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Dataset for natural organic matter treatment by tailored biochars
The dataset presented here are collected for tailoring biochars from pinecone biomass through chemical modification for the adsorption of natural organic matter (NOM) from lake water. The data includes schematics, figures and tables. The characterization of biomass and tailored biochars by Brunauer,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706767/ https://www.ncbi.nlm.nih.gov/pubmed/31463348 http://dx.doi.org/10.1016/j.dib.2019.104353 |
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author | Yazdani, Maryam Roza Duimovich, Nicola Tiraferri, Alberto Laurell, Panu Borghei, Maryam Zimmerman, Julie B. Vahala, Riku |
author_facet | Yazdani, Maryam Roza Duimovich, Nicola Tiraferri, Alberto Laurell, Panu Borghei, Maryam Zimmerman, Julie B. Vahala, Riku |
author_sort | Yazdani, Maryam Roza |
collection | PubMed |
description | The dataset presented here are collected for tailoring biochars from pinecone biomass through chemical modification for the adsorption of natural organic matter (NOM) from lake water. The data includes schematics, figures and tables. The characterization of biomass and tailored biochars by Brunauer, Emmett and Teller surface area measurement (BET), thermogravimetric analysis (TGA), energy dispersive X-ray (EDX) along with the adsorption of NOM from lake water by the tailored bichars and the desorption using alkaline solution are provided. This is complimentary dataset for the experimental set-up and data gathered related to the article [1] on biochar fabrication and lake water treatment. See this article [1] for further information and discussion. |
format | Online Article Text |
id | pubmed-6706767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-67067672019-08-28 Dataset for natural organic matter treatment by tailored biochars Yazdani, Maryam Roza Duimovich, Nicola Tiraferri, Alberto Laurell, Panu Borghei, Maryam Zimmerman, Julie B. Vahala, Riku Data Brief Environmental Science The dataset presented here are collected for tailoring biochars from pinecone biomass through chemical modification for the adsorption of natural organic matter (NOM) from lake water. The data includes schematics, figures and tables. The characterization of biomass and tailored biochars by Brunauer, Emmett and Teller surface area measurement (BET), thermogravimetric analysis (TGA), energy dispersive X-ray (EDX) along with the adsorption of NOM from lake water by the tailored bichars and the desorption using alkaline solution are provided. This is complimentary dataset for the experimental set-up and data gathered related to the article [1] on biochar fabrication and lake water treatment. See this article [1] for further information and discussion. Elsevier 2019-08-02 /pmc/articles/PMC6706767/ /pubmed/31463348 http://dx.doi.org/10.1016/j.dib.2019.104353 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Environmental Science Yazdani, Maryam Roza Duimovich, Nicola Tiraferri, Alberto Laurell, Panu Borghei, Maryam Zimmerman, Julie B. Vahala, Riku Dataset for natural organic matter treatment by tailored biochars |
title | Dataset for natural organic matter treatment by tailored biochars |
title_full | Dataset for natural organic matter treatment by tailored biochars |
title_fullStr | Dataset for natural organic matter treatment by tailored biochars |
title_full_unstemmed | Dataset for natural organic matter treatment by tailored biochars |
title_short | Dataset for natural organic matter treatment by tailored biochars |
title_sort | dataset for natural organic matter treatment by tailored biochars |
topic | Environmental Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706767/ https://www.ncbi.nlm.nih.gov/pubmed/31463348 http://dx.doi.org/10.1016/j.dib.2019.104353 |
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