Evidence map›Paper›PMID 42252354›Full record

ArticleWorld journal of microbiology & biotechnology2026

Enhanced deep nitrogen removal from low C/N wastewater via manganese-catalyzed iron-carbon micro-electrolysis coupled biological system: performance, mechanism, and microbial adaptability.

Hengyuan Liu, Kai Wang, Feng Sha, Haibing Che, Rui Sun, Xiuhua Li

Abstract read
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In one paragraph

Article in World journal of microbiology & biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

6 authors.

Hengyuan LiuCollege of Chemistry and Life Sciences, Chifeng University, NO.1 Yingbin Road, Hongshan District, Chifeng, 024000, China. liuhy729@163.com.
Kai WangCollege of Chemistry and Life Sciences, Chifeng University, NO.1 Yingbin Road, Hongshan District, Chifeng, 024000, China.
Feng ShaCollege of Chemistry and Life Sciences, Chifeng University, NO.1 Yingbin Road, Hongshan District, Chifeng, 024000, China.
Haibing CheCollege of Chemistry and Life Sciences, Chifeng University, NO.1 Yingbin Road, Hongshan District, Chifeng, 024000, China.
Rui SunCollege of Chemistry and Life Sciences, Chifeng University, NO.1 Yingbin Road, Hongshan District, Chifeng, 024000, China.
Xiuhua LiCollege of Chemistry and Life Sciences, Chifeng University, NO.1 Yingbin Road, Hongshan District, Chifeng, 024000, China.

Funding

Chifeng Yulong Talent Project ylycjb2024-3Doctoral Research and Innovation Development Fund Project of Chifeng University BSJJ040Natural Science Foundation of Inner Mongolia Autonomous Region 2023MS02009
6 · The paper itself

Abstract

Efficient nitrate removal from low carbon-to-nitrogen (C/N) wastewater remains challenging due to the scarcity of electron donors in conventional biological denitrification. To address this limitation, a synergistic system coupling manganese-catalyzed iron-carbon micro-electrolysis (IMC-ME) with biological denitrification was developed. Under carbon-deficient conditions (C/N = 2), the coupled system achieved 90.1% nitrate removal and 77.2% total nitrogen (TN) removal, substantially outperforming the standalone biological process (49.4% TN removal). Mechanistically, manganese dioxide functioned as a cathodic catalyst, accelerating zero-valent iron corrosion and continuously generating Fe

Indexed as

CarbonElectrolysisIronManganeseNitrogenWastewaterBacteriaBiofilmsCatalysisDenitrificationManganese CompoundsNitratesOxidesThiobacillusWater PurificationCarbonIronManganeseManganese Compoundsmanganese dioxideNitratesNitrogenOxidesWastewaterIron-manganese-carbon micro-electrolysisLow C/N ratioMicrobial community structureNitrate removalSynergistic denitrification

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Registered trials

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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.