As glass used to confine the long-lived radionuclides arising from spent nuclear fuel are intended to be stored definitively in a deep geological repository, it is important to predict the glass dissolution rate when being in contact with underground water. Indeed, this material alteration rate is directly linked to the release of the radioelements into the environment, thus the safety of the disposal. For the development of a predictive model, the first step is to determine the preponderant elementary mechanisms originating the glass alteration.
More and more sophisticated stochastic approaches based on Monte Carlo algorithms have been developed in previous years to try to capture these elementary mechanisms and determine the rate limiting once depending on the glass composition and the alteration conditions.
This presentation starts with a history of these developments from the very first model proposed by M. Aertsens around 1995 until the one currently under development.
The first attempt to simulate nuclear glass alteration using a Monte Carlo approach is due by M. Aertsens from Mol in Belgium. An ordered SiO2-Na2O network in contact with water was built to simulate the glass alteration and a set of probabilities were introduced to represent the release of Si into the solution and the Na+-H+ exchange mechanisms. Unfortunately, this method was limited because of a too-long computational time.
Then a refined algorithm was developed at CEA Marcoule [1,2]. The glass compositions considered were more complex (up to five oxides SiO2-B2O3-Na2O-CaO-ZrO2) and the glass structure was still represented by an ordered network in contact with water. A larger set of probabilities was used to simulate the hydrolysis of glass formers (Si, Al). One Si or one Al ion in contact with water was released into the solution with a probability depending on the local degree of polymerization of its site. The B ions were immediately released in the solution when in contact with water. Using this more sophisticated algorithm, it has been possible to reproduce with good agreement some experimental results, such as the behavior of B, a glass dissolution tracer, for a series of SiO2-B2O3-Na2O glasses altered at different S/V ratios, or the strengthening effect of ZrO2.
Later, S. Kerisit at PNNL [3] continued to improve the Monte Carlo method first by adding Al2O3 to the glass composition, then by considering the boroxols rings effect. He developed his own code, before a new collaboration with CEA started. He observed a non-linear impact of Al2O3 on the glass alteration and an acceleration of the glass alteration with the number of boroxol rings.
CEA and PNNL codes were later compared successfully during A. Jan’s thesis [3]. One of the main objectives of this thesis was to use these Monte Carlo codes to try to reproduce the increase of glass alteration after irradiation of the material by heavy ions. But it has not been possible to trigger the Monte Carlo probabilities to reproduce the experimentally observed radiation effects. From this work, it has been concluded that other important elementary mechanisms were still missing.
For this reason, a new Monte Carlo algorithm is currently in development. The main difference with the previous CEA and PNNL codes is the possibility for water to diffuse inside the glass (this has never been considered so far) and the possibility to represent the alteration layer ripening. Thanks to these new options, it should be possible to represent more precisely both the radiation effects on glass alteration, along with the so called residual rate. The first results obtained with this refined Monte Carlo method will be presented for a series of SiO2-B2O3-Al2O3-Na2O glasses.

[1] C. Cailleteau, F. Angeli, F. Devreux, S. Gin, J. Jestin, P. Jollivet, O. Spalla, Nat. Mater. 7 (2008) 978-983.
[2] F. Devreux, A. Ledieu, P. Barboux, Y. Minet, J. Non-Cryst. Solids 343 (2004) 13-25.
[3] S. Kerisit, E.M. Pierce, Geochim. Cosmochim. Acta 75 (2011) 5296-5309.
[4] A. Jan, J.-M. Delaye, S. Gin, S. Kerisit, J. Non-Cryst. Solids 519 (2019) 6-13.