Novel graph machine based QSAR approach for the prediction of the adsorption enthalpies of alkanes on zeolites

Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks

The hydrophobicity of oils is a key parameter to design surfactant/oil/water (SOW) macro-, micro-, or nano-dispersed systems with the desired features. This essential physicochemical characteristic is quantitatively expressed by the equivalent alkane carbon number (EACN) whose experimental determination is tedious since it requires knowledge of the phase behavior of the SOW systems at different temperatures and for different surfactant concentrations. In this work, two mathematical models are proposed for the rapid prediction of the EACN of oils. They have been designed using artificial intelligence (machine-learning) methods, namely, neural networks (NN) and graph machines (GM). While the GM model is implemented from the SMILES codes of a 111-molecule training set of known EACN values, the NN model is fed with some σ-moment descriptors computed with the COSMOtherm software for the 111-molecule set. In a preliminary step, the leave-one-out algorithm is used to select, given the available data, the appropriate complexity of the two models. A comparison of the EACNs of liquids of a fresh set of 10 complex cosmetic and perfumery molecules shows that the two approaches provide comparable results in terms of accuracy and reliability. Finally, the NN and GM models are applied to nine series of homologous compounds, for which the GM model results are in better agreement with the experimental EACN trends than the NN model predictions. The results obtained by the GMs and by the NN based on σ-moments can be duplicated with the demonstration tool available for download as detailed in the Supporting Information.

High throughput screening of CO2 solubility in aqueous monoamine solutions

Post-combustion Carbon Capture and Storage technology (CCS) is viewed as an efficient solution to reduce CO(2) emissions of coal-fired power stations. In CCS, an aqueous amine solution is commonly used as a solvent to selectively capture CO(2) from the flue gas. However, this process generates additional costs, mostly from the reboiler heat duty required to release the carbon dioxide from the loaded solvent solution. In this work, we present thermodynamic results of CO(2) solubility in aqueous amine solutions from a 6-reactor High Throughput Screening (HTS) experimental device. This device is fully automated and designed to perform sequential injections of CO(2) within stirred-cell reactors containing the solvent solutions. The gas pressure within each reactor is monitored as a function of time, and the resulting transient pressure curves are transformed into CO(2) absorption isotherms. Solubility measurements are first performed on monoethanolamine, diethanolamine, and methyldiethanolamine aqueous solutions at T = 313.15 K. Experimental results are compared with existing data in the literature to validate the HTS device. In addition, a comprehensive thermodynamic model is used to represent CO(2) solubility variations in different classes of amine structures upon a wide range of thermodynamic conditions. This model is used to fit the experimental data and to calculate the cyclic capacity, which is a key parameter for CO(2) process design. Solubility measurements are then performed on a set of 50 monoamines and cyclic capacities are extracted using the thermodynamic model, to asses the potential of these molecules for CO(2) capture.