A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/agronomy12020446 http://hdl.handle.net/11449/234141 |
Resumo: | Surface quality is key for any adsorbent to have an effective adsorption. Because analyzing an adsorbent can be costly, we established an imagery protocol to determine adsorption robustly yet simply. To validate our hypothesis of whether stereomicroscopy, superpixel segmentation and fractal theory consist of an exceptional merger for high-throughput predictive analytics, we developed carbon-capturing biointerfaces by pelletizing hydrochars of sugarcane bagasse, pinewood sawdust, peanut pod hull, wheat straw, and peaty compost. The apochromatic stereomicroscopy captured outstanding micrographs of biointerfaces. Hence, it enabled the segmenting algorithm to distinguish between rough and smooth microstructural stresses by chromatic similarity and topological proximity. The box-counting algorithm then adequately determined the fractal dimension of microcracks, merely as a result of processing segments of the image, without any computational unfeasibility. The larger the fractal pattern, the more loss of functional gas-binding sites, namely N and S, and thus the potential sorption significantly decreases from 10.85 to 7.20 mmol CO2 g−1 at sigmoid Gompertz function. Our insights into analyzing fractal carbon-capturing biointerfaces provide forward knowledge of particular relevance to progress in the field’s prominence in bringing high-throughput methods into implementation to study adsorption towards upgrading carbon capture and storage (CCS) and carbon capture and utilization (CCU). |
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A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing BiointerfacesAdsorbentBox-counting methodHigh-resolution stereomicroscopy imagery dataPhysical adsorptionPorous carbonaceous materialSimple linear iterative clustering algorithmSuperpixel segmentationSurface quality is key for any adsorbent to have an effective adsorption. Because analyzing an adsorbent can be costly, we established an imagery protocol to determine adsorption robustly yet simply. To validate our hypothesis of whether stereomicroscopy, superpixel segmentation and fractal theory consist of an exceptional merger for high-throughput predictive analytics, we developed carbon-capturing biointerfaces by pelletizing hydrochars of sugarcane bagasse, pinewood sawdust, peanut pod hull, wheat straw, and peaty compost. The apochromatic stereomicroscopy captured outstanding micrographs of biointerfaces. Hence, it enabled the segmenting algorithm to distinguish between rough and smooth microstructural stresses by chromatic similarity and topological proximity. The box-counting algorithm then adequately determined the fractal dimension of microcracks, merely as a result of processing segments of the image, without any computational unfeasibility. The larger the fractal pattern, the more loss of functional gas-binding sites, namely N and S, and thus the potential sorption significantly decreases from 10.85 to 7.20 mmol CO2 g−1 at sigmoid Gompertz function. Our insights into analyzing fractal carbon-capturing biointerfaces provide forward knowledge of particular relevance to progress in the field’s prominence in bringing high-throughput methods into implementation to study adsorption towards upgrading carbon capture and storage (CCS) and carbon capture and utilization (CCU).Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Graduate Program in Agronomy Plant Production Department of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)Graduate Program in Agronomy Plant Production Department of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)CAPES: 001Universidade Estadual Paulista (UNESP)Moreira, Bruno Rafael de Almeida [UNESP]de Brito Filho, Armando Lopes [UNESP]Júnior, Marcelo Rodrigues Barbosa [UNESP]da Silva, Rouverson Pereira [UNESP]2022-05-01T13:41:36Z2022-05-01T13:41:36Z2022-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/agronomy12020446Agronomy, v. 12, n. 2, 2022.2073-4395http://hdl.handle.net/11449/23414110.3390/agronomy120204462-s2.0-85124590693Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAgronomyinfo:eu-repo/semantics/openAccess2022-05-01T13:41:36Zoai:repositorio.unesp.br:11449/234141Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-05-01T13:41:36Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces |
title |
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces |
spellingShingle |
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces Moreira, Bruno Rafael de Almeida [UNESP] Adsorbent Box-counting method High-resolution stereomicroscopy imagery data Physical adsorption Porous carbonaceous material Simple linear iterative clustering algorithm Superpixel segmentation |
title_short |
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces |
title_full |
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces |
title_fullStr |
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces |
title_full_unstemmed |
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces |
title_sort |
A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces |
author |
Moreira, Bruno Rafael de Almeida [UNESP] |
author_facet |
Moreira, Bruno Rafael de Almeida [UNESP] de Brito Filho, Armando Lopes [UNESP] Júnior, Marcelo Rodrigues Barbosa [UNESP] da Silva, Rouverson Pereira [UNESP] |
author_role |
author |
author2 |
de Brito Filho, Armando Lopes [UNESP] Júnior, Marcelo Rodrigues Barbosa [UNESP] da Silva, Rouverson Pereira [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Moreira, Bruno Rafael de Almeida [UNESP] de Brito Filho, Armando Lopes [UNESP] Júnior, Marcelo Rodrigues Barbosa [UNESP] da Silva, Rouverson Pereira [UNESP] |
dc.subject.por.fl_str_mv |
Adsorbent Box-counting method High-resolution stereomicroscopy imagery data Physical adsorption Porous carbonaceous material Simple linear iterative clustering algorithm Superpixel segmentation |
topic |
Adsorbent Box-counting method High-resolution stereomicroscopy imagery data Physical adsorption Porous carbonaceous material Simple linear iterative clustering algorithm Superpixel segmentation |
description |
Surface quality is key for any adsorbent to have an effective adsorption. Because analyzing an adsorbent can be costly, we established an imagery protocol to determine adsorption robustly yet simply. To validate our hypothesis of whether stereomicroscopy, superpixel segmentation and fractal theory consist of an exceptional merger for high-throughput predictive analytics, we developed carbon-capturing biointerfaces by pelletizing hydrochars of sugarcane bagasse, pinewood sawdust, peanut pod hull, wheat straw, and peaty compost. The apochromatic stereomicroscopy captured outstanding micrographs of biointerfaces. Hence, it enabled the segmenting algorithm to distinguish between rough and smooth microstructural stresses by chromatic similarity and topological proximity. The box-counting algorithm then adequately determined the fractal dimension of microcracks, merely as a result of processing segments of the image, without any computational unfeasibility. The larger the fractal pattern, the more loss of functional gas-binding sites, namely N and S, and thus the potential sorption significantly decreases from 10.85 to 7.20 mmol CO2 g−1 at sigmoid Gompertz function. Our insights into analyzing fractal carbon-capturing biointerfaces provide forward knowledge of particular relevance to progress in the field’s prominence in bringing high-throughput methods into implementation to study adsorption towards upgrading carbon capture and storage (CCS) and carbon capture and utilization (CCU). |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-01T13:41:36Z 2022-05-01T13:41:36Z 2022-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/agronomy12020446 Agronomy, v. 12, n. 2, 2022. 2073-4395 http://hdl.handle.net/11449/234141 10.3390/agronomy12020446 2-s2.0-85124590693 |
url |
http://dx.doi.org/10.3390/agronomy12020446 http://hdl.handle.net/11449/234141 |
identifier_str_mv |
Agronomy, v. 12, n. 2, 2022. 2073-4395 10.3390/agronomy12020446 2-s2.0-85124590693 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Agronomy |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799964632171937792 |