Evidence map›Paper›PMID 35896703›Full record

ArticleScientific reports2022

Heavy metal removal using an advanced removal method to obtain recyclable paper incineration ash.

Hak-Min Kim, Tae-Yeol Choi, Min-Ju Park, Dae-Woon Jeong

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Hak-Min KimIndustrial Technology Research Center, Changwon National University, 20 Changwondaehak-ro, Changwon, Gyeongnam, 51140, Republic of Korea.
Tae-Yeol ChoiDepartment of Smart Environmental Energy Engineering, Changwon National University, 20 Changwondaehak-ro, Changwon, Gyeongnam, 51140, Republic of Korea.
Min-Ju ParkDepartment of Smart Environmental Energy Engineering, Changwon National University, 20 Changwondaehak-ro, Changwon, Gyeongnam, 51140, Republic of Korea.
Dae-Woon JeongDepartment of Smart Environmental Energy Engineering, Changwon National University, 20 Changwondaehak-ro, Changwon, Gyeongnam, 51140, Republic of Korea. dwjeong@changwon.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Various agents, including ethylenediaminetetraacetic acid, oxalic acid, citric acid, and HCl, were applied to remove heavy metals from raw paper incineration ash and render the ash recyclable. Among these prepared agent solutions, ethylenediaminetetraacetic acid showed the highest efficiency for Pb removal, while oxalic acid showed the highest efficiencies for Cu, Cd, and As removal. Additionally, three modes of an advanced removal method, which involved the use of both ethylenediaminetetraacetic acid and oxalic acid, were considered for use at the end of the rendering process. Among these three modes of the advanced removal method, that which involved the simultaneous use of ethylenediaminetetraacetic acid and oxalic acid, i.e., a mixture of both solutions, showed the best heavy metal removal efficiencies. In detail, 11.9% of Cd, 10% of Hg, 28.42% of As, 31.29% of Cu, and 49.19% of Pb were removed when this method was used. Furthermore, the application of these three modes of the advanced removal method resulted in a decrease in the amounts of heavy metals eluted and brought about an increase in the CaO content of the treated incineration ash, while decreasing its Cl content. These combined results enhanced the solidification effect of the treated incineration ash. Thus, it was confirmed that the advanced removal method is a promising strategy by which recyclable paper incineration ash can be obtained.

Identifiers

PMID35896703
PMCPMC9329337

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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.