Glasses are nonequilibrium solids with properties highly dependent on their method of preparation. In vapor-deposited molecular glasses, structural organization could be readily tuned with deposition rate and substrate temperature. Here, we show that the atomic arrangement of strong network-forming GeO 2 glass is modified at medium range (<2 nm) through vapor deposition at elevated temperatures. Raman spectral signatures distinctively show that the population of six-membered GeO 4 rings increases at elevated substrate temperatures. Deposition near the glass transition temperature is more efficient than postgrowth annealing in modifying atomic structure at medium range. The enhanced medium-range organization correlates with reduction of the room temperature internal friction. Identifying the microscopic origin of room temperature internal friction in amorphous oxides is paramount to design the next-generation interference coatings for mirrors of the end test masses of gravitational wave interferometers, in which the room temperature internal friction is a main source of noise limiting their sensitivity.
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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.
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- Award ID(s):
- 1922167
- PAR ID:
- 10500649
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Materials Degradation
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2397-2106
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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