.
Xiaotong ZhuCollege of Civil Engineering, Liaoning Technical University, Fuxin, China.
Liaoning Technical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In view of water and soil getting polluted by Pb(II), Zn(II), and other heavy metals in tailings and acid mine drainage (AMD), we explored the removal effect of sulfate-reducing bacteria (SRB) on Pb(II), Zn(II), and other pollutants in solution and tailings based on the microbial treatment technology. We used the scanning electron microscope-energy dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), and X-ray fluorescence (XRF), to reveal the mechanism of SRB treatment of tailings. The results showed that SRB had a strong removal capacity for Zn(II) at 0-40 mg/L; however, Zn(II) at 60-100 mg/L inhibited the growth of SRB. Similarly, SRB exhibited a very strong ability to remove Pb(II) from the solution. At a Pb(II) concentration of 10-50 mg/L, its removal percentage by SRB was 100%. SRB treatment could effectively immobilize the pollutants leached from the tailings. With an increase in the amount of tailings added to each layer, the ability of SRB to treat the pollutants diminished. When 1 cm of tailingssand was added to each layer, SRB had the best effect on tailing sand treatment. After treatment, the immobilization rates of

Indexed as

acid mineheavy metalssulfate-reducing bacteria (SRB)tailing sandstreatment

Identifiers

PMID38618484
PMCPMC11010684
OpenAlexW4393273841

What OpenQuestion holds

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LicenceCC BY
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.