227
Views
1
CrossRef citations to date
0
Altmetric
Articles

Adsorption of Cu(II) ve Zn(II) ions by alginate-based composites: Full factorial design approach

, &
Pages 787-800 | Received 30 Oct 2021, Accepted 20 Dec 2021, Published online: 06 Jan 2022
 

Abstract

Groundnut biochar (GHB) encapsulated in calcium-alginate (CA) beads (GHB-CA) was synthesized as a novel composite adsorbent for the removal of copper (II) and zinc (II) from an aqueous solution. BET, SEM, EDS, FTIR, TGA were used to characterize the adsorbents. The data obtained were used to establish the adsorption isotherm using the models of two-, three- and four-parameter isotherms. To study the kinetics of adsorption of heavy metals adsorption on CA and GHB-CA, the pseudo-first-order model, the pseudo-second-order model, Esquivel, Avrami and Bangham were used. The adsorption performance showed that they could use low-cost and effective alginate-based composites for heavy metal removal. To obtain maximum adsorption, applied a full factorial design of three factors (metal ion type, adsorbent type and adsorbent dose) at two levels. It statistically optimized the influence of main variables on copper and lead removal using the synthesized novel adsorbent.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Funding

This work was financially supported by The Research Fund of the Ondokuz Mayıs University. Project Number: PYO.MUH.1904.18.006.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 906.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.