What question did this study set out to answer?

April 27, 2026Next Materials0 citationsOpen Access

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

NSNasir Widha SetyantoUniversity of Brawijaya SSSugiono SugionoUniversity of Brawijaya YIYudy Surya IrawanUniversity of Brawijaya

Key Points

To investigate how SnO₂ gum affects the pore structure of combusted bagasse leaves and validate pore network simulations.
Incorporated SnO₂ gum into combusted bagasse leaves to enhance porosity and reduce pore size.
Used cylinder-based and blob-based models for pore network simulations and comparison against real sample data.
Analyzed porosity and pore size changes using BET and BJH methods, and validated simulations with SSIM and NRMSE.
Pore structure improved with SnO₂ gum, increasing porosity from 45% to 72% and reducing average pore size from 50 nm to 20 nm.
Blob-based model outperformed cylinder model with SSIM of 0.621 vs. 0.264 and NRMSE of 0.268 vs. 0.548.
Histogram overlap exceeded 55% for both combustion cases, confirming higher accuracy of blob-based simulation.

Abstract

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. • Blob-based model outperformed cylinder model in pore network similarity. • Fast combustion showed higher structural match with experimental data. • Similarity indexes confirmed improved accuracy of blob-based simulation. • SnO₂ gum addition increased combustion rate and pore structure changes. • Histogram overlap exceeded 55% for both slow and fast combustion cases.

AIに質問

Bookmark

View Full Paper