Evidence map›Paper›PMID 35241732›Full record

ArticleScientific reports2022

Waste tea residue adsorption coupled with electrocoagulation for improvement of copper and nickel ions removal from simulated wastewater.

Nizeyimana Jean Claude, Lin Shanshan, Junaid Khan, Wu Yifeng, Han Dongxu, Liu Xiangru

Open access · goldAbstract 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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.2field-weighted citation impact, top 14% 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

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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

6 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

6 authors at 1 institution in 1 country.

Nizeyimana Jean ClaudeSchool of Environment Northeast, Normal University, Changchun, 130117, China.
Lin ShanshanSchool of Environment Northeast, Normal University, Changchun, 130117, China. linss071@nenu.edu.cn.
Junaid KhanSchool of Environment Northeast, Normal University, Changchun, 130117, China.
Wu YifengSchool of Environment Northeast, Normal University, Changchun, 130117, China.
Han DongxuSchool of Environment Northeast, Normal University, Changchun, 130117, China.
Liu XiangruSchool of Environment Northeast, Normal University, Changchun, 130117, China.
Northeast Normal University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present research involves removing copper and nickel ions from synthesized wastewater by using a simple, cheap, cost-effective, and sustainable activated green waste tea residue (AGWTR) adsorption coupled with electrocoagulation (ADS/EC) process in the presence of iron electrodes. By considering previous studies, their adsorbents used for treating their wastewaters firstly activate them by applying either chemicals or activating agents. However, our adsorbent was prepared without applying neither chemicals nor any activating agents. The operating parameters such as pH, hydraulic retention time, adsorbent dose, initial concentration, current density, and operating cost for both metals were optimized. In ADS/EC, the removal efficiency was obtained as 100% for copper and 99.99% for nickel ions. After the ADS/EC process, Fourier transform infrared (FT-IR) spectroscopy, Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray spectroscopy (EDS) analysis were used to characterize the adsorbent green waste tea residue. The adsorption isotherm and kinetic model results showed that the Langmuir and the pseudo-second-order were well-fitted to the experimental adsorption data better than the Freundlich and pseudo-first-order models for both Cu

Indexed as

WastewaterWater Pollutants, ChemicalAdsorptionCopperElectrocoagulationHydrogen-Ion ConcentrationIonsKineticsNickelSpectroscopy, Fourier Transform InfraredTeaCopperIonsNickelTeaWastewaterWater Pollutants, Chemical

Identifiers

PMID35241732
PMCPMC8894501
OpenAlexW4220896556

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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.