Solubility of methane in aqueous solutions of triethylenediamine

Predicting methane solubility in water and seawater by machine learning algorithms: Application to methane transport modeling

The upward migration of methane from natural gas wells associated with fracking operations may lead to contamination of groundwater resources and surface leakage. Numerical simulations of methane transport in the subsurface environment require knowledge of methane solubility in the aqueous phase. This study employs machine learning (ML) algorithms to predict methane solubility in aquatic systems for temperatures ranging from 273.15 to 518.3 K and pressures ranging from 1 to 1570 bar. Four regression algorithms including regression tree (RT), boosted regression tree (BRT), least square support vector machine (LSSVM) and Gaussian process regression (GPR) were utilized for predicting methane solubility in pure water and mixed aquatic systems containing Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^- and SO₄^-2. The experimental data collected from the literature were used to implement the models. We used Grid search (GS), Random search (RS) and Bayesian optimization (BO) for tuning hyper-parameters of the ML models. Moreover, the predicted values of methane solubility were compared against Spivey et al. (2004) and Duan and Mao (2006) equations of state. The results show that the BRT-BO model is the most rigorous model for the prediction task. The coefficient of determination (R²) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 1.19 × 10^-7. The performance of the BRT-BO model is satisfactory, showing an acceptable agreement with experimental data. The comparison results demonstrated the superior performance of the BRT-BO model for predicting methane solubility in aquatic systems over a span of temperature, pressure and ionic strength that occurs in deep marine environments.

Selective CO Production by Photoelectrochemical Methane Oxidation on TiO2

The inertness of the C-H bond in CH₄ poses significant challenges to selective CH₄ oxidation, which often proceeds all the way to CO₂ once activated. Selective oxidation of CH₄ to high-value industrial chemicals such as CO or CH₃OH remains a challenge. Presently, the main methods to activate CH₄ oxidation include thermochemical, electrochemical, and photocatalytic reactions. Of them, photocatalytic reactions hold great promise for practical applications but have been poorly studied. Existing demonstrations of photocatalytic CH₄ oxidation exhibit limited control over the product selectivity, with CO₂ as the most common product. The yield of CO or other hydrocarbons is too low to be of any practical value. In this work, we show that highly selective production of CO by CH₄ oxidation can be achieved by a photoelectrochemical (PEC) approach. Under our experimental conditions, the highest yield for CO production was 81.9%. The substrate we used was TiO₂ grown by atomic layer deposition (ALD), which features high concentrations of Ti³⁺ species. The selectivity toward CO was found to be highly sensitive to the substrate types, with significantly lower yield on P25 or commercial anatase TiO₂ substrates. Moreover, our results revealed that the selectivity toward CO also depends on the applied potentials. Based on the experimental results, we proposed a reaction mechanism that involves synergistic effects by adjacent Ti sites on TiO₂. Spectroscopic characterization and computational studies provide critical evidence to support the mechanism. Furthermore, the synergistic effect was found to parallel heterogeneous CO₂ reduction mechanisms. Our results not only present a new route to selective CH₄ oxidation, but also highlight the importance of mechanistic understandings in advancing heterogeneous catalysis.