Effect of SnO₂ gum addition on pore structure: Computational reconstruction of carbon materials from bagasse leaves

This study investigates the influence of SnO₂ gum on the pore structure of combusted bagasse leaves and evaluates the accuracy of pore network simulations using two geometric approaches: cylinder-based and blob-based models. The incorporation of SnO₂ gum significantly enhanced the material’s porosity from 45% to 72% and reduced the average pore size from 50 nm to 20 nm, as confirmed by Brunauer-Emmet-Teller (BET) and Barret-Joyner-Halenda (BJH) analyses. Simulated pore networks were validated against binary images derived from real samples using structural similarity index (SSIM), normalized root mean square error (NRMSE), and histogram overlap. Results indicate that the blob-based model outperforms the cylinder-based approach, achieving higher SSIM (0.621 vs. 0.264) and lower NRMSE (0.268 vs. 0.548), demonstrating closer resemblance to actual pore morphology. The visual and quantitative analyses affirm that SnO₂ gum promotes a finer, more interconnected porous structure, potentially improving material performance for adsorption and catalytic applications. This research highlights the critical role of accurate modeling in porous material design and provides a scalable route for enhancing biochar-based functional materials through structural tuning and simulation-guided optimization.