DMREF: Turning Carbon Dioxide into 3D-Printed Concrete via Integrated Machine Learning, Simulations, and Experiments

混凝土是迄今为止世界上制造最多的材料，它面临着许多挑战，包括大量的具体化碳强度、缓慢的强度发展速度和较低的强度与重量比。为了克服这些挑战，人们正在研究能够吸收二氧化碳的替代水泥。这项旨在革新和设计我们未来的设计材料奖(DMREF)支持基础研究，通过综合计算和实验方法加速这些替代材料的合成和设计。对控制加工-结构-性能关系的机制的新知识有可能使设计和发现环境友好型混凝土材料成为可能，这些材料可以被3D打印成可定制的形式。通过允许在加工过程中吸收二氧化碳，可以极大地减少与传统混凝土生产相关的碳足迹。这项研究有可能重新设计和重新想象混凝土，将其作为生产增值产品的资源，而不是作为废物。通过无缝集成实验、模拟和机器学习，这个合作项目将培养学生精通与材料科学和工程、资源管理和建筑行业相关的实验和建模方法。注重对本科生的培训将激发下一代工程师的环境良知。这项研究的目标是破译所需的基础知识，以加速设计一种新的3D可打印的硅酸盐水泥粘结剂，允许吸收二氧化碳。为此，本研究的目标是：(I)了解、控制和优化浓缩紫铁矿悬浮液的流变学，以实现印刷性能；(Ii)改进常温下的紫铁矿碳化路线，最大限度地吸收二氧化碳，以加速碳化动力学；以及(Iii)发现具有高承载能力和最佳强度与重量比的新的多材料3D打印元结构。这项研究依赖于模拟(即从电子到连续体)、实验和机器学习活动的迭代闭环集成，这些活动相互作用，相互促进。实验方法和计算方法之间的协同作用将为研究矿物吸附剂的界面反应过程提供新的线索。该项目还将促进我们对浓缩悬浮液流变学的最新了解，并阐明发现允许浓缩浆料印刷的聚合物背后的分子设计原理。最后，通过开创基于机器学习的多材料3D打印，本研究将开发新的方法来优化轻、硬和强的元结构的几何和空间分布。总体而言，通过将二氧化碳矿化和3D打印的优势结合在一起，这项工作将带来开创性的智力贡献，以加快具有理想性能和低碳影响的变革性建筑材料的设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Concrete, which is by far the most manufactured material in a world, faces many challenges, including a substantial embodied carbon intensity, slow strength-development rate, and poor strength-to-weight ratio. To overcome these challenges, alternative cements are being investigated that allow the uptake of carbon dioxide. This Designing Materials to Revolutionize and Engineer our Future (DMREF) award supports fundamental research to accelerate the synthesis and design of these alternative materials through an integrated computational and experimental approach. New knowledge of the mechanisms that control processing-structure-property relationships has the potential to enable the design and discovery of environmentally-friendly concrete materials that can be 3D printed into customizable forms. By allowing take of carbon dioxide in the processing, the carbon footprint associated with traditional concrete production can be greatly reduced. This research has the potential to redesign and reimagine concrete as a resource for the production of value-added products rather than as a waste. By seamlessly integrating experiments, simulations, and machine learning, this collaborative project will train students to be well-versed in both experimental and modeling approaches of relevance to materials science and engineering, resource management, and construction industries. A focus on the training of undergraduate students will engage the environmental conscience of the next generation of engineers.This objective of this research is to decipher the fundamental knowledge required to accelerate the design of a new 3D-printable portlandite-based cementitious binder that permits CO2 uptake. Toward this end, this research aims: (i) to understand, control, and optimize the rheology of concentrated portlandite suspensions to enable printability, (ii) to refine portlandite carbonation routes at ambient temperature to maximize CO2 uptake to accelerate the carbonation kinetics, and (iii) to discover new multi-material 3D-printed metastructures with high load-bearing capability and optimal strength-to-weight ratio. This research relies on an iterative closed-loop integration of simulation (i.e., from electrons to continua), experimental, and machine learning activities that mutually inform and advance each other. The synergy between experimental and computational approaches will shed new light on interfacial reaction processes of mineral sorbents. This project will also advance the state of the art in our understanding of the rheology of concentrated suspensions, and elucidate the molecular design principles behind the discovery of polymers that permit the printability of concentrated slurries. Finally, by pioneering machine-learning-informed multi-material 3D-printing, this research will develop new methods to optimize the geometry and spatial distribution of metastructures that are light, stiff, and strong. Overall, by marrying the benefits of CO2 mineralization and 3D-printing, this work will result in pioneering intellectual contributions to accelerate the design of transformative construction materials with desirable properties and low carbon impact.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.