2025-07-17 ワシントン大学UW)
From seaweed to structural material: A seaweed called Ulva (righthand petri dish) is dried (center), powdered (left) and then mixed directly in with traditional cement (beaker). The darker cement cube (top center) contains 5% seaweed by weight.Mark Stone/University of Washington
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＜関連情報＞
- https://www.washington.edu/news/2025/07/17/seaweed-infused-cement-could-cut-concretes-carbon-footprint/
- https://www.cell.com/matter/fulltext/S2590-2385(25)00310-8
藻類バイオマターを組み込んだ持続可能なセメントの設計加速のための機械学習を用いた閉ループ最適化 Closed-loop optimization using machine learning for the accelerated design of sustainable cements incorporating algal biomatter
Meng-Yen Lin ∙ Kristen Severson ∙ Paul Grandgeorge ∙ Eleftheria Roumeli
Matter Published:July 8, 2025
DOI:https://doi.org/10.1016/j.matt.2025.102267
Progress and potential
The concrete industry is a large producer of global greenhouse gases, the majority of which are attributable to ordinary Portland cement. Our research demonstrates a sustainable alternative by incorporating whole macroalgae as a biomatter substitute in cement. Biomatter is a natural choice for meeting sustainability goals, but it introduces complexity in the material design. Using machine learning and a life-cycle assessment (LCA)-integrated design, we significantly accelerated the discovery of an algal cement formulation that reduces global warming potential by 21% while still meeting strength requirements. The long-term vision of this research is to serve as a foundation for the accelerated design of sustainable biomaterials. The progress presented here not only offers a tangible step toward lowering the carbon footprint of cement but also provides a proof point of the utility of a framework for materials design that integrates LCA with experimental testing and computational modeling.
Highlights
- ML-guided experimental framework enables closed-loop optimization of new materials
- New algae cement is optimized to improve GWP while keeping functional strength
- Amortized Gaussian process model with early stopping accelerates optimization process
- Model-informed knowledge provides understanding of modified cement hydration
Summary
The substantial embodied carbon of cement, coupled with the ever-increasing need for construction materials, motivates the need for more sustainable cementitious materials. An emerging strategy to mitigate CO2 emissions involves incorporating carbon-negative biomatter; however, this introduces new challenges due to complex hydration-strength relationships and the combinatorial design space. Here, using machine learning, we develop a closed-loop optimization strategy to accelerate green-cement design with minimal CO2 emissions while meeting compressive-strength criterion. Green cements incorporating algae are tested in real time to predict strength evolution, with early-stopping criteria applied to accelerate the optimization process. This approach, using only 28 days of experiment time, attains both the strength requirement and 93% of the achievable improvement in global warming potential (GWP), resulting in a cement that has a 21% reduction in GWP. We further validate model-informed relationships via analysis of hydration, demonstrating the potential for developing materials grounded in scientific understanding.
