Engineered waste-derived mesoporous green n-SiO2 for low-carbon cementitious materials: Box Behnken Design modelling and mechanisms
廃棄物由来のメソポーラスグリーンn-SiO2を用いた低炭素セメント材料: Box Behnken Designモデリングとメカニズム (AI 翻訳)
Safiki Ainomugisha, Moses J. Matovu, Farid Abed, Hussein M. Hamada, Zaid A. Al-Sadoon, Musa Manga
🤖 gxceed AI 要約
日本語
廃棄物由来の高多孔質ナノシリカ（n-SiO2）の比表面積（SSA）と水結合材比が低炭素セメントの性能に与える影響をBox-Behnkenデザインで最適化。中程度のSSA（579 m²/g）を3%添加した場合、28日強度が41%向上。高SSA（758 m²/g）では水和速度が向上し、誘導時間が85.5%短縮。メカニズムとして核生成とポゾラン反応を解明。
English
This study uses Box-Behnken Design to optimize low-carbon cement performance by engineering the specific surface area (SSA) of waste-derived nano-silica. 3% addition of medium SSA (579 m²/g) improved 28-day strength by 41%. High SSA (758 m²/g) reduced induction time by 85.5% and increased cumulative heat release by 360%. The enhancement is attributed to nucleation and pozzolanic reactions, refining pore structure.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の建設業界はカーボンニュートラルに向けセメントのCO2削減が急務であり、廃棄物由来ナノシリカの利用は循環型社会にも貢献する。本研究成果は、国内の低炭素コンクリート開発やサプライチェーン排出削減に示唆を与える。
In the global GX context
Cement production accounts for ~8% of global CO2 emissions. This paper demonstrates how waste-derived nano-silica can enhance cement performance while reducing clinker content, supporting global decarbonization of construction materials. The mechanistic insights are valuable for developing sustainable supplementary cementitious materials (SCMs).
👥 読者別の含意
🔬研究者:Provides a systematic optimization method (BBD) and mechanistic understanding of how SSA and w/b ratio interact in nano-silica-modified cement.
🏢実務担当者:Offers practical guidance on selecting nano-silica SSA and dosage for maximizing concrete strength and reducing cement use.
🏛政策担当者:Supports policies promoting waste valorization and low-carbon construction materials with empirical evidence.
📄 Abstract（原文）
Highly porous, eco-friendly nano-silica is essential for sustainable applications. Biomass-derived nanosilica ( n -SiO 2 ) provides a sustainable route to enhance cement-based materials (CBMs); however, the role of its intrinsic properties, particularly specific surface area (SSA), remains inadequately understood. This study systematically and uniquely explores how engineered SSA in highly porous n -SiO 2 interacts with varying water-to-binder systems to govern low-carbon cementitious performance. Using a Box–Behnken Design (BBD), the cement strength was optimized. The impact of these influencing factors on hydration kinetics, microstructural properties, and the mechanisms underlying the enhanced properties has been explained. The study revealed that lower and medium SSA n -SiO 2 (263 and 579 m²/g) significantly enhanced cement strength at low w/b ratios, whereas high SSA performed better in high w/b systems. The 3% n -SiO 2 mixture at 579 m²/g, with a w/b ratio of 0.5, showed the greatest strength increase of 41% at 28 days. Optimum conditions were SSA ≈ 264.7 m²/g, n -SiO 2 content ≈ 2.98%, and a w/b ratio of 0.5 for attaining maximum strength across all curing ages (statistically significant p < 0.0001). Meanwhile, BET pore structure analysis revealed that gel pores (< 10 nm) dominated in the n -SiO 2 -modified sample mixes. Use of engineered, highly porous silica with SSA of 758 m²/g showed better hydration kinetics, with an 85.5% reduction in induction time and a 360% increase in cumulative heat release at 24 h. The enhancement mechanism involved nucleation seeding and pozzolanic reactions, resulting in a refined pore structure and an enhanced interfacial transition zone. This study provides critical insights for optimizing biomass-derived, engineered high-SSA n -SiO 2 to achieve superior performance in cementitious materials, thereby supporting its broader adoption as a sustainable and environmentally friendly SCM.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s43939-026-00770-9first seen 2026-06-23 05:28:16
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。