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Research Article

Preparation and characterization of a new adsorbent from raphia taedigera seed

Emmanuel Folorunso Olasehinde1, Segun Michael Abegunde2

1Department of Chemistry, Federal University of Technology, Akure, Nigeria                                             2Department of Science Technology, Federal Polytechnic, Ado-Ekiti, Ekiti State, Nigeria  

Keywords

Abstract



Raphia taedigera;

adsorbent;

carbonization;

activation;

methylene blue;

SEM



There has been widespread research and engagement in recent years on the use of plant material in preparing porous carbon for wastewater treatment. In this study, new biochars (BCs) impregnated with 0.1 M sulphuric acid (RTA) and 0.1 M sodium hydroxide (RTB) were prepared. The BCs and raw seed [RTR] were characterized using scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectrophotometer, and thermogravimetric/Differential thermal analyser. [TGA/DTA) to determine their morphology, spectral bands and thermal behaviour respectively. RTR showed a scattered aggregated surface microstructure, while both RTA and RTB gave aggregated rough surfaces with high number of pores. The FTIR analysis showed significant changes in term of band shift, bands disappearances, and new band formations due to pyrolysis and activation of the BCs. The results of the TGA/DTA revealed that RTA and RTB have better thermal stability than RTR, with the maximum degradation temperature of RTR, RTA, and RTB occurred at 3800C, 4250C and 4100C respectively. Adsorption efficiencies ranged from 81.671 to 99.743% for performance evaluation on the BCs using different adsorbent dosages and the initial concentrations of methylene blue solutions. These preliminary investigations suggest that biochars synthesized from Raphia taedigera seed can be used for methylene blue removal from wastewater.

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LATEST News


27/12/2022 Reviewer Awards: The winners of 2022 reviewer awards of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Reviewer Awards section. 


23/12/2022 Best Paper Award: According to the Advisory Board decision, the paper authored by Nitin Kumar, Michele Barbato, Erika L. Rengifo-López and Fabio Matta entitled as “Capabilities and limitations of existing finite element simplified micro-modeling techniques for unreinforced masonry” is awarded the 2022 Best Paper Award of Research on Engineering Structures and Materials (RESM). 

23/12/2022 Most Cited Paper Award:  According to the Editorial Board evaluation, the paper authored by Aykut Elmas, Güliz Akyüz, Ayhan Bergal, Müberra Andaç and Ömer Andaç entitled as “Mathematical modelling of drug release" is awarded the 2022 Most Cited Paper Award of Research on Engineering Structures and Materials (RESM). 


13/04/2022 Fraudulent Emails Impersonating Our Journal: We noticed that some emails are sent to some people impersonating our journal staff as sender and requesting recipients to follow some links. Our journal and staff has nothing to do with these emails and please do not follow the given links. Senders seem to have malicious aims. The emails include a portion of some of our previous emails to the journal users and researchers. This is only to deceive the receiver and make them trust the email. Do not follow any links or perform suspicious actions specified in these emails. Please, check the sender info carefully. Even the sender address or name resembles the journal related words they are different, generally in an easily noticeable way.


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LATEST AWARDS


2022 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.


2022 Best Paper Award:

The paper authored by Nitin Kumar, Michele Barbato, Erika L. Rengifo-López and Fabio Matta entitled as “Capabilities and limitations of existing finite element simplified micro-modeling techniques for unreinforced masonry” is awarded the 


2022 Most Cited Paper Award:

The paper authored by Aykut Elmas, Güliz Akyüz, Ayhan Bergal, Müberra Andaç and Ömer Andaç entitled as “Mathematical modelling of drug release" is awarded the


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