Izadifar Mohammadreza, Ukrainczyk Neven, Schönfeld Klara, Koenders Eduardus
Institute of Construction and Building Materials, Technical University of Darmstadt Franziska-Braun-Str. 3 Darmstadt 64287 Germany
Nanoscale Adv. 2025 Jun 3;7(14):4325-4335. doi: 10.1039/d5na00103j. eCollection 2025 Jul 10.
This research utilizes computational chemistry to investigate the complex mechanisms driving the dissolution of thermally activated metakaolin (MK) clay, a key supplementary cementitious material (SCM) in the manufacturing of concrete and geopolymer-based materials, thereby contributing to a reduced carbon footprint. A thorough exploration of the dissolution process is fundamental for fully understanding its pozzolanic reactivity. Expanding on our recent investigations into SiO dissolution in MK, this work addresses critical data gaps in understanding the dissolution behavior of aluminate species. The findings complement essential input for microscopic forward dissolution rate computations using the atomistic kinetic Monte Carlo (kMC) upscaling approach. To this end, the study calculates the atomistic activation energy (Δ ) of aluminate species at the transition state for the hydrolysis reaction using machine learning force fields (MLFF) based on density functional theory (DFT) and the improved dimer method (IDM) under far-from-equilibrium conditions, focusing on three activators: NaOH, KOH, and water. The analysis explores both the presence and absence of van der Waals (vdW) interactions, along with varying geometric configurations of hydration shells surrounding cations (Na, K) and the hydroxide anion (OH). The findings indicate that KOH generally exhibits lower Δ than NaOH, especially when vdW interactions are considered. Moreover, the findings emphasize that reduced hydration shells around KOH and NaOH lead to lower Δ for the dissolution of aluminate species.
本研究利用计算化学方法，探究驱动热活化偏高岭土（MK）黏土溶解的复杂机制。MK黏土是混凝土和地质聚合物基材料制造中的一种关键辅助胶凝材料（SCM），有助于减少碳足迹。对溶解过程进行全面探索，对于充分理解其火山灰反应活性至关重要。在我们最近对MK中SiO溶解的研究基础上，本工作解决了理解铝酸盐物种溶解行为方面的关键数据空白。这些发现为使用原子动力学蒙特卡罗（kMC）放大方法进行微观正向溶解速率计算提供了重要输入。为此，本研究在远离平衡条件下，使用基于密度泛函理论（DFT）的机器学习力场（MLFF）和改进的二聚体方法（IDM），计算了铝酸盐物种在水解反应过渡态的原子活化能（Δ ），重点关注三种活化剂：NaOH、KOH和水。分析探讨了范德华（vdW）相互作用的有无，以及围绕阳离子（Na、K）和氢氧根阴离子（OH）的水化壳层的不同几何构型。研究结果表明，KOH的Δ 通常低于NaOH，尤其是考虑vdW相互作用时。此外，研究结果强调，KOH和NaOH周围水化壳层的减少导致铝酸盐物种溶解的Δ 降低。
