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Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities
Among the most relevant themes of modernity, using renewable resources to produce biofuels attracts several countries’ attention, constituting a vital part of the global geopolitical chessboard since humanity’s energy needs will grow faster and faster. Fortunately, advances in personal computing ass...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921263/ https://www.ncbi.nlm.nih.gov/pubmed/36770184 http://dx.doi.org/10.3390/ma16031175 |
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author | Gomes Souza, Fernando Pal, Kaushik Ampah, Jeffrey Dankwa Dantas, Maria Clara Araújo, Aruzza Maranhão, Fabíola Domingues, Priscila |
author_facet | Gomes Souza, Fernando Pal, Kaushik Ampah, Jeffrey Dankwa Dantas, Maria Clara Araújo, Aruzza Maranhão, Fabíola Domingues, Priscila |
author_sort | Gomes Souza, Fernando |
collection | PubMed |
description | Among the most relevant themes of modernity, using renewable resources to produce biofuels attracts several countries’ attention, constituting a vital part of the global geopolitical chessboard since humanity’s energy needs will grow faster and faster. Fortunately, advances in personal computing associated with free and open-source software production facilitate this work of prospecting and understanding complex scenarios. Thus, for the development of this work, the keywords “biofuel” and “nanocatalyst” were delivered to the Scopus database, which returned 1071 scientific articles. The titles and abstracts of these papers were saved in Research Information Systems (RIS) format and submitted to automatic analysis via the Visualization of Similarities Method implemented in VOSviewer 1.6.18 software. Then, the data extracted from the VOSviewer were processed by software written in Python, which allowed the use of the network data generated by the Visualization of Similarities Method. Thus, it was possible to establish the relationships for the pair between the nodes of all clusters classified by Link Strength Between Items or Terms (LSBI) or by year. Indeed, other associations should arouse particular interest in the readers. However, here, the option was for a numerical criterion. However, all data are freely available, and stakeholders can infer other specific connections directly. Therefore, this innovative approach allowed inferring that the most recent pairs of terms associate the need to produce biofuels from microorganisms’ oils besides cerium oxide nanoparticles to improve the performance of fuel mixtures by reducing the emission of hydrocarbons (HC) and oxides of nitrogen (NOx). |
format | Online Article Text |
id | pubmed-9921263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99212632023-02-12 Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities Gomes Souza, Fernando Pal, Kaushik Ampah, Jeffrey Dankwa Dantas, Maria Clara Araújo, Aruzza Maranhão, Fabíola Domingues, Priscila Materials (Basel) Article Among the most relevant themes of modernity, using renewable resources to produce biofuels attracts several countries’ attention, constituting a vital part of the global geopolitical chessboard since humanity’s energy needs will grow faster and faster. Fortunately, advances in personal computing associated with free and open-source software production facilitate this work of prospecting and understanding complex scenarios. Thus, for the development of this work, the keywords “biofuel” and “nanocatalyst” were delivered to the Scopus database, which returned 1071 scientific articles. The titles and abstracts of these papers were saved in Research Information Systems (RIS) format and submitted to automatic analysis via the Visualization of Similarities Method implemented in VOSviewer 1.6.18 software. Then, the data extracted from the VOSviewer were processed by software written in Python, which allowed the use of the network data generated by the Visualization of Similarities Method. Thus, it was possible to establish the relationships for the pair between the nodes of all clusters classified by Link Strength Between Items or Terms (LSBI) or by year. Indeed, other associations should arouse particular interest in the readers. However, here, the option was for a numerical criterion. However, all data are freely available, and stakeholders can infer other specific connections directly. Therefore, this innovative approach allowed inferring that the most recent pairs of terms associate the need to produce biofuels from microorganisms’ oils besides cerium oxide nanoparticles to improve the performance of fuel mixtures by reducing the emission of hydrocarbons (HC) and oxides of nitrogen (NOx). MDPI 2023-01-30 /pmc/articles/PMC9921263/ /pubmed/36770184 http://dx.doi.org/10.3390/ma16031175 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gomes Souza, Fernando Pal, Kaushik Ampah, Jeffrey Dankwa Dantas, Maria Clara Araújo, Aruzza Maranhão, Fabíola Domingues, Priscila Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities |
title | Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities |
title_full | Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities |
title_fullStr | Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities |
title_full_unstemmed | Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities |
title_short | Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities |
title_sort | biofuels and nanocatalysts: python boosting visualization of similarities |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921263/ https://www.ncbi.nlm.nih.gov/pubmed/36770184 http://dx.doi.org/10.3390/ma16031175 |
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