ArticleWater science and technology : a journal of the International Association on Water Pollution Research2022
Effective sorptive removal of five cationic dyes from aqueous solutions by using magnetic multi-walled carbon nanotubes.
Article in Water science and technology : a journal of the International Association on Water Pollution Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Preparation of Reusable Porous Carbon Nanofibers from Oxidized Coal Liquefaction Residue for Efficient Adsorption in Water Treatment.Materials (Basel, Switzerland) · 2023Article
- One-Step Pyrolysis Fabrication of Magnetic Bagasse Biochar Composites with Excellent Lead Adsorption Performance.ACS omega · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The excellent properties of micro-nano materials and structures have attracted extensive attention especially in wastewater treatment. Based on this, magnetic multi-walled carbon nanotubes (MMWCNTs) have been prepared and used for the sorptive removal of five typical cationic dyes from aqueous solutions in the present study. Effects of several operational and environmental factors were investigated carefully, including initial pH values, common ions, contact time and temperature. The kinetics processes were well fitted by the pseudo-second-order kinetic model. The maximum adsorption capacities on MMWCNTs at 298.15 K for malachite green oxalate, auramine O, neutral red, crystal violet and rhodamine B were 442.2, 295.2, 183.4, 165.3 and 143.0 mg g
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Registered trials
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.