Application of community data to surface complexation modeling framework development: Iron oxide protolysis

A Raman measurement scheme for accurate compositional analysis of dark-colored sample by minimizing laser-induced sample degradation: Determination of Fe3O4 concentration in sintered ores

An effective and rapid Raman measurement scheme to determine Fe3O4 concentration in sintered ores was explored. Because sintered ores are brownish-black materials that easily absorb laser photons, accurate quantitative analysis requires obtaining an Fe3O4 peak with a high signal-to-ratio by reducing the possibility of local sample heating and degradation. For this purpose, a wide area coverage (WAC) Raman scheme with a laser-illumination diameter of 1 mm was adopted to decrease the laser power per area (LP/A) on each sample. The sintered ore sample was also wetted with water to reduce the chance of further heating by the laser. The combination of the WAC scheme and water-wetting allowed to increase the laser power during sample measurement, and the subsequent intensity (as well as the signal-to-noise ratio) of the Fe3O4 peak was elevated compared with both that measured by a Raman microscope yielding a higher LP/A and without water-wetting of the sample. In the Raman spectra of 93 real sintered ore samples measured using the proposed scheme, the ratio of Fe3O4 and Fe2O3 peak areas correlated closely (R2 = 0.94) with Fe3O4 concentration determined by titration. The demonstrated scheme is practical when Raman spectroscopy is employed for compositional analysis of dark and highly photon-absorbing samples.

Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model

This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to utilize ML algorithms and establish the parameter model, to link factors influencing the adsorption amount of oxyanions with optimized surface complexation constants. However, the results revealed the optimized chromate or arsenate surface complexation constants might fall into local extrema, making it unable to establish a reasonable mapping relationship between adsorption conditions and surface complexation constants by ML algorithms. In contrast, species-informed models were successfully obtained, by incorporating the surface species information calculated from the unoptimized SCM with the adsorption condition as input features. Compared with the optimized SCM, the species-informed model could make more accurate predictions on pH edges, isotherms, and kinetic data for various input conditions (for chromate: root mean square error (RMSE) on test set = 5.90 %; for arsenate: RMSE on test set = 4.84 %). Furthermore, the utilization of the interpretable formula based on Local Interpretable Model-Agnostic Explanations (LIME) enabled the species-informed model to provide surface species information like SCM. The species-informed SCM-ML hybrid modeling method proposed in this study has great practicality and application potential, and is expected to become a new paradigm in surface adsorption model.

Using machine learning to explore oxyanion adsorption ability of goethite with different specific surface area

In this study, we developed prediction models for the adsorption of divalent and trivalent oxyanions on goethite based on machine learning algorithms. After verifying the reliability of the models, the importance of goethite specific surface area (SSA) and the average oxyanion adsorption capacities of goethite with different SSAs were calculated by shapley additive explanations (SHAP) importance analysis and partial dependence (PD) analysis. Despite there were differences in the feature importance of divalent and trivalent oxyanions, the contribution of goethite's SSA to the adsorption amount ranked the fourth based on SHAP importance, indicating SSA played the important role in oxyanion adsorption. Meanwhile, the PD values of SSA and the optimized complexation constants from surface complexation modeling (SCM) both indicated a non-monotonic relationship between the goethite with different SSA and its oxyanions binding capacity. When the total site concentration and crystal face composition were used as the machine learning model input features, the SHAP importance values of crystal faces and the PD decomposition results indicated that the (001) face showed the crucial influence on oxyanions adsorption amount. These findings demonstrated the important role of crystal face composition in goethite's adsorption ability, and provided a theoretical explanation for the variations of oxyanions adsorption amount on different SSA goethite.