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of the Tc-species (Y1-map) and geochemical parameters such as pH, Eh, Tc(VII) initial, Tc loading, carbonate and contact time (Y2, ,7-map). Moreover, this supervised trained XY-SOM allows quantification of the coexisting Tc-species and the isolation of their EXAFS spectra from the spectral mixtures [6]. The architecture for a three fused XY-SOM is shown in Figure 103 as an example.

X-ray absorption near-edge structure (XANES) data confirmed the complete reduction of Tc(VII) to Tc(IV) by chukanovite under all experimental conditions. Consistent with mineral phases identified by X-ray diffraction, SOM analysis of the EXAFS spectra revealed the presence of three species in the sorption samples (Figure 104). Between pH 7.8 and 11.8, TcO2-dimers form inner-sphere sorption complexes both at the surface of the initial chukanovite as well as on the surface of magnetite formed due to redox reactions. At pH ≥11.9, Tc(IV) is incorporated in a mixed,

chukanovite-like, Fe/Tc hydroxy carbonate precipitate. These species also formed in coprecipitates.

Reoxidation experiments showed the instability of chukanovite under aerobic conditions. Instead of re- oxidising to soluble Tc(VII), however, Tc remained in its tetravalent oxidation state, and was firmly retained by environmentally more stable minerals magnetite and/or goethite, which formed due to the oxygen ingress. Tc(IV) incorporation into octahedral sites of these iron minerals prevents or impedes Tc reoxidation.

Generally, it can be concluded that the corrosion products chukanovite, magnetite and goethite contribute over a wide pH range (7.8 - 12.6) to the overall in-situ Tc retention capacity expected for subsurface nuclear waste repositories as well as for contaminated sites, thus mitigating its environmental mobility.

Fig. 104: Tc chukanovite reaction pathways: two sorption and one incorporation species determined by SOM as endmember components as a function of pH value.

PRINCIPAL PUBLICATION AND AUTHORS

Technetium immobilization by chukanovite and its oxidative transformation products: Neural network analysis of EXAFS spectra, K. Schmeide (a), A. Rossberg (a,b), F. Bok (a), S. Shams Aldin Azzam (a), S. Weiss (a), A.C. Scheinost (a,b), Sci. Total Environ. 770, 145334 (2021); https:/doi.org/10.1016/j.scitotenv.2021.145334 (a) Helmholtz-Zentrum Dresden Rossendorf (HZDR), Institute of Resource Ecology, Dresden (Germany) (b) ESRF

REFERENCES

[1] S. Bauters et al., Chem. Commun. 56, 9608-9611 (2020). [2] E. Yalcintas et al., Dalton Trans. 45, 17874-17885 (2016). [3] C.I. Pearce et al., Sci. Total Environ. 716, 132849 (2020). [4] T. Kohonen, Biol. Cybern. 43, 59-69 (1982). [5] W. Melssen et al., Chemometr. Intell. Lab. Systems 83, 99-113 (2006). [6] K. Domaschke et al., in ESANN Proceedings, Bruges, Belgium, 277-282 (2014).