Evidence map›Paper›PMID 26403610›Full record

ArticleEcotoxicology (London, England)2015

Spectroscopic characterization of the complexes between Fe/Mn and natural organic matters by electron paramagnetic resonance and synchrotron-based techniques.

Hong-Wei Luo, Zhi-Qi Lin, Guo-Ping Sheng

Abstract read
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In one paragraph

Article in Ecotoxicology (London, England), 2015. 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
–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

1 citing paper in PubMed.

  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

3 authors.

Hong-Wei LuoCAS Key Laboratory of Urban Pollutant Conversion, Department of Chemistry, University of Science and Technology of China, Hefei, 230026, China.
Zhi-Qi LinCAS Key Laboratory of Urban Pollutant Conversion, Department of Chemistry, University of Science and Technology of China, Hefei, 230026, China.
Guo-Ping ShengCAS Key Laboratory of Urban Pollutant Conversion, Department of Chemistry, University of Science and Technology of China, Hefei, 230026, China. gpsheng@ustc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The surface interaction between heavy metals and natural organic matters (NOM) substantially affects their migration and conversion in natural environments. In this study, the chemical speciation and element mapping of Fe and Mn in reduced NOM were investigated. The results show that quinone and semiquinone moieties dominated the redox properties in NOM, and the EPR signal intensity exhibited pH dependence with an increase of EPR signal intensity at a higher pH value. The EPR results indicate that the complexes displayed the characteristics of superparamagnetic oxides/oxyhydroxides after Fe/Mn complexed with NOM. μ-XRF results suggest that the scatterplots of Fe and Mn distributions at pH 11 had the most positive linearly-related plot points, indicating strong correlations for Mn-Fe binary metallic ions. μ-XANES results further interpret the presence of higher Mn oxidation state at pH 11, while Fe kept trivalent in all samples. These results reveal that the surface interactions are closely related to the redox state of NOM and are beneficial for better understanding the speciation, immobilization, transport, and toxicity of metal ions in natural waters.

Indexed as

Electron Spin Resonance SpectroscopyEnvironmental MonitoringHumic SubstancesIronManganeseSpectrometry, X-Ray EmissionWater Pollutants, ChemicalX-Ray Absorption SpectroscopyHumic SubstancesIronManganeseWater Pollutants, ChemicalElectron paramagnetic resonance (EPR)Heavy metalsNatural organic matters (NOM)X-ray absorption near edge structure (μ-XANES)X-ray fluorescence mapping (μ-XRF)

Identifiers

PMID26403610

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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.