Conventional hydrogen separations from reformed hydrocarbons often deploy a water gas shift (WGS) reactor to convert CO to CO₂, followed by adsorption processes to achieve pure hydrogen. The purified hydrogen is then fed to a compressor to deliver hydrogen at high pressures. Electrochemical hydrogen pumps (EHPs) featuring proton-selective polymer electrolyte membranes (PEMs) represent an alternative separation platform with fewer unit operations because they can simultaneously separate and compress hydrogen continuously. In this work, a high-temperature PEM (HT-PEM) EHP purified hydrogen to 99.3%, with greater than 85% hydrogen recovery for feed mixtures containing 25–40% CO. The ion-pair HT-PEM and phosphonic acid ionomer binder enabled the EHP to be operated in the temperature range from 160 to 220 °C. The ability to operate the EHP at an elevated temperature allowed the EHP to purify hydrogen from gas feeds with large CO contents at 1 A cm^–2. Finally, the EHP with the said materials displayed a small performance loss of 12 μV h^–1 for purifying hydrogen from syngas for 100 h at 200 °C.

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.