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Mineral identification and quantification are key to the understanding and, hence, the capacity to predict material properties. The method of choice for mineral quantification is powder X-ray diffraction (XRD), generally using a Rietveld refinement approach. However, a successful Rietveld refinement requires preliminary identification of the phases that make up the sample. This is generally carried out manually, and this task becomes extremely long or virtually impossible in the case of very large datasets such as those from synchrotron X-ray diffraction computed tomography. To circumvent this issue, this article proposes a novel neural network (NN) method for automating phase identification and quantification. An XRD pattern calculation code was used to generate large datasets of synthetic data that are used to train the NN. This approach offers significant advantages, including the ability to construct databases with a substantial number of XRD patterns and the introduction of extensive variability into these patterns. To enhance the performance of the NN, a specifically designed loss function for proportion inference was employed during the training process, offering improved efficiency and stability compared with traditional functions. The NN, trained exclusively with synthetic data, proved its ability to identify and quantify mineral phases on synthetic and real XRD patterns. Trained NN errors were equal to 0.5% for phase quantification on the synthetic test set, and 6% on the experimental data, in a system containing four phases of contrasting crystal structures (calcite, gibbsite, dolomite and hematite). The proposed method is freely available on GitHub and allows for major advances since it can be applied to any dataset, regardless of the mineral phases present.

Abstract In the present study, the chemical composition, mineralogy, and mechanisms of alteration of a cementitious grout based on a CEM III/C with addition of smectite, hydrotalcite, and silica fume, are studied using a combination of chemical and physical methods. This material was designed in the context of geological repository of radioactive wastes, with a twofold aim: first, to fill the technical voids left by drilling operations at the interface between the geological formation and the disposal galleries. Second, to neutralize a potential acidic transient due to pyrite oxidation, and to create an environment that favors low corrosion rates of carbon steels. The grout is mainly composed of calcium silicate hydrates having a Ca/Si ratio of ~0.8, incorporating Al in the bridging site of the Si chains (C-A-S-H), and accounting for 29–36 wt.% of the sample. It also contains silica fume (38–48 wt.%), smectite with interlayer Na (11–17 wt.%), hydrotalcite with interlayer CO3 2− (3–4 wt.%), and lower amounts of portlandite, calcite, and possibly gibbsite and gypsum. Upon alteration by water in a flow-through reactor, the main modifications affecting the sample are calcite and gypsum dissolution, hence releasing aqueous Ca2+ that is adsorbed in smectite interlayer by replacing Na+, and stoichiometric C-A-S-H dissolution. The evolution of solution chemistry and of the solid phase composition are reproduced successfully using a thermokinetic model.

Co/Fe-based layered double hydroxides (LDHs) are among the most promising materials for electrochemical applications, particularly in the development of energy storage devices, such as electrochemical capacitors. They have also been demonstrated to function as energy conversion catalysts in photoelectrochemical applications for CO2 conversion into valuable chemicals. Understanding the formation mechanisms of such compounds is therefore of prime interest for further controlling the chemical composition, structure, morphology, and/or reactivity of synthesized materials. In this study, a combination of X-ray diffraction, vibrational and absorption spectroscopies, as well as physical and chemical analyses were used to provide deep insight into the coprecipitation formation mechanisms of Co/Fe-based LDHs under high supersaturation conditions. This procedure consists of adding an alkaline aqueous solution (2.80 M NaOH and 0.78 M Na2CO3) into a cationic solution (0.15 M CoII and 0.05 M FeIII) and varying the pH until the desired pH value is reached. Beginning at pH 2, pH increases induce precipitation of FeIII as ferrihydrite, which is the pristine reactional intermediate. From pH > 2, CoII sorption on ferrihydrite promotes a redox reaction between FeIII of ferrihydrite and the sorbed CoII. The crystallinity of the poorly crystalized ferrihydrite progressively decreases with increasing pH. The combination of such a phenomenon with the hydrolysis of both the sorbed CoIII and free CoII generates pristine hydroxylated FeII/CoIII LDHs at pH 7. Above pH 7, free CoII hydrolysis proceeds, which is responsible for the local dissolution of pristine LDHs and their reprecipitation and then 3D organization into CoII4FeII2CoIII2 LDHs. The progressive incorporation of CoII into the LDH structure is accountable for two phenomena: decreased coulombic attraction between the positive surface-charge sites and the interlayer anions and, concomitantly, the relative redox potential evolution of the redox species, such as when FeII is re-oxidized to FeIII, while CoIII is re-reduced to CoII, returning to a CoII6FeIII2 LDH. The nature of the interlamellar species (OH−, HCO3−, CO32− and NO3−) depends on their mobility and the speciation of anions in response to changing pH.