A High-Throughput Imagery Protocol to Predict Functionality upon Fractality of Carbon-Capturing Biointerfaces
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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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Adsorbent, Box-counting method, High-resolution stereomicroscopy imagery data, Physical adsorption, Porous carbonaceous material, Simple linear iterative clustering algorithm, Superpixel segmentation
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Inglês
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Agronomy, v. 12, n. 2, 2022.
