Enabling carbon dioxide mineralization and active set control in portlandite-based cementitious suspensions
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Dai, Xiaodi (ORCID:0000000262607786)↗
Engineering topics
Bauchy, Mathieu (ORCID:0000000346000631)
Publications and source records attributed to Bauchy, Mathieu (ORCID:0000000346000631).
Microstructurally resolved electrochemical evolution of mechanical- and irradiation-induced damage in nuclear alloys
Abstract There is a need for high-throughput, scale-relevant, and direct electrochemical analysis to understand the corrosion behavior and sensitivity of nuclear materials that are exposed to extreme (high pressure, temperature, and radiation exposure) environments. We demonstrate the multi-scale, multi-modal application of scanning electrochemical cell microscopy (SECCM) to electrochemically profile corrosion alterations in nuclear alloys in a microstructurally resolved manner. Particularly, we identify that both mechanically deformed and irradiated microstructures show reduced charge-transfer resistance that leads to accelerated oxidation. We highlight that the effects of mechanical deformation and irradiation are synergistic, and may in fact, superimpose each other, with implications including general-, galvanic-, and/or irradiation-activated stress-corrosion cracking. Taken together, we highlight the ability of non-destructive, electrochemical interrogations to ascertain how microstructural alterations result in changes in the corrosion tendency of a nuclear alloy: knowledge which has implications to rank, qualify and examine alloys for use in nuclear construction applications.
Chen, Xin↗
Unveiling the effect of composition on nuclear waste immobilization glasses’ durability by nonparametric machine learning
Abstract Ensuring the long-term chemical durability of glasses is critical for nuclear waste immobilization operations. Durable glasses usually undergo qualification for disposal based on their response to standardized tests such as the product consistency test or the vapor hydration test (VHT). The VHT uses elevated temperature and water vapor to accelerate glass alteration and the formation of secondary phases. Understanding the relationship between glass composition and VHT response is of fundamental and practical interest. However, this relationship is complex, non-linear, and sometimes fairly variable, posing challenges in identifying the distinct effect of individual oxides on VHT response. Here, we leverage a dataset comprising 654 Hanford low-activity waste (LAW) glasses across a wide compositional envelope and employ various machine learning techniques to explore this relationship. We find that Gaussian process regression (GPR), a nonparametric regression method, yields the highest predictive accuracy. By utilizing the trained model, we discern the influence of each oxide on the glasses’ VHT response. Moreover, we discuss the trade-off between underfitting and overfitting for extrapolating the material performance in the context of sparse and heterogeneous datasets.
36 MATERIALS SCIENCE↗