Dipartimento di Fisica e Geologia, Universitá di Perugia, I-06123, Perugia, Italy.
Department of Physics, Sapienza University of Rome, I-00185, Rome, Italy.
Nat Commun. 2018 Jul 6;9(1):2647. doi: 10.1038/s41467-018-04977-0.
Autocatalysis, i.e., the speeding up of a reaction through the very same molecule which is produced, is common in chemistry, biophysics, and material science. Rate-equation-based approaches are often used to model the time dependence of products, but the key physical mechanisms behind the reaction cannot be properly recognized. Here, we develop a patchy particle model inspired by a bicomponent reactive mixture and endowed with adjustable autocatalytic ability. Such a coarse-grained model captures all general features of an autocatalytic aggregation process that takes place under controlled and realistic conditions, including crowded environments. Simulation reveals that a full understanding of the kinetics involves an unexpected effect that eludes the chemistry of the reaction, and which is crucially related to the presence of an activation barrier. The resulting analytical description can be exported to real systems, as confirmed by experimental data on epoxy-amine polymerizations, solving a long-standing issue in their mechanistic description.
自动催化作用,即通过产生的相同分子来加速反应,在化学、生物物理学和材料科学中很常见。基于速率方程的方法通常用于模拟产物的时间依赖性,但无法正确识别反应背后的关键物理机制。在这里,我们开发了一种受双组分反应混合物启发的斑杂粒子模型,并赋予其可调节的自动催化能力。这种粗粒化模型捕捉到了在受控和现实条件下发生的自动催化聚集过程的所有一般特征,包括拥挤的环境。模拟表明,对动力学的全面理解涉及一种出乎意料的效应,这种效应逃避了反应的化学性质,并且与存在激活势垒密切相关。所得的分析描述可以导出到实际系统中,这一点通过对环氧树脂-胺聚合反应的实验数据得到了证实,解决了其机械描述中的一个长期存在的问题。