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

DMREF：通过集成机器学习、模拟和实验将二氧化碳转化为 3D 打印混凝土

• 批准号: 1922167
• 负责人: Mathieu Bauchy
• 金额: $ 150万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922167&HistoricalAwards=false
• 关键词: DMREF; Turning; Carbon; Dioxide; into

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.

混凝土是迄今为止世界上制造最多的材料，面临着许多挑战，包括碳强度高、强度发展速度慢以及强度重量比差。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。 “设计材料以彻底改变和设计我们的未来”(DMREF) 奖支持基础研究，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认识有可能使设计和发现可 3D 打印成可定制形式的环保混凝土材料成为可能。通过在加工过程中吸收二氧化碳，可以大大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想象混凝土作为生产增值产品的资源，而不是浪费。通过无缝集成实验、模拟和机器学习，该合作项目将培养学生精通与材料科学与工程、资源管理和建筑行业相关的实验和建模方法。注重本科生培训将培养下一代工程师的环境意识。这项研究的目的是破译加速设计新型可 3D 打印波特兰石基胶结材所需的基础知识，该胶结材可吸收二氧化碳。为此，本研究的目标是：(i) 了解、控制和优化浓缩硅酸盐悬浮液的流变性，以实现可打印性；(ii) 改进环境温度下的硅酸盐碳酸化路线，以最大限度地吸收二氧化碳，从而加速碳酸化动力学；(iii) 发现具有高承载能力和最佳强度重量比的新型多材料 3D 打印元结构 比率。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，这些活动相互告知并相互推进。实验和计算方法之间的协同将为矿物吸附剂的界面反应过程提供新的线索。该项目还将推进我们对浓缩悬浮液流变学的理解，并阐明允许浓缩浆料可印刷的聚合物发现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，本研究将开发新方法来优化轻质、刚性和坚固的元结构的几何形状和空间分布。总体而言，通过结合二氧化碳矿化和 3D 打印的优势，这项工作将带来开创性的智力贡献，以加速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点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

How clay particulates affect flow cessation and the coiling stability of yield stress-matched cementing suspensions

粘土颗粒如何影响屈服应力匹配固井悬浮液的流动停止和卷绕稳定性

• DOI: 10.1039/c9sm02414j
• 发表时间: 2020
• 期刊: Soft Matter
• 影响因子: 3.4
• 作者: Mehdipour, Iman;Atahan, Hakan;Neithalath, Narayanan;Bauchy, Mathieu;Garboczi, Edward;Sant, Gaurav
• 通讯作者: Sant, Gaurav

Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning

• DOI: 10.1021/acsnano.1c05619
• 发表时间: 2021-11-23
• 期刊: ACS NANO
• 影响因子: 17.1
• 作者: Du, Tao;Liu, Han;Smedskjaer, Morten M.
• 通讯作者: Smedskjaer, Morten M.

Deconstructing water sorption isotherms in cement pastes by lattice density functional theory simulations

• DOI: 10.1111/jace.17829
• 发表时间: 2021-04
• 期刊: Journal of the American Ceramic Society
• 影响因子: 3.9
• 作者: Yao Zhang;Han Liu;Cheng Zhao;J. W. Ju;M. Bauchy

Machine Learning Enables Rapid Screening of Reactive Fly Ashes Based on Their Network Topology

机器学习能够根据网络拓扑快速筛选反应性飞灰

• DOI: 10.1021/acssuschemeng.0c06978
• 发表时间: 2021
• 期刊: ACS Sustainable Chemistry & Engineering
• 影响因子: 8.4
• 作者: Song, Yu;Yang, Kai;Chen, Jingyi;Wang, Kaixin;Sant, Gaurav;Bauchy, Mathieu
• 通讯作者: Bauchy, Mathieu

Interpreting the Strength Activity Index of Fly Ash with Machine Learning

用机器学习解释粉煤灰的强度活动指数

• DOI: 10.1520/acem20220024
• 发表时间: 2022
• 期刊: Advances in Civil Engineering Materials
• 影响因子: 1.4
• 作者: Song, Yu;Zhang, Steven;Wang, Kaixin;Jin, Chen;Sant, Gaurav;Bauchy, Mathieu
• 通讯作者: Bauchy, Mathieu