A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorMoreira, Bruno Rafael de Almeida-
Autor(es): dc.creatorde Brito Filho, Armando Lopes-
Autor(es): dc.creatorJúnior, Marcelo Rodrigues Barbosa-
Autor(es): dc.creatorda Silva, Rouverson Pereira-
Data de aceite: dc.date.accessioned2025-08-21T16:31:39Z-
Data de disponibilização: dc.date.available2025-08-21T16:31:39Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agronomy12020446-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/234141-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/234141-
Descrição: dc.descriptionSurface 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).-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionGraduate Program in Agronomy Plant Production Department of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)-
Descrição: dc.descriptionGraduate Program in Agronomy Plant Production Department of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)-
Descrição: dc.descriptionCAPES: 001-
Idioma: dc.languageen-
Relação: dc.relationAgronomy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAdsorbent-
Palavras-chave: dc.subjectBox-counting method-
Palavras-chave: dc.subjectHigh-resolution stereomicroscopy imagery data-
Palavras-chave: dc.subjectPhysical adsorption-
Palavras-chave: dc.subjectPorous carbonaceous material-
Palavras-chave: dc.subjectSimple linear iterative clustering algorithm-
Palavras-chave: dc.subjectSuperpixel segmentation-
Título: dc.titleA High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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