Modeling hydration kinetics of sustainable cementitious binders using an advanced nucleation and growth approach
Modeling hydration kinetics of sustainable cementitious binders using an advanced nucleation and growth approach
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使用先进的成核和生长方法模拟可持续水泥粘合剂的水化动力学
DOI：
10.1016/j.conbuildmat.2023.133327
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发表时间：
2023
影响因子：
7.4
通讯作者：
Kumar, Aditya
中科院分区：
文献类型：
--
作者：
Han, Taihao;Huang, Jie;Sant, Gaurav;Neithalath, Narayanan;Goel, Ashutosh;Kumar, Aditya
利用补充胶凝材料（SCMs）部分替代波特兰水泥（PC）作为粘结剂，减少碳足迹并保持优异的性能。尽管如此，预测[PC + SCM]粘合剂的水化动力学对于当前的分析模型来说是具有挑战性的，因为SCMs和PC中存在广泛的化学成分和分子结构差异。本研究建立了一个先进的相边界成核和生长（png）模型，以获得水化动力学的优先预测。， [PC + SCM]粘合剂的时间分辨放热释放曲线。先进的png模型集成了人工智能作为附加组件，使其能够准确模拟[PC + SCM]粘合剂的水化动力学。本研究利用了一个数据库，其中包括710种[PC + SCM]粘合剂的量热谱，包括各种常用的SCM以及商用和合成PC。结果表明，该模型能较好地预测[PC + SCM]的热演化曲线。
Supplementary cementitious materials (SCMs) are utilized to partially substitute Portland cement (PC) in binders, reducing carbon-footprint and maintaining excellent performance. Nonetheless, predicting the hydration kinetics of [PC + SCM] binders is challenging for current analytical models due to the extensive diversity of chemical compositions and molecular structures present in both SCMs and PC. This study develops an advanced phase boundary nucleation and growth (pBNG) model to yielda prioripredictions of hydration kinetics—i.e., time-resolved exothermic heat release profiles—of [PC + SCM] binders. The advanced pBNG model integrates artificial intelligence as an add-on, enabling it to accurately simulate hydration kinetics for [PC + SCM] binders. This study utilizes a database that includes calorimetry profiles of 710 [PC + SCM] binders, encompassing a diverse range of commonly-used SCMs as well as both commercial and synthetic PCs. The results show that the advanced pBNG model predicts the heat evolution profiles of [PC + SCM] in a high-fidelity manner.
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影响因子：
3.8
作者：
S. Nazar;Jian Yang;Ayaz Ahmad;Syed Farasat Ali Shah
通讯作者：
S. Nazar;Jian Yang;Ayaz Ahmad;Syed Farasat Ali Shah
影响因子：
4.1
作者：
Ley-Hernandez, Aida Margarita;Lapeyre, Jonathan;Feys, Dimitri
通讯作者：
Feys, Dimitri
影响因子：
4.6
作者：
Lapeyre J;Han T;Wiles B;Ma H;Huang J;Sant G;Kumar A
通讯作者：
Kumar A
影响因子：
8.4
作者：
I. Mehdipour;Aditya Kumar;K. Khayat
通讯作者：
I. Mehdipour;Aditya Kumar;K. Khayat
DOI：
10.3389/fmats.2021.796476
发表时间：
2022-01
期刊：
--
影响因子：
--
作者：
Taihao Han;Sai Akshay Ponduru;R. Cook;Jie Huang;G. Sant;Aditya Kumar
通讯作者：
Taihao Han;Sai Akshay Ponduru;R. Cook;Jie Huang;G. Sant;Aditya Kumar